Multiproduct Firms, Export Product Scope, and Trade
Liberalization: The Role of Managerial E¢ ciency
Larry Qiuy
University of Hong Kong

Miaojie Yuz
Peking University

December 10, 2014

Abstract
This paper provides a theoretical and empirical analysis of the e¤ects of unilateral tari¤ cuts on …rms’
export product scope. The theoretical model explicitly incorporates cost of management, in addition to thecommonly used production cost. Firms are heterogeneous in terms of managerial e¢ ciency but homogenous
in terms of production productivity. The analysis predicts that the home country’s tari¤ cut reduces allhome …rms’ export product scope, whereas in response to the foreign country’s tari¤ cut, a home …rm’s
export product scope expands (shrinks) if the …rm’s management cost is low (high). These predictions aresupported by our empirical analysis based on data on Chinese …rms from 2000 to 2006.
JEL: F12, F13, F15
Keywords: Multiproduct …rm; Management cost; Managerial e¢ ciency; Export product scope; Tradeliberalization; China

We thank Cheng Chen, Sai Ding, Amber Li, Yi Lu, and Zhihong Yu for their very helpful comments and suggestions. We
bene…t from presentations at the 2013 Annual Conference of China Trade Research Group, the International Workshop on Tradeand Productivity at the University of Glasgow, 2013 Asia Paci…c Trade Seminars, Hong Kong Institute of Monetary Research,
Hong Kong University of Science and Technology, Midwest International Economics Meeting (Fall 2013), Kobe University and
Nihon University. Larry Qiu thanks Hong Kong Institute of Monetary Research for …nancial support and hosting his visit during
the last stage of this project. Miaojie Yu thanks Nihon University for hosting his visit during the last stage of this project. We
thank Mohan Zhou for his valuable research assistance. All errors in this manuscript are ours.
y Faculty of Business and Economics, the University of Hong Kong, Hong Kong, Tel: (852)28591043, Email: larryqiu@hku.hk.
z China Center for Economic Research (CCER), National School of Development, Peking University, Beijing 100871, China, Tel:(86)10-62753109, E-mail: mjyu@nsd.pku.edu.cn.

1Introduction

Multiproduct …rms contribute a large percent to production and international trade in the global economy. These…rms frequently adjust their product scope in response to changes in the economic environment and policies(Bernard et al., 2009). Product scope adjustments a¤ect a …rm’s and an industry’s average productivity(Bernard et al., 2010 and 2011). Recent literature on multiproduct …rms has increased our understandingof …rms’ adjustment in response to trade liberalization (see the most recent discussion by Qiu and Zhou,2013). However, our knowledge about this issue is far from complete. This paper addresses such an issue
by examining how …rms that di¤er in managerial e¢ ciency adjust their export product scope (the numberof products exported ), which is di¤erent from the total product scope (the number of products produced ), in
response to unilateral trade liberalization, namely, domestic tari¤ or foreign tari¤ cut.
Some discrepancies exist between theoretical and empirical literature on multiproduct …rms and trade. First,
most existing theoretical models analyze how multiproduct …rms adjust their product scope in response to globalization, which is represented by bilateral (or bilateral) trade liberalization. In reality, although multilateral
trade liberalization exists through multilateral trade negotiations under the framework of the GATT and bilateral trade liberalization exists under free trade agreements, unilateral trade liberalization, or asymmetricbilateral trade liberalization occurs. For example, since 1979 when China started to open its economy, China
has unilaterally reduced its average tari¤s from above 40 percent to approximately 15 percent prior to its accession to the WTO in 2001. This evidence indicates that our understanding would not be complete if we do notknow how …rms adjust their product scope in response to unilateral trade liberalization. Moreover, theoreticalanalysis on the e¤ects of unilateral trade liberalization o¤ers a better foundation for empirical investigation,
which explores the sensitivity of exporters’ extensive margins to each country’s tari¤ changes. On the one
hand, domestic trade liberalization exerts a negative impact (the competition e¤ect) on a …rm’s pro…t fromeach product. On the other hand, a foreign country’s trade liberalization provides a positive opportunity (themarket expansion e¤ect) to a …rm’s pro…t from each product. Although existing theoretical studies focus on thee¤ects of bilateral trade liberalization on …rms’adjustment in product scope, they all appear to suggest that thequalitative e¤ects of unilateral trade liberalization on all …rms with high or low productivity are similar. However, empirical studies (e.g., Dhingra, 2013) show that …rms respond di¤erently. In this paper, we argue that…rms di¤er not only in production productivity, but also in many other aspects, such as managerial e¢ ciency.
Firm heterogeneity in management cost could generate results di¤erent from …rm heterogeneity in production
cost.1
1 Nocke and Yeaple (2014) introduced two dimensions of …rm heterogeneity: organizational capital and organizational e¢ ciency.
These two types of capability result in a trade-o¤ between producing more products with lower productivity and producing lessproducts with higher productivity. Managerial e¢ ciency is very di¤erent from organizational capability.

1Our model, which focuses on …rm heterogeneity in managerial e¢ ciency, assumes that …rms are homogeneousin terms of production productivity. Speci…cally, domestic …rms are of two types, e¢ cient ones with low
management cost and ine¢ cient ones with high management cost. All …rms produce multiple products andemploy the same production structure for their products. Every …rm has a core competency and faces increasing
marginal costs of production for its other products. In addition, introducing each new product at the …rm level
incurs a …xed cost. A product will be produced if the …xed cost of product introduction is lower than the sumof the pro…t derived from the domestic market and that derived from the foreign market. The home country’stari¤ cut increases competition in the domestic market and thus reduces a domestic …rm’s pro…t from all its
products. Marginal products are no longer pro…table to be produced, and consequently, each domestic …rm
reduces its total product scope. If the total product scope of a …rm is reduced to a large extent, the products
available for exports are also reduced. Therefore, our model predicts that a drastic domestic tari¤ cut reduces
a …rm’s export product scope.
A …rm decides its optimal export product scope such that its marginal product of export (de…ned as theleast productive product exported) earns zero pro…t from the foreign market. This zero pro…t condition implies
that all …rms’marginal products of export have the same unit cost, which is the sum of production cost andmanagement costs. Thus, an e¢ cient …rm’s marginal product has a higher production cost than that of anine¢ cient. Foreign tari¤ cuts exert two e¤ects on each product exported by a domestic …rm. First, a reductionin the iceberg-type foreign tari¤ lowers the marginal costs of production of all the home country’s export productsby the same percentage. This phenomenon can be considered an individual shock (positive) because productswith di¤erent marginal costs of production face di¤erent cost reductions in the absolute term. As a result, ane¢ cient …rm’s marginal product receives a larger reduction (in the absolute term) than that of an ine¢ cient…rm. Second, all the home country’s export products lower their prices because of cost reduction and thus makes
the foreign market competition tougher. This can be considered as a common shock (negative) to all products.
Our analysis shows that under certain conditions, the positive individual shock outweighs the negative common
shock for the marginal products of e¢ cient …rms; however, the opposite is true for the marginal products ofine¢ cient …rms. Consequently, e¢ cient …rms expand their export product scope in response to foreign tari¤cuts, whereas ine¢ cient …rms reduce theirs.
An empirical analysis based on Chinese …rms’ export product scope data from 2000 to 2006 is conductedin this study. Since home tari¤ reductions of some products in an industry are not directly relevant to a …rmin the same industry if the …rm does not and will not produce those products, the pro-competitive e¤ects oftari¤ reduction would be over-estimated if home tari¤s were measured at the industry level (Yu, 2013). By
the same token, using industry-level measure of foreign tari¤s would also lead to a similar estimation bias. To
avoid such a bias, we construct and use …rm-speci…c measures of home tari¤s and foreign tari¤s in all our count2

data estimation. Our preliminary empirical analysis which does not consider …rm heterogeneity in manageriale¢ ciency …nds that …rms reduce their export product scope in response to both the home tari¤ reductions
and foreign tari¤ reductions. Such a …nding holds for both low-productivity and high-productivity …rms. By
contrast, our main empirical analysis which accounts for the role of managerial e¢ ciency …nds strongly support
for our theoretical prediction: in response to Chinese tari¤ cuts, Chinese …rms reduce their export productscope; in response to foreign tari¤ cuts, Chinese …rms expand their export product scope, whereas those withlow-managerial e¢ ciency reduce theirs. In this analysis, we need to separate the true production e¢ ciency fromthe usual measured TFP, we use a …rm’s overhead expenses to proxy for management costs, and we construct
two indicators, high overhead indicator and low overhead indicator, of managerial e¢ ciency for each …rm within
an industry. Our estimation results are very robust.
The present study di¤ers from existing ones in literature in many ways. As pointed out earlier, the present
study focuses on the e¤ect of unilateral trade liberalization as opposed to bilateral trade liberalization. More
importantly, this study introduces managerial e¢ ciency as a source of …rm heterogeneity and shows that thisheterogeneity, rather than heterogeneity in production productivity, di¤erentiates the responses of …rms toforeign countries’trade liberalization. The importance of managerial e¢ ciency and its di¤erence from production
productivity have been emphasized in management science literature. For example, Gort and Lee (2003) utilized
American industrial data and found that managerial e¢ ciency contributes substantially to the total factor
productivity (TFP) in American manufacturing sectors. They identi…ed three sources of managerial e¢ ciency,
namely, superior initial managerial endowments, the accumulation of managerial knowledge through learning,
and the impact of an e¤ective market for managerial resources internal to the …rm. These sources of manageriale¢ ciency are di¤erent from a …rm’s production productivity.
In the theoretical literature of multiproduct …rms, all studies assume …rm heterogeneity in production
productivity and most, with the exception of Nocke and Yeaple (2014) and Qiu and Zhou (2013), predictthat in response to bilateral trade liberalization, all …rms (less productive and more productive) reduce theirproduct scope (Arkolakis and Muendler, 2011; Baldwin and Gu, 2009; Bernard et al., 2011; Dhingra, 2013;
Eckel and Neary, 2010; Feenstra and Ma, 2008). Qiu and Zhou (2013) showed that with an increasing …xed
cost of product introduction, the marginal products of …rms acquire di¤erent productivities; thus, …rms may
adjust their product scope in response to trade liberalization in the opposite directions.2 The present study does
not rely on the assumption of increasing …xed cost of product introduction to show that …rms with di¤erent
managerial e¢ ciencies may still exhibit opposite responses to foreign tari¤ cuts even though they have the same
production productivity.
2 If a …rm is very productive, its marginal product is also productive. In this case, the positive e¤ect of market expansion
dominates over the negative e¤ect of increased competition; the …rm then expands its product scope. By contrast, if a …rm is notvery productive, its maginal product is also not productive. The negative e¤ect dominates, and the …rm reduces its product scope.

3

Not all existing studies are about the e¤ects of bilateral trade liberalization. The implications of unilateral
trade liberalization can be obtained from the study of Mayer et al. (2013). The main result of Mayer et al.’s(2013) study indicates that in tough markets, the production distribution of …rms is skewed towards their corecompetencies and …rms reduce their product scope. A foreign tari¤ cut suggests that the foreign market has
become less tough and thus more pro…table to exporting …rms. A direct implication of this result to our modelis as follows: extremely tough competition in the domestic market (because of domestic tari¤ cuts) induces all
domestic …rms to reduce the set of produced products, and minimal competition in the export market (becauseof foreign tari¤ cuts) induces all exporters to expand the set of exported products. This implication is con…rmed
in our model without …rm heterogeneity in managerial e¢ ciency. However, a tari¤ cut in the foreign market has
both cost (positive for exporters) and competition (negative) e¤ects. These e¤ects are di¤erent for …rms withdi¤erent managerial e¢ ciencies; thus …rms adjust their export product scope di¤erently.
Existing empirical studies on multiproduct …rms generally …nd that trade liberalization has signi…cant e¤ects
on …rms’product scope choice. Despite the fact that most theoretical studies focus on bilateral trade liberalization, many empirical studies highlight unilateral trade liberalization. Dhingra (2013) showed that from 2003 to2006 in Thailand, less export-oriented domestic …rms increased their product lines in response to a unilateraltari¤ cut, whereas more export-oriented domestic …rms reduced their product lines. Iacovone and Javorcik(2010) documented the phenomenon of product "churning" among Mexican …rms as a result of improved accessto foreign markets, that is, a substantial number of Mexican …rms discontinued several existing products andsimultaneously developed new products for export. Goldberg et al. (2010) showed that from 1989 to 2003 whenintensive trade and other reforms took place in India, Indian …rms added more product lines than what they
discontinued; the discontinuance was unrelated to tari¤ reduction. Empirical studies on bilateral or multilateral
trade liberalization include those of Baldwin and Gu (2009), Bernard et al. (2011), and Berthou and Fontagne(2011).3 Previous empirical …ndings are far from complete or conclusive. The empirical …ndings of the present
study contributes to this literature. Chinese exporters’data are utilized to examine the e¤ects of tari¤ cuts in
China and foreign countries on export product scope. The results show that managerial e¢ ciency is importantin determining the extent to which …rms adjust their export product scope.4
The present study is also related to a recent one by Caliendo and Rossi-Hansberg (2012) in that bothemphasize the importance of the other aspects of …rm heterogeneity, in addition to heterogenous productivity.
3 Baldwin and Gu (2009) found that tari¤ cuts between 1973 and 1997 induced scope contraction in small or non-exporting
Canadian …rms, but had no e¤ect on large or exporting …rms. Bernard et al. (2011) demonstrated that U.S. …rms exposed tonumerous tari¤ reductions under the Canada–U.S. Free Trade Agreement reduced the number of products they produced relative
to …rms exposed to only a few tari¤ reductions. Berthou and Fontagne (2011) found that after the eurozone was established in
1999, productive French …rms increased their export product scope in the eurozone destinations, whereas slightly productive French…rms reduced their export product scope.
4 Manova and Zhang (2013) also employed Chinese data to explore the behavior of multiproduct …rms. An important feature of
their study is the linkage between multiple products and multiple quality level. Eckel et al.’s (2011) study on Mexican …rms also
has the same feature.

4

As Caliendo and Ross-Hansberg (2012) pointed out, a …rm is merely a technology to produce goods at a given
marginal cost. In reality, a …rm has many facets aside from its production technology. Caliendo and RossiHansberg (2012) added organization structure to …rms; we introduce management dimension to …rms. Moreover,Caliendo and Rossi-Hansberg (2012) assumed that …rms are heterogeneous with respect to demand for theirproducts to show the importance of the other aspects of …rms; we assume that …rms are heterogenous in termsof their managerial e¢ ciency. These added features of …rms enrich our understanding of …rms’ responses toglobalization.5
The rest of this paper is organized as follows. In Section 2, Chinese data are utilized to conduct a preliminary
empirical analysis on Chinese …rms’response to trade liberalization without di¤erentiating them by manageriale¢ ciency. The theoretical model with …rm heterogeneity in managerial e¢ ciency is introduced in Section 3,
and an equilibrium analysis is conducted to derive results related to the e¤ects of trade liberalization on …rms’
export product scope. Chinese data are employed in Section 4 to test the main theoretical predictions. Section
5 provides the concluding remarks.

2

Preliminary Empirical Analysis

The …rst empirical analysis on Chinese …rms’export product scope adjustment in response to the reduction intrade costs is conducted in this section.

2.1

Estimation Framework and Measures

A …rm’s total product scope is de…ned in this study as the total number of products that the …rm produces
and sells to the markets (either domestic or foreign). Export product scope is de…ned as the total number of
products that the …rm sells to the foreign market. A …rm’s export product scope is mainly determined by the
pro…tability of a …rm’s products in the foreign market, which in turn is a¤ected by many factors, including
GDP, productivity, and trade costs. A …rm’s domestic market pro…tability a¤ects its total product scope, but
may or may not a¤ect its export product scope.
We focus on two explanatory variables to determine how changes in trade costs a¤ect a Chinese …rm’s export
product scope. The …rst variable is the home country’s import tari¤s (referred to as home tari¤ and denotedby HT hereafter), and the second one is the foreign countries’import tari¤s (referred to as foreign tari¤ anddenoted by FT hereafter). Accordingly, the following empirical equation is established.
eit =

0+

1 T F Pit +2 HTit +3 F Tit +it + it;(1)where eit is …rm i’s export product scope, T F Pit is …rm i’total factor productivity, HTit is the home (Chinese)
5 Nocke and Yeaple (2014) also introduced another dimension of …rm heterogeneity in addition to production productivity.

5

tari¤ level faced by …rm i, and F Tit is the foreign tari¤ level faced by …rm i (all in year t).

it is a vector ofcontrol variables, including …rm i’s export market size (foreign countries’GDP), ownership type (state-owned
enterprise, multinational …rm, or others), and trade mode (processing or ordinary trade).
Although a country has many tari¤ lines, tari¤s that are not relevant to a particular …rm may not havean impact on the …rm’s export product scope. Hence, we construct …rm-speci…c tari¤ to better evaluate thee¤ects of tari¤ changes on …rms’export product scope. For home tari¤s, suppose that a …rm produces a set of
products for the domestic market. The …rm’s pro…t will be a¤ected directly by all tari¤ lines in this product set.
A tari¤ line will have a signi…cant e¤ect if the …rm has a larger share of the corresponding product in its total
domestic sales. This condition suggests that a …rm-speci…c tari¤ should be the average of all relevant tari¤sweighted by the share of each product’s sales. However, data on product-level domestic sales are unavailable.
Thus, we adopt a less satisfactory approach by using the share of a …rm’s export to substitute the share of its
domestic sales, as in Yu (2013). Speci…cally, we introduce the following measure as …rm i’s home tari¤:!
kX
Xi;initial_yearkPHTit =t;k
k2Eit
k2Eit Xi;initial_year(2)kis …rm i’s exports of product k in thewhere Eit is the set of …rm i’s export products in year t, Xi;initial_year…rst year the …rm appears in the sample, andkt is the home country’s ad valorem tari¤ on product k in year

t. Inspired by Topalova and Khandelwal (2011) and Yu (2013), we …x exports for each product at the initialperiod to avoid possible reverse causality (endogeneity problem) in …rm’s export scope with respect to measured
home tari¤s. However, such a measure still faces some possible caveats which will be discussed later.
The construction of …rm-speci…c foreign tari¤s is more complicated than the construction of home tari¤sbecause …rms not only export multiple products, but also export them to multiple countries, with di¤erent
subsets of products for di¤erent countries. The following measure of F Tit is proposed in this study to capture
the relative importance of di¤erent tari¤s of di¤erent foreign countries.
"
!
k
kc
X
X Xi;initial_year
Xi;initial_year
P
F Tit =kk
Xi;initial_year
k2Eit Xi;initial_year c2C
k2E
it

it

where

kc
t

#

;

(3)

kc
kc
t is product k’s ad valorem tari¤ imposed by country c in year t, Xi;initial_year is the value of …rmki’s export of product k to country c in the …rst year the product appears in the sample, Xi;initial_year=kcPXi;initial_
yearcc2Cit Xik;initial_year , and Cit are the sets of countries where …rm i has exports in year t. The ratio X ki;initial_ year

represents the share of …rm i’s product k exported to country c in the …rst year the …rm appears in the sample;kc
P Xi;initial_
year
it captures the relative importance of kc
ina¤ecting…rm
i’s
productkexport.
Thus,
(
) kc
k
t
t
X
c2Cit

i;initial_ year

is the time-invariant weighted average of foreign tari¤s on product k for …rm i. Such a time-invariant weight
can avoid the a¤ord-mentioned endogeneity of weighted tari¤s.

6

We then address T F Pit . Although many methods can be employed to measure a …rm’s TFP, or productivity,
we adopt the Olley-Pakes (1996) approach to estimate each Chinese …rm’s TFP (referred to as TFP1). We
modify the standard Olley-Pakes approach to better re‡ect the reality in China. First, following Feenstra etal. (2013), we use de‡ated output and input prices at the …rm-product level to measure TFP. Second, we use
real capital depreciation to construct a …rm’s real investment (the perpetual inventory method).6 Third, we
consider the e¤ect of China’s WTO accession in 2001 and the processing behavior of …rms in TFP realization.
A detailed description of the augmented Olley-Pakes TFP measures is provided in Appendix C. We also employ
other measures of TFP to verify the robustness of the results.7

2.2

Data

Regression (1) and construction of HT, FT and TFP require extensive information. Thus, we employ threehighly disaggregate panel datasets: product-level tari¤ data of every country, …rm-level production data of
Chinese …rms, and …rm and product-level trade data of Chinese …rms. A brief description of these datasets isprovided below, and detailed discussions are provided in Appendix A.
Tari¤s. The WTO o¢ cial webpage shows the tari¤s of all WTO member countries/regions at HS six-digit
level.8 The database includes the following tari¤ data for each product category: number of ad valorem duties
and non ad valorem duties; average, minimum, and maximum ad valorem duties; and percentage of free duty
and bound duty. For analysis purpose, average ad valorem duty is considered the most suitable; hence, only
this item is included in our dataset.
Firm production data. China’s National Bureau of Statistics maintains a rich database derived from annual
surveys of large manufacturing enterprises in China. This database, called the Chinese Manufacturing Enterprises (CME) database, includes all state-owned enterprises (SOE) and large non-SOEs whose annual sales are
more than RMB …ve million (or, equivalently, $770,000). Approximately 162; 885 …rms are included in 2000
6 Firm-level data only provide the book value of each …rm’s capital stock.

However, the original value of each …rm’s capitalstock must be obtained for TFP estimation. To solve this problem, we assume At = Ao ts=o (1 + rs ), where At is the book valueof a …rm’s capital stock in year t, Ao is the original value of the …rm’s capital stock when it is purchased in year o, and rs is theestimated province-industry-level growth rate of nominal capital stock in year s obtained from Brandt et al. (2012). If At and rs
are known for each …rm, the …rm’s original nominal book value can be determined accordingly. Approximately 40% of observations
have missing investment data, but this is not a problem because our estimation results do not change qualitatively when othermeasures of TFP are employed as shown later.
7 The Levinsohn and Petrin (2003) approach is also a popular method to construct TFP. In this approach, materials (i.e.,intermediate inputs) are used as a proxy variable. Yu (2013) argues that this approach is appropriate for …rms that do not utilizea large amount of imported intermediate inputs, and so it is less appropriate for our study because Chinese …rms rely substantiallyon imported intermediate inputs whose prices are signi…cantly di¤erent from those of domestic intermediate inputs (Helpern et al.,2011). Nonetheless, our results do not change qualitatively when Levinsohn-Petrin (2003) TFP or System-GMM TFP is employed.
Estimates that employ such TFP measures are not reported to save space but are available upon request.
8 Data can be accessed at http://tari¤data.wto.org/ReportersAndProducts.aspx. The data from Trade Analysis and Information
System generally have missing values, particularly data on the tari¤s imposed by other countries on Chinese exports. The productdestination-year combinations that have missing tari¤s are thus eliminated.

7and 301; 961 in 2006.9 The CME database contains information on more than 100 …nancial variables obtained
from each …rm’s accounting statement. The database has obvious omissions and errors. Following Feenstra
et al. (2013), we clean the database as follows. We eliminate the observations (i.e., …rms) wherein some key…nancial variables (such as total assets, net value of …xed assets, sales, and gross value of industrial output) are
missing, or the number of employees is less than eight.10 According to the basic rules of the generally accepted
accounting principles, we also omit the observations wherein (i) liquid assets are larger than total assets, (ii)total …xed assets are larger than total assets, (iii) the net value of …xed assets is larger than the total assets,(iv) the …rm’s identi…cation number is missing, or (v) the …rm’s establishment time is invalid.
Export data. China’s General Administration of Customs maintains a highly disaggregate trade database
wherein each international trade transaction is recorded. The database contains a large variety of information
about each trading …rm, including each export product’s price, quantity, value, and destination. Productinformation is available at the HS eight-digit level. We use this database to calculate each Chinese …rm’s export
product scope and construct the weights for HT and F T for each …rm. Some …rms export products that belong
to more than one industry. Considering that our focus is on within-industry multiproduct analysis, we assign a…rm to an industry at HS 2-digit level, in which the …rm has the most number of export products.
Our study requires the merging of the Customs database and CME database. Matching the two is challenging
because they use completely di¤erent …rm-identi…cation systems. As in Yu (2013), by using the …rms’Chinese
names, zip codes, and telephone numbers, we are able to match 76,946 …rms, which account for more than 40%of the manufacturing …rms reported in the CME database and approximately 53% of the export value reported
in the Customs database.11 This representation is comparable to that of Bernard et al. (2009) for US data andWang and Yu (2012) for Chinese data.
The summary statistics are reported in Table 1 which has three sub-tables. Tables 1A and 1B show thatexport product scope has a very large variation: The minimum of export product scope is 1 (i.e., a single
product), whereas the maximum is 527, with the mean equal to 6.49. Approximately 79% of the Chinese …rms
(in our merged dataset) exported more than a single product from 2000 to 2006 and accounted for 91.4% of thetotal exports. Moreover, approximately two-thirds of the …rms exported less than 5 products, 90% exported
less than 15 products, and only 5% exported more than 25 products.
As shown in Table 1C, China’s home tari¤s (measured at both industry and …rm levels) declined by approx9 In fact, the aggregate data for the manufacturing sector in the annual China’s Statistical Yearbook are compiled from the CME

dataset.
1 0 The reason for selecting eight workers as the threshold is that …rms with less than eight workers fall under a di¤erent legal
regime, as mentioned by Brandt et al. (2012). We adopt this criterion also because a very small company may not have a goodaccounting/reporting system. However, our results are not sensitive to this critical level.
1 1 Our merged dataset has higher mean of sales than the full-sample CME database; this …nding indicates that large …rms arelikely to be matched. The same matching procedure was used by Yu and Tian (2012).

8

imately 50% from 2000 to 2006, whereas foreign tari¤s (measured at the …rm level) decreased by only 3%. One
possible reason for the slight decline of foreign tari¤s is that most important export destinations for Chinese…rms are developed countries which typically had already had a low import tari¤s in the beginning year (i.e.,
in 2000) of our sample (See Yu, 2013, for a detailed discussion).
[Table 1]

2.3

Estimates

The estimation results from the same regression model (1) can di¤er both quantitatively and qualitatively depending on our assumption of the distribution of the dependent variable. We …rst assume a normal distribution.
The OLS regression estimates are shown in Table 2. Both home and foreign tari¤s are positively associatedwith …rms’export product scope. This result suggests that …rms reduce their export product scope in response
to both home and foreign tari¤ cuts.
[Table 2]
Our data clearly show that most of the …rms export a small number of products, and only a few of them exporta very large number of products. This observation suggests that the dependent variable does not follow a normal
distribution. Thus, the OLS result reported in column (1) of Table 2 could be biased. Because our dependent
variable is a non-negative count number, the use of count-data estimates would be more reliable (Cameron andTrivedi, 2005). Given that Poisson distribution is the most popular discrete distribution used to capture thecharacteristics of various count data, we also calculate the Poisson estimate (with a clustered robust standardeerror). Speci…cally, the dependent variable is assumed to have a probability function f (e; ) = exp( e! ) ; where= exp(X0) and X denotes the vector of independent variables. The regression results are shown in column(2) of Table 2. The main results obtained from OLS remain valid qualitatively. Both home and foreign tari¤s
have positive and signi…cant e¤ects on export product scope.
Although Poisson distribution is the most popular approach for count data, it may not provide the best
representation of our sample distribution. If our sample follows a Poisson distribution, then the mean andvariance of a …rm’s export product scope should be identical and equal to . However, our data reveal thatthe variance of the sample (var(e) = 96:9) is approximately 15 times larger than its mean (e = 6:5), whichindicates that Poisson distribution does not provide a good representation of our data. Moreover, our test of
the goodness of …t for the Poisson model reports an extremely large

2value (607,445), which again con…rms

the inappropriateness of Poisson distribution for our dependent variable.
We then resort to negative binomial distribution because it allows the sample to exhibit a pattern of overdispersion. The probability density function of negative binomial distribution has the following form: f (e; ) =
9

1

(
(

1

+e)
) (e+1) (

1
1

+

)

e

1
1

+

, where= exp(X0) and( ) is the Gamma function. In fact, whendrawing a graph based on the proportion of …rms with di¤erent export product scope (see Figure 1), we noticethat the negative binomial distribution approximates the observed distribution much better than the Poisson
distribution. Since around 80% of the exporters have export product scope less than 10, we assume 10 as the
maximum value of the discrete level.12 We report the negative binomial regression results in column (3) of
Table 2. We …nd that the over-dispersion parameterdi¤erent from zero (we obtaingenerated by the likelihood ratio test is signi…cantly= :660 from the regression), indicating that negative binomial distribution isa good probabilistic representative of our data.13 The coe¢ cients of both home and foreign tari¤s are positiveand statistically signi…cant. We include year-speci…c …xed e¤ects in the regressions because several other timevariant variables, such as exchange rate, may a¤ect the …rms’optimal export product scope. We also include…rm-speci…c …xed e¤ects to control the e¤ects of …rm-invariant variables, such as …rm location.14 The …xede¤ect negative binomial estimates are presented in column (4) of Table 2. The coe¢ cients of home and foreigntari¤s are again positive and highly signi…cant. When year …xed e¤ect is introduced, another control variable,
China’s GDP, is dropped out automatically.
[Figure 1]
All estimates in Table 2 show that more-productive …rms have a larger export product scope. This …nding
supports the prediction of existing theoretical studies on multiproduct …rms using Chinese data. Gravity models
indicate that the GDP of two trading countries has positive e¤ects on bilateral trade ‡ows. We obtain somee¤ects of gravity on export product scope. On the one hand, we …nd that the GDP of foreign countries increases
the export product scope of Chinese …rms. To better evaluate the e¤ects of foreign countries’GDP, we construct
and use …rm-speci…c GDP in our analysis by using the share of a …rm’s export to each country as the weightof the corresponding importing country’s GDP. On the other hand, the e¤ect of China’s GDP on the export
product scope of Chinese …rms is insigni…cant (with an erratic sign). This e¤ect is not be emphasized in
our analysis because it will be automatically eliminated when …rm-speci…c and year-speci…c …xed e¤ects arecontrolled. We also obtain the following observations: (1) a …rm’s capital-labor ratio has a negative e¤ect on
its export product scope; (2) SOEs have larger export product scopes than non-SOE with other things equal;and (3) …rms that engage in processing trade have smaller export product scopes than other …rms, i.e., thosethat only engage in ordinary trade.15
1 2 Changing the maximum number does not change our estimation results.
1 3 We also perform regression based on gamma distribution and obtain results very similar to those of negative binomial distrib-

ution. Such results are not presented in the table to save space but are available upon request.
1 4 Firm-speci…c …xed e¤ects in the negative binomial model apply to the distribution of the dispersion parameter (Hardin and
Hilbe, 2003).
1 5 Some …rms change their types of ownership and shipment mode (i.e., processing or ordinary exports). Hence, SOE, foreign, andprocessing indicators are not eliminated from the …xed-e¤ects estimates. The transitional probability matrixes are not reported to10Four important caveats relate to home tari¤s. First, two groups of …rms are special. Pure domestic …rms
do not have any exports; thus, their export product scope (zero) is insensitive to changes in home and foreigntari¤s. By contrast, pure exporting …rms have no domestic sales; thus, home tari¤s do not have any e¤ect on
their export product scope. We omit these two groups of …rms from the sample to obtain better results. The
regression results are shown in column (5) of Table 2. All the coe¢ cients are very close to their counterparts incolumn (4). This result implies that omitting them from the sample does not change our estimation results.
Second, HTit disregards tari¤s on intermediate goods. However, changes in the intermediate goods’tari¤swill a¤ect the …nal goods’pro…ts, which then a¤ect the …rm’s decision on the total product and export productscopes of the …nal goods. Moreover, trade liberalization in …nal goods is often accompanied by trade liberalization in intermediate goods. Hence, the cost e¤ects associated with tari¤ changes on intermediate goods must
be controlled. Accordingly, we include "home input tari¤s" as an additional independent variable. Processing
imports are duty-free in China; hence, even …rms that import the same set of inputs may face di¤erent e¤ectivetari¤s. This phenomenon makes it more di¢ cult to construct …rm-speci…c "home input tari¤s." Given that a…rm can engage in both processing and non-processing imports, we adopt the index of …rm-speci…c input tari¤s(F ITit ) suggested by Yu (2013) as our "home input tari¤s" for …rm. The index is
F ITit =

X

k2Oi

P

mki;initial_yearkk2Mi mi;initial_year

k
t;

(4)

where mki;initial_year is the value of …rm i’s imports of intermediate good k in the …rst year the …rm appears inthe sample, Oi is the set of …rm i’s non-processing imports, and Mi is the set of the …rm’s total imports. The
set of processing imports does not appear in (4) because processing imports are duty free. F ITit is constructedwith time-invariant weights to avoid the a¤ord-mentioned endogeneity of weighted tari¤s, measuring the importweight of each product based on data on the …rm’s …rst year in the sample. Table 3 shows the negative binomial
estimates when "home input tari¤s" is included as a control variable. Trade liberalization in intermediate goods
imports lowers export product scope. This result is counter-intuitive. However, the result is reversed for lessintegrated …rms. In any case, the inclusion of such a control variable does not alter the e¤ects and signi…canceof the two key variables, that is, home tari¤s and foreign tari¤s of the …nal goods.
[Table 3]
The third caveat is that in constructing HTit , we assume that the share of each product a …rm sells in thedomestic market is the same as that in the foreign market. This is de…nitely untrue, but we would not be able tosolve the problem directly because of data limitations. We eliminate the pure domestic and pure exporting …rms
from the sample to address this problem in part because these two types of …rms violate the aforementioned
save space but are available upon request.

11

assumption to the largest degree. We also perform the following auxiliary regressions to verify the robustness ofthe main results. China holds an important position in global supply chains (GSCs), and di¤erent …rms engage
in GSCs at di¤erent degrees (Yu, 2013). As a result, the di¤erences between their sales distribution in thedomestic market and that in foreign markets are also di¤erent. We classify all two-digit Chinese industries into
two groups, namely, less integrated and more integrated, according to their "production depth" of engaging in
GSCs, which is measured by the ratio of value-added to gross industrial output (OECD, 2010). The divisionline is the mean of the production depth ratio across industries. We then run the regressions separately for these
two groups and obtain the estimates in columns (2) and (3) of Table 3. These two groups have di¤erent degrees
of approximation to the "equal share" assumption; however, we …nd that for both the less-integrated group andmore-integrated group, home and foreign tari¤s have the same qualitative results (sign and signi…cance) as in
the main model. Hence, our main …ndings are not sensitive to the "equal share" assumption.
The fourth caveat is that HTit does not include tari¤s on products that …rm i does not produce. When
there is a change in the tari¤s on products that a …rm does not produce but is competing with, the pro…ts of the…rm’s existing products and those the …rm may potentially produce will be a¤ected. Consequently, the …rm’s
decision on its total product and export product scopes may change. The reason is obvious. Suppose that a…rm produces products x and y and product z’s tari¤ experiences a large reduction. Producing z then becomes
pro…table for the …rm. However, if the …rm produces z, its pro…t from existing products may decrease (e.g.,
drawing resources away from production of existing products). Thus, the total product scope may expand or
shrink, depending on how the existing products’pro…ts are a¤ected. We adopt an industry-wide, as opposed to…rm-speci…c, tari¤ to replace HTit to capture this e¤ect. The regression results are presented in column (4) of
Table 3. The coe¢ cients of both home and foreign tari¤s are positive and signi…cant.
Lastly, tari¤ changes may induce a new entry. The export product scopes of new entrants may di¤ersigni…cantly from those of the incumbents. In this case, the estimate may not re‡ect the actual e¤ects of tari¤changes on existing …rms’export product scope because it also includes the new entrants’export product scope.
We run a balanced panel regression to separate these e¤ects and report the results in column (5) of Table 3.
The sign and signi…cance of the home tari¤ e¤ects do not change.

2.4

Role of Firm Heterogeneity in Productivity

The general conclusion from Table 3 is that Chinese …rms would reduce their export product scope in response
to home and foreign tari¤ cuts. As Qiu and Zhou (2013) pointed out, existing theoretical studies and empirical…ndings show that heterogeneous …rms with di¤erent productivities may or may not respond to trade liberalization in the same manner with regard to their product scope adjustment. This issue is investigated with
Chinese data in the present study. We divide all …rms into two groups: low-productivity and high-productivity

12

…rms within each industry. We then combine all low- (high-) productivity …rms from all industries as the low(high) productivity category. Table 4 shows the negative binomial estimates for the low productivity category
in columns (1) and (2) and the high productivity category in columns (3) and (4). Columns (1) and (3) containestimates without …xed e¤ects, and columns (2) and (4) contain estimates with two-way …xed e¤ects. The keycoe¢ cients, namely, home and foreign tari¤s, are positive and signi…cant. That is, the low-productivity andhigh-productivity Chinese …rms adjust their export product scope in the same direction in response to tari¤cuts. Firm heterogeneity in productivity does not matter in this regard.
[Table 4]

2.5

Endogeneity Issues

The estimates in Tables 2-4 may encounter the endogeneity issues of reverse causality. When …rms are forced toreduce their export product scope because of the tough import competition induced by home tari¤ cuts, they
may lobby the government for imposing temporary trade restrictions (Grossman and Helpman, 1994; 1996;
Bown and Crowley, 2013). Thus, export product scope could reversely a¤ect home tari¤s (HTit ).16 Evidence
for such a phenomenon exists in developing countries, such as Turkey (Gawande and Bandyopadhyay, 2000),
and developed countries, such as the U.S. (Goldberg and Maggi, 1999). This phenomenon may not occur in
China because of China’s special policy regime and strong regulations on labor unions. Nevertheless, we check
whether our main results are sensitive to this potential problem. We control for such reverse causality by using
an IV approach.
Inspired by Amiti and Konings (2007) and Yu (2013), here we construct a one-year lag of home tari¤s as the
instrument by replacingkt in Eq.

(2) withkt 1. The idea is that the government is generally hard to remove

the high protection status quo from an industry with high tari¤s, perhaps due to the pressure from domestic
special interest groups. Thus, compared to other industries, sectors with high tari¤s one year ago would still
have relatively high tari¤s in the current year.
We report the IV Poisson estimates in columns (1)-(3) of Table 5.17 All variables are measured at theirlevels but "home tari¤s" is treated as an endogenous variable whereas a "one-year lag of home tari¤s" serves as
the instrument. Column (1) includes the two key variables (i.e., home tari¤s and foreign tari¤s), the two-digit
industry-speci…c …xed e¤ects, and the year-speci…c …xed e¤ects. Column (2) adds more control variables such as…rm productivity (TFP1), log weighted GDP of importers, foreign indicators, SOE indicators, and processing
1 6 However, it is not a worry for the reverse causality of foreign tari¤s (F T ) since Chinese …rm’s export product scope would notitreversely a¤ect the import tari¤s imposed by all trading partners. This is especially true when foreign tari¤s are already measured
by time-invariant weight.
1 7 The IV Poisson estimates implement a generalized method of moments (GMM) estimator of Poisson regression and allow
endogenous variables to be instrumented by excluded instruments. Poisson regression assumes E(ejx) = exp(Xb) to obtain aconsistent estimate of b; thus, it is appropriate for a wide variety of models where the dependent variable is non-negative (Mullahy,
1997; Nichols, 2007).

13

indicators. After controlling for endogeneity, both home tari¤s and foreign tari¤s are still positive signi…cant,suggesting that both home tari¤ reductions and foreign tari¤ cut reduce home …rm’s export product scope.
Column (3) drops pure processing …rms out of the sample, which yields similar results.
Finally, column (4) runs the IV Poisson estimate where all variables are in one-period di¤erence.18 Given
that di¤erence estimates wipe out all unobserved …rm heterogeneity (Amiti and Konings, 2007), we only include
year-speci…c …xed e¤ects to control the time-variant factors. Inspired by the work of Tre‡er (2004), we employ
two-period di¤erences in …rm-level home tari¤s as the instrument of …rm-level home tari¤s. Both home tari¤s(HTit ) and foreign tari¤s (F Tit ) are again positive and signi…cant. The Cragg-Donald

2

statistics is above thecritical values suggested by Stock and Yogo (2005). This result indicates that our IV estimates are not weakly
identi…ed.
[Table 5]

2.6

Summary and Issues

Two important results are obtained in this preliminary empirical analysis and they deserve further investigation
and understanding. First, we …nd that …rms adjust their export product scope similarly in response to home andforeign tari¤ cuts. However, these two types of tari¤ reduction have opposite e¤ects on the …rms. As indicatedin literature (e.g., Qiu and Zhou, 2013), bilateral trade liberalization poses both a threat and an opportunity
to every …rm. A home tari¤ cut intensi…es domestic competition; this situation is not good for the home …rms.
By contrast, a foreign tari¤ cut makes the domestic …rms’export more pro…table. This conventional wisdom
does not clarify our …nding on Chinese …rms’export product scope adjustment.
Second, we …nd that …rms with di¤erent levels of productivity adjust their export product scope in thesame direction in response to tari¤ cuts. However, recent literature on heterogeneous …rms suggests thathigh-productivity …rms normally behave di¤erently from low-productivity …rms.
We explore the two issues mentioned above by …rst developing a theoretical model (in Section 3) and thentesting the predictions from the model (in Section 4).

3

Theoretical Model and Analysis

Our model consists of a world with two countries: China and Foreign. Each country has two industries, namely,
the numeraire good industry and the di¤erentiated products industry. Di¤erentiated products are produced bya continuum of …rms with measure 1, and numeraire good is produced by atomic …rms.
1 8 We eliminate observations with negative …rst-di¤erence of export product scope from the sample.

14

3.1

Technologies

In China, every …rm in the di¤erentiated goods industry can produce multiple products. All …rms employ thesame production technology but have di¤erent managerial capabilities.19 On the production side, we supposethat a …rm produces a set of products with measure s. We index the …rm’s core competency as product 0 andthe others in descending productivity in [0; s]; this indexing captures the situation wherein products further
away from the core competency become less productive. We let the unit cost of producing the ith product be
ci = c + i, where> 0 captures the decline of productivity. The unit cost of producing the core competency

is c. Introducing a product is costly. If a …rm introduces s products, a …xed cost equal to ks will exist, wherek > 0.
On the management side, we assume that each …rm in each market incurs a cost of managing sales, called
management cost. Management cost is a …xed cost with regard to the sales value/volume, but is increasing
with regard to the number of products.20 This is justi…ed by the fact that a …rm needs to have di¤erent sales
teams for di¤erent product lines which have di¤erent features, functions and target consumers. Moreover, we
further assume that …rms are of two types: e¢ cient …rms that have lower management cost, denoted by ml per
product line per market, and ine¢ cient …rms that have higher management cost, denoted by mh per productline per market, with mh > ml . Let

denote the fraction of e¢ cient …rms in the economy.

Given that our focus is on Chinese …rms, we simplify the situation for foreign …rms. We assume that acontinuum of identical foreign …rms produce the di¤erentiated goods. All of them have the same marginal costof production, which is assumed to be zero. Each …rm produces a single product. Managing sales has no cost,and product introduction has no …xed cost. The measure of foreign …rms is also assumed to be 1.

3.2

Product Markets

Following Melitz and Ottaviano (2008), we assume that Z identical consumers exist in China, with each having
a quasi-linear preference for the numeraire good and all varieties from the di¤erentiated goods industry.
U = Q0 +

Z

i2

where

;

, and

qi di

1
2

Z

i2

2

qi di

1
2

Z

qi2 di;i2

are all positive constants; Q0 is the consumption of the numeraire good;is the set of

all varieties sold in the Chinese market; and qi is the consumption of variety i. A consumer maximizes her
utility subject to a budget constraint. As a result, the market demand for variety i by all Z consumers is
1 9 We will examine the case of …rm heterogeneity in production productivity in Subsection 3.4.
2 0 In our earlier version of the paper, we also assume that management cost is increasing in sales. The present assumption is more
realistic.

15

pi =

Z

Z

j2

qj dj

Z qi , from which we obtain the demand function for variety i as

pi = Abqi , where A =+ P
and b = :M+
ZIn the above demand function, pi is the price of variety i, M is the measure of(5), and P =R

i2

pi di is theaggregate price of all varieties. Slope b is exogenous, but the intercept A is endogenous, depending on the degreeof product substitution ( ) and the degree of product market competition (captured by the endogenous P andM ).
The set of varieties,, is large; thus, the seller of variety i regards himself as a small monopolist of variety

i whose decision has no direct e¤ect on other products.21 Competition in the market is captured completely by
the vertical intercept of the demand function (A).
The foreign country also has Z consumers and the same demand structure as China. In particular, thedemand function for variety i is
pi = Abqi , where A =

+ P
M +

:

(6)

In this demand function, M is the measure of the set of varieties sold in the foreign market, which is denoted
R
by F , and P = i2 F pi di is the aggregate price of all varieties in the foreign market.
We assume that tari¤s take the form of iceberg transport cost. We let t (> 1) and t (> 1) denote China’stari¤ and the foreign country’s tari¤. Then, t units of a product must be produced by a foreign …rm to sell one
unit in the Chinese market, and t units of a product must be produced by a Chinese …rm to sell one unit in
the foreign market. Free trade exists in the numeraire good industry.

3.3

Firms’Decision

We …rst analyze the Chinese …rms’ decisions. Each …rm takes A and A as given when making its decisions.
Because all Chinese …rms have the same production productivity, without loss of generality, we set c = 0 to
reduce notation. Suppose that …rm j (j = l for e¢ cient …rm and j = h for ine¢ cient …rm) decides to introduce
a range of products, [0; sj ], which is called the …rm’s total product scope, and export a range of products [0; ej ],which is called the …rm’s export product scope.22 With consumer preference and market size (Z) in the twomarkets being the same, Chinese …rms have a disadvantage in the foreign market because they face tradeprotection in the foreign market. Hence, in equilibrium, a Chinese …rm will not introduce a product that isexported to the foreign market but not sold to the domestic market, that is, ej

sj . If ej < sj , then some

2 1 Following most studies in the literature, we do not consider carniberlization, which is about strategic competition among
varieties of the same …rm.
2 2 If a …rm produces product i 2 [0; s], it will produce all products i0 < i because of the decreasing e¢ ciency in [0; s]. If it exports
product i 2 [0; e], it will export all products i0 < i.

16

products (i 2 (ej ; sj ]) are sold in the domestic market but are not exported; the …rm’s export products are asubset of its total products. This is the case when the …xed cost of product introduction (k) is not too large.
Under this circumstance, the …rm’s decision in the home market is expressed as
Z sj[(A bqi )qi
iqi ] di mj sj ksj :max
sj ;qi(7)0

It is easy to derive the set of …rst order conditions, from which we obtain the optimal total product scopep
A 2 b(mj + k)
sj =
:
(8)
The total product scope is larger with stronger market demand (A), lower management cost, lower cost of
product introduction (k), and slower decline of productivity ( ). The optimal quantity, price, and pro…t of eachproduct are, respectively,
qji =

A

i
2b

, pji =

A+ i
, and
2

ji =

1
(A
4b

2

i) , for all i 2 [0; sj ]:(9)

A stronger demand (i.e., a larger A) leads to a larger output, a higher price and a larger pro…t.
Given ej < sj , the …rm’s optimal decision in the foreign market is provided bymax
ej ;qiZ ej[(Abqi )qi

t iqi ]dimj ej :

0

It is easy to derive the set of …rst order conditions, from which we obtain the optimal export product scope
p
A
2 bmj
ej =
:
(10)
t
The optimal quantity, price and pro…t of each export product for i 2 [0; ej ] are
qji =

A

it
2b

; pji =

A + it
,
2

and

ji =

1
(A
4b

2

it ) :The export product scope is larger with stronger market demand (A ), lower management cost (mj ), slower
decline in productivity ( ), and lower foreign tari¤ (t ). A decrease in foreign tari¤ increases output and pro…t,
but reduces price. Tougher competition (smaller A ) reduces output, price and pro…t.
It is easy to obtain
@ ji
<0
@t

and

@ 2 ji
< 0:
@i@t

(11)

That is, every product bene…ts from foreign country’s tari¤ cut, and due to the iceberg-transportation natureof tari¤, products with higher i bene…t more from the foreign country’s tari¤ cut.
We now analyze the foreign …rms. In the Chinese market, a foreign …rm chooses its quantity to maximize
its pro…t (Abqf c )qf c

tqf c , where subscript fc stands for a foreign …rm in the Chinese market. Thus, theoptimal quantity, price, and pro…t of a foreign …rm in the Chinese market are, respectively,
qf c =A+t
A t, pf c =
, and
2b
2
17

fc =

1
(A
4b

t)2 :

(12)

In the foreign market, the foreign …rm chooses its output to maximize its pro…t (Abqf f )qf f , where subscript¤ stands for a foreign …rm in the foreign market. The optimal quantity, price, and pro…t of a foreign …rm in
the foreign market are, respectively,AA, pf f =
, and
2b
2

qf f =

3.4

ff =

1
A 2:
4b

(13)

Market Equilibrium

Lastly, we determine equilibrium A and A .
Given that A =+ P
M + , we …rst calculate M

and P . M = sl + sh (1) + 1. From (9), we obtain theaggregate price of …rm j in the Chinese market as follows:
pj =

Z sj

pji di =0

1
1
Asj + s2j :2
4Each foreign …rm’s price in the Chinese market is given in (12). Thus, the aggregate price in the Chinese marketis1
2A sl +
4P =To simplify the notation, we let

s2l + 2A(1)sh + (1= 2 . Using the results in A =A=2)s2h + 2(A + t) :+ P
M+yields+ [ s2l + (1)s2h ] + 2t:
2[ sl + (1)sh + 1 + ](14)In the foreign market, the total number of products sold is M = el + (1of Chinese exporter j is
pj =

Z ej
0

pli di =)eh + 1. The aggregate price1
1
A el + t e2j :24Each foreign …rm’s price is provided in (13). Thus, the aggregate price in the foreign market is
P =

1
2A
4

Using the results in A =

+ P
M +

el + te2l + 2A (1)eh + t (1)e2h + 2A:

yieldsA =2 + t [ e2l + (1)e2h ]:
2[ el + (1)eh + 1 + ](15)

Substituting A in (8), we obtain two equations jointly determining the optimal total product scopes sl andsh , which are functions of t. Substituting A in (10), we obtain two equations jointly determining the optimalexport product scopes el and eh , which are functions of t .

18

3.5

Trade Liberalization

We analyze the respective e¤ects of two types of trade liberalization on the export product scope of Chinese…rms. The …rst type of liberalization is tari¤ reduction in China (t), and the second type is tari¤ reduction in
the foreign country (t ).
dehl
We …rst examine dedt and dt . Based on the expression of ej from (10) and A from (15), we immediately

know that t does not have any direct e¤ect on el and eh . However, the optimal export product scope given in(10) is obtained under the condition that el < sl and eh < sh . The Chinese tari¤ cut may eventually resultdshlin the violation of this condition. We prove in Appendix B that ds
dt > 0 and dt > 0. Hence, when a tari¤

cut is implemented in China, Chinese …rms reduce their total product scope (sl and sh ). When tari¤ cuts are
implemented continuously, the total product scope is eventually reduced to the level of the export product scope(el and eh ). Once el = sl , the optimal export product scope of e¢ cient Chinese …rms is no longer given in(10), and once eh = sh , the optimal export product scope of ine¢ cient Chinese …rms is no longer given in (10).
dedsThen, we have dtj = dtj > 0.
We then discuss the e¤ect of foreign tari¤ cuts on Chinese …rms’export product scope. We prove in Appendix
B that(i)deldeh< 0, and (ii)
> 0 i¤ mh is su¢ ciently large:
dt
dt

(16)

The above analysis allows us to establish the following proposition.
Proposition 1. (i) Suppose that there is a drastic cut of import tari¤ s in China. Then a further tari¤ cut
reduces all Chinese …rms’ export product scope.
(ii) In response to a tari¤ cut by the foreign country, Chinese …rms with high managerial e¢ ciency expand
their export product scope, whereas those with low managerial e¢ ciency reduce their export product scope if andonly if their management cost is su¢ ciently high (i.e., mh is su¢ ciently large).

The results of the proposition are surprising. On the one hand, one may ask why a domestic tari¤ cut a¤ects
export product scope. On the other hand, one may ask why some exporters are negatively a¤ected by a foreigntari¤ cut such that they have to reduce their export product scope. The explanation is as follows. With regard
to domestic tari¤ cut, each …rm incurs a cost of introducing (or maintaining) every product it produces. By
retaining a product, a …rm obtains pro…t from the market; however, discontinuing a product results in savingfrom the …xed cost of production introduction (k) and the …xed cost of sales (mj ). When the domestic market
is very pro…table, the pro…t from the domestic market alone can cover the …xed costs. In that case, el < sland lowering the Chinese tari¤ reduces a Chinese …rm’s domestic market pro…t, which results in a reduction
19

in total product scope but not in export product scope because the latter is only a¤ected by foreign market
pro…tability. However, when a drastic tari¤ cut is implemented, the …rm reduces its product scope to a large
extent that the set of products available for export is likewise reduced. This is how domestic trade liberalizationa¤ects exports.
A foreign tari¤ cut has a direct and an indirect e¤ect on Chinese exporters. Every …rm’s marginal productearns zero pro…t from the foreign market. On the one hand, the foreign tari¤ cut lowers the cost of every
exported product and thus increases pro…ts. The latter induces all …rms to expand their export product scope.
This phenomenon is the cost e¤ect, which is positive and direct. On the other hand, a low cost causes every…rm to reduce the prices of all its products, which increases competition (lowering A ). This competition e¤ect,which is negative and indirect, reduces the pro…ts of all products (including the marginal products) and tends toreduce every …rm’s export product scope. The export product scope of a …rm is reduced or expanded dependingon the net e¤ect on its marginal product. The competition e¤ect is the same for all products as it shifts down
the demand intercept (A ). However, the cost e¤ect is di¤erent for the e¢ cient …rms and the ine¢ cient …rms.
An e¢ cient …rm’s marginal product has a higher marginal cost of production (el ) than that of an ine¢ cient…rm (eh ) because the marginal products of all …rms earn zero pro…t and the cost of a product is the sum of
its management and production costs. As discussed earlier right below (11), a reduction in foreign tari¤ (inthe form of iceberg transport cost) reduces all products’cost of production by the same percentage; thus, ane¢ cient …rm bene…ts more than an ine¢ cient …rm because the former enjoys a larger cost reduction in theabsolute term. This is why under certain conditions, the net e¤ect is positive for e¢ cient …rms (the cost e¤ect
is larger than the competition e¤ect) but negative for ine¢ cient …rms (the competition e¤ect is larger than thecost e¤ect).

3.6

Firm Heterogeneity in Production Productivity

The most interesting message from Proposition 1 is that when a tari¤ cut is implemented in the foreign country,
Chinese …rms with di¤erent managerial e¢ ciency levels respond in opposite directions. In this section, we
examine whether the heterogeneous response to foreign tari¤ cuts by …rms with di¤erent managerial e¢ ciency
levels can be reinterpreted as by …rms with di¤erent production productivity levels, namely, the usual Melitz(2003) type of …rm heterogeneity. We make a few modi…cations to the main model. First, we assume that
Chinese …rms are homogeneous in managerial e¢ ciency to obtain a clean result; for simplicity, we let ml =mh = 0. Second, Chinese …rms di¤er in production productivity. We assume that the cost of core competencyc is uniformly distributed in [0; 1]. Third, we merely focus on equilibrium analysis in the foreign market.
The derivation of optimal export product scope is similar to that in the main model except for one di¤erence:
all equilibrium variables are functions of c: Suppose that a …rm has cost of core competence equal to c, called

20

…rm c. Then, …rm c’s optimal export product scope ise(c) =

A

ct
t

=

A
t

c

:

(17)

We assume that the foreign tari¤ is not too high or the foreign demand is su¢ ciently strong such that all Chinese…rms export to further simplify the analysis. This condition requires A > t which we assume to hold below.
The export quantity, price, and pro…t of …rm c’s ith product areqi (c) =A

A + (c + i)t(c + i)t; pi (c) =
,
2b
2

and

i (c) =

1[A
4b(c + i)t ]2 :Thus, the aggregate price of …rm c is given by
Z e(c)
1
1
p (c) =pi (c)di = ( A + ct )e(c) + t e(c)2
2
4
0
(A
ct )
(3A + ct ):=4 t
The aggregate price in the foreign market is given by
Z 1
1
9A 2 3A t + t
1
P =p (c)dc + A =
+ A :
2
12
t
2
0
The number of products in the foreign market is given by

M =

Z 1
0

A

ct
t

dc + 1 =The above expressions of P and M , together with A =

2A
t
+1
2 t 2
+ P
M +

, allow us to solve for the equilibrium A

as a function of t , expressed as A (t ). Substituting equilibrium A into the expression of optimal productscope (17), we obtaine(t ; c) =de(t ;c)dt

A (t )tc:

is independent of c. That is, all …rms respond to the foreign tari¤ cut in the same direction. Our;c)numerical example shows that de(t< 0. That is, in response to the foreign tari¤ cut, all Chinese …rms
dt

expand their export product scope.
One may ask why the result is di¤erent from Proposition 1, which is derived from the model with …rmheterogeneity in managerial e¢ ciency. In the main model, the two types of …rms have di¤erent production
productivity levels for their marginal products; thus, foreign tari¤ reduction a¤ects them di¤erently. However,with homogeneity in managerial e¢ ciency and heterogeneity in production productivity, all …rms have the same
production productivity for their marginal products. This condition can be seen by substituting (17) into thecost of a …rm’s marginal product: c + e(c) = A t(t ) , which is independent of c. Thus, all …rms’ marginal
products will be a¤ected similarly.
21

4Further Empirical Analysis: The Role of Managerial E¢ ciency

Guided by our theoretical analysis, we conduct an empirical investigation with emphasis on the responses ofheterogenous …rms to foreign tari¤ cuts.
We …rst construct a measure of managerial e¢ ciency. We follow management science literature (e.g., Miller
and Vollmann, 1985; Cooper and Kaplan, 1991; Fisher and Ittner, 1999) and use a …rm’s overhead expenses
to proxy for management costs. Speci…cally, we construct two indicators of managerial e¢ ciency for each …rm:
high overhead indicator (representing low managerial e¢ ciency) and low overhead indicator (representing highmanagerial e¢ ciency). We rank all …rms in each industry according to their overhead expenses (in logarithm).
When a …rm’s overhead expenses is higher than the top 25th quantile, the …rm has low managerial e¢ ciency;
its high overhead indicator takes the value one, and its low overhead indicator takes the value zero. Similarly,
when a …rm’s overhead expenses is lower than the bottom 25th quantile, the …rm has high managerial e¢ ciency;
its high overhead indicator takes the value zero, and its low overhead indicator takes the value one. The low
and high overhead indicators of …rms with overhead expenses between the 25th and 75th quantile are zero. We
then pool …rms from all industries while maintaining the value of their indicators.
After introducing managerial e¢ ciency and distinguishing it from production productivity, we realize a
problem in the TFP measure. The conventional measure of TFP, including our TFP1, is a Solow residual that
includes both managerial e¢ ciency and production productivity. To clarify this, consider the following standard
Cobb-Douglas gross production function
ln Yit =k ln Kit +l ln Lit +m ln Mit + xit + "it ;(18)

where Yit , Kit , Lit , Mit , and xit are …rm i’s sales, capital, labor, intermediate inputs, and productivity in year t,
respectively.23 The conventional Olley-Pakes measure of productivity involves obtaining the di¤erence between
log output and log factor inputs times their estimated coe¢ cients as follows:
T F P 1itxit = ln Yit^ k ln Kit^ l ln Lit^ m ln Mit :(19)

In this approach, …rm productivity (TFP1) is clearly correlated with the ex-post productivity shock ("it ), suchas managerial e¢ ciency.
We follow Feenstra et al.

(2013) and construct an ex-ante productivity measure called TFP2 to ex-

clude managerial e¢ ciency from our TFP measure.

Suppose that investment Vit in the Olley-Pakes ap-

proach depends on anticipated productivity T F P 2it of the …rm according to the following functional relation:
Vit = g1 (xit ; ln Kit ; EXit ; P Eit ; W T Ot ), where EXit (P Eit ) is the export (processing export) indicator thatmeasures whether …rm i exports (engages in processing exports) in year t, and W T Ot is an indicator that
2 3 Note that Feenstra et al. (2013) worked on a value-added production function instead.

22

equals one for every year after 2001 and zero before 2001 as China became a WTO member in 2001. Inverting
this relation, anticipated productivity can be obtained as
T F P 2it = g1 1 (Vit ; ln Kit ; EXit ; P Eit ; W T Ot ):(20)

Appendix C provides a more detailed discussion on our TFP1 and TFP2 construction. Table A2 in the Appendix
provides the industry-level estimates of the …rms’TFP1 and TFP2 in each industry, together with the associatedcoe¢ cients of labor, capital, and materials.
With the newly constructed TFP2, we consider the following speci…cation to include …rm heterogeneity inmanagerial e¢ ciency.
eit =

0+

1 T F P 2it +2 HTit +3 F Tit +4 F Tit

HOit +

5 F Tit

LOit +

it + it;(21)

where foreign tari¤s appear three times in the equation: the foreign tari¤s itself, its interaction with the highoverhead indicator (HOit ), and its interaction with the low overhead indicator (LOit ). Our theory predicts
that ^ 3 + ^ 4 > 0 and ^ 3 + ^ 5 < 0 because high (low) management cost …rms reduces (increases) their export
product scope in response to foreign tari¤ cuts. The regression results are given in Table 6.
[Table 6]
As shown in column (1) of Table 6, high-productivity …rms have larger export product scopes. Again,
we …nd that both home and foreign tari¤ variables have positive and statistically signi…cant coe¢ cients. More
importantly, the interaction between foreign tari¤s and the high managerial e¢ ciency (i.e., low overhead) dummyis negative and signi…cant, with a much larger economic magnitude than the own coe¢ cient of foreign tari¤s.
This …nding indicates that the e¤ect of foreign tari¤s on export product scope is negative (i.e., 0:002 0:005 < 0).
Thus, a foreign tari¤ reduction increases the export product scope of high managerial e¢ ciency …rms. By
contrast, the coe¢ cient of the interaction between foreign tari¤s and the low managerial e¢ ciency (i.e., highoverhead) indicator is positive and signi…cant. Given that 0:002 + 0:003 > 0, a foreign tari¤ reduction reduces
the export product scope of low managerial e¢ ciency …rms. Middle managerial e¢ ciency …rms also reduce theirexport product scope (as indicated by the coe¢ cient value 0:002).
Since home tari¤s measure may face some potential pitfalls due to our data limitations, it is interesting to see
whether our results are driven by such a measured home tari¤s. Hence, we replace …rm-speci…c time-invariant
home tari¤s with simple industry-level home tari¤s in column (2) of Table 6, we …nd that all results remainrobust. However, the coe¢ cients of …rm’s true production e¢ ciency (TFP2) are no longer signi…cant, thoughstill positive, in both column (1) and (2). We suspect this is due to the inclusion of pure processing …rms which,by de…nition, sell all of their products to other countries. Such …rms are found to be less productive than nonpure processing …rms (Dai et al., 2013). To make sure that our results are not driven by the pure processing
23

…rms, we drop them in column (3). We still …nd that both time-invariant home tari¤s and time-invariant
foreign tari¤s are positive and signi…cant, and the interaction terms with foreign tari¤s exhibit a similar pattern
as discussed above. Finally, it is reasonable to ask how sensible our results are to the 25th quantile overhead
expenses threshold. In column (4) we take a more parsimonious number and re-de…ne high managerial-e¢ ciency…rms as those with overhead expenses lower than the bottom 10th quantile and low managerial-e¢ ciency …rms
as those with overhead expenses higher than the 90th quantile. We …nd that our results are insensitive to suchan alternative threshold.
[Table 7]
Generally speaking, larger …rms also have higher overhead expenses, other things equal (especially for equalmanagerial e¢ ciency). This raises a concern about the appropriateness of "total overhead expenses" as ameasure of managerial e¢ ciency. We address this concern in two ways. First, we include log of …rm sales as
an additional regressor. Second and more importantly, we use overhead-pro…t ratio, de…ned as a …rm’s total
overhead expenses divided by its pro…t, to de…ne managerial e¢ ciency.24 We, again, use top and bottom 25th
quantiles of …rm’s overhead-pro…t ratio as thresholds to de…ne the low and high overhead indicators in eachindustry. Finally, we perform the Wald test of equality of coe¢ cients ^ 3 and ^ 5 for each speci…cations. The
low p-values in each column con…rm a strong signi…cant di¤erence in the two coe¢ cients between foreign tari¤sand its interaction with the low overhead indicators.
Column (1) of Table 7 reports the negative binomial estimation results. The coe¢ cients of home tari¤s,foreign tari¤s and its interactions with high (low) overhead indicator all have the anticipated signs. However, themagnitude of the interaction term between foreign tari¤s and low overhead indicator is too small to dominate theown coe¢ cient of the foreign tari¤s. We suspect that this is due to the lack of control for endogeneity of hometari¤s. Hence, we run the IV Poisson estimates in columns (2)-(4) by treating home tari¤s as endogenous. By
using previous year’s home tari¤s (with initial weight) as the instrument, the IV Poisson estimation in column(2) shows that the coe¢ cients of home tari¤s, foreign tari¤s and the interactions with overhead indicators
are signi…cant and have the anticipated signs; and more importantly, the magnitude of the interaction term
between foreign tari¤s and low overhead indicator is larger than that of the foreign tari¤s. Since the measure oftime-invariant home tari¤s cannot apply to the sample of pure domestic …rms and pure exporters, as discussed
above, we hence drop the two types of …rms in column (3) and …nd very robust results. The last column ofTable 7 further drops pure processing …rms and still exhibits similar results as those in column (3). Thus, all
our …ndings are robust.
2 4 We use log value for total overhead expenses, but actual value for overhead-pro…t ratio to make the estimate values comparable
in terms of size.

24

5

Concluding Remarks

In this study, we conduct theoretical and empirical analyses on the e¤ects of unilateral tari¤ cuts on …rms’
export product scope. The preliminary empirical analysis based on Chinese data shows that Chinese …rms
reduce their export product scope in response to a domestic tari¤ cut and a foreign tari¤ cut. Low-productivity
and high-productivity …rms behave similarly. We build a theoretical model to fully understand this phenomenon.
The novelty of our theoretical model is that it explicitly incorporates a new dimension of …rm heterogeneity,
namely, managerial e¢ ciency. Our model predicts that the home country’s tari¤ cut reduces all home …rms’
export product scope; however, in response to a foreign country’s tari¤ cut, a home …rm’s export product scope
expands (shrinks) when the …rm’s management cost is low (high). We then conduct another empirical analysis
and obtain strong evidence to support our theoretical predictions.
Firm heterogeneity in managerial e¢ ciency is the new element in our theoretical and empirical models. We
verify the generality of our results based on our speci…cations of managerial e¢ ciency. In our theoretical model,
we model a …rm’s management cost as a linear function of its total sales. After checking, we …nd that theresults also hold if a …rm’s management cost is assumed as a quadratic function of total sales. It will be moreconvincing if we obtain the same results using a more general function of managerial e¢ ciency. Similarly, in ourempirical analysis, we use a …rm’s total overhead expenses and overhead-pro…t ratio, respectively, as a proxyfor management cost. Although these are common measures of management cost in literature, it would beinteresting to explore other measures to con…rm the robustness of our results.

25

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28

Table 1A: Distribution of Firms’Export Product Scope
Export Product Scope
Number of Obs.
Export Value
Percent Cumulative Percent Cumulative
1
21.06
21.06
8.64
8.64
2
15.66
36.72
8.65
17.29
3
11.54
48.25
7.80
25.10
4
8.91
57.16
7.60
32.70
5
6.90
64.07
5.85
38.54
6-15
25.82
89.89
31.35
69.89
16-25
6.01
95.90
10.99
80.88
26-527
4.10
100.0
19.12
100.0
Table 1B: Summary Statistics (2000-2006)
Variable
Mean
Std. Dev.
Export Product Scope
6.49
9.84
Firm Sales (RMB1,000)
150,053 1,061,312
Number of Employees
479
1,687
Home Tari¤s (Firm Level)
8.52
7.70
Home Tari¤s (Industry Level)
11.72
5.59
Foreign Tari¤s (Firm Level)
7.47
7.10
Home Input Tari¤s (Firm Level)
2.12
3.88
Log China’s GDP
28.29
.265
Log Importers’Weighted GDP
28.70
2.43
Overhead-Pro…t Ratio
8.46
234
Log of Overhead Expenses
6.83
2.18
SOE Indicator
.021
.141
Foreign Indicator
.589
.491
Processing Indicator
.286
.452
Notes: Value is in Chinese yuan. US$1 was equivalent to approximately 8.20 yuan for most of the time in 2000-2006.

Year

2000
2006
Change (%)

Table 1C: Tari¤ Reductions
Ind. Home Tari¤s Firm Home Tari¤s Firm Foreign Tari¤s
Mean Std. Dev. Mean Std. Dev. Mean
Std. Dev.
(1)
(2)
(3)
(4)
(5)
(6)
20.34
8.44
15.62
4.13
7.72
8.77
10.11
4.15
7.69
1.60
7.61
8.35
50.31
–
50.77
–
2.43
–

Firm Input Tari¤s
Mean Std. Dev.
(7)
(8)
2.69
5.22
1.70
3.47
36.80
–

Notes: Columns (1)-(2) report the mean and standard deviation of 3-digit industry-level home import tari¤s whereas columns(3)-(4) report …rm-level home import tari¤s as described in Eq. (2). Columns (5)-(6) report the mean and standard deviation of…rm-level foreign tari¤s as described in Eq.(3). The last two columns report the mean and standard deviation of …rm input tari¤s
as described in Eq. (4) in the text.

29

Table 2: Baseline Estimates
Econometric Methods:
OLS
Poisson
Negative Binomial
Regressand: Export Product Scope
(1)
(2)
(3)
(4)
(5)
Home Tari¤s (Firm-Level)
0.134*** 0.015*** 0.016*** 0.007*** 0.008***(29.77)(21.45)(37.43)(10.91)(10.55)
Foreign Tari¤s0.103*** 0.013*** 0.013*** 0.003*** 0.003***(21.58)(25.27)(27.17)(9.21)(6.68)Log Firm TFP (TFP1)
2.781*** 0.273*** 0.344*** 0.035*** 0.051***(22.92)(19.85)(40.59)(4.19)(4.78)
Log China’s GDP
0.630*** 0.093*** 0.076***(4.21)(4.25)(5.91)
Log Weighted GDP of Importers
1.367*** 0.223*** 0.155*** 0.123*** 0.130***(60.91)(43.74)(131.14)(54.57)(46.25)
Log Capital-Labor Ratio
0.028
0.007
-0.011***-0.0010.002(1.01)(1.29)(-4.69)(-0.28)(0.29)
Foreign Indicator
0.443***
0.015
0.082*** 0.113*** 0.109***(5.39)(0.77)(12.91)(5.11)(4.11)SOE Indicator
1.062*** 0.158*** 0.118***-0.059-0.081*(3.28)(2.64)(5.03)(-1.50)(-1.91)
Firm-speci…c Fixed E¤ects
No
No
No
Yes
Yes
Year-speci…c Fixed E¤ects
No
No
No
Yes
Yes
Pure Domestic Firms Dropped
No
No
No
No
Yes
Pure Exporting Firms Dropped
No
No
No
No
Yes
Prob.> 2
.000
.000
.000
.000
.000
Observations
87,76387,76387,763
63,844
43,191
Note: Robust t-values corrected for clustering at the …rm level in parentheses. *(**) indicates signi…cance at the 10% (5%)
level. 23,919 observations are dropped in Columns (4) and (5) because of only one observation per group.

30

Table 3: Negative Binomial Estimates
Regressand: Export Product ScopeAll
GSCs Integrated
Sample
Less
More
(1)
(2)
(3)
Home Tari¤s (Firm-Level)0.009*** 0.008*** 0.009***(9.11)(5.81)(6.75)
Home Tari¤s (Industry-Level)
Foreign Tari¤sHome Input Tari¤s
Log Firm TFP (TFP1)
Log Weighted GDP of Importers
Log Capital-Labor Ratio
Foreign Indicator
SOE Indicator
Firm-speci…c Fixed E¤ects
Year-speci…c Fixed E¤ects
Pure Domestic Firms Dropped
Pure Exporting Firms Dropped
Balanced Panel Considered
Prob.> 2
Observations

0.002***(3.01)
0.084(0.62)
0.032**(2.53)
0.128***(33.27)0.003(0.41)
0.147***(3.99)-0.108**(-2.01)
Yes
Yes
Yes
Yes
No
.000
22,2910.001(1.62)
0.170(0.71)
0.046**(2.18)
0.118***(18.65)0.011(0.97)
0.054(0.92)-0.073(-0.67)
Yes
Yes
Yes
Yes
No
.000
7,738

0.002***(2.96)
0.126(0.75)
0.030(1.60)
0.131***(26.31)-0.002(-0.23)0.192***(3.89)-0.119*(-1.93)
Yes
Yes
Yes
Yes
No
.000
14,090All Sample(4)0.010***(17.13)0.004***(16.49)0.125***(19.46)0.119***(89.72)-0.008***(-3.08)0.128***(13.66)
0.061**(2.37)
Yes
Yes
No
No
No
.000
81,081(5)0.024***(8.06)

0.027***(7.72)-0.632(-1.05)0.186***(3.87)
0.154***(13.40)-0.022(-1.29)-0.272***(-4.15)0.061(0.36)
Yes
Yes
Yes
Yes
Yes
.000
1,821

Note: T-values corrected for clustering at the …rm level in parentheses. *(**) indicates signi…cance at the 10% (5%) level. 19
industries out of 30 are classi…ed as more global supply chain (GSC) integrated. Refer to the text for details.

31

Table 4: Negative Binomial Estimates with Heterogeneous Productivity
Productivity Category
Low Productive
High Productive
Regressand: Exporter Scope
(1)
(2)
(3)
(4)
Home Tari¤s (Firm-level)0.013***
0.011*** 0.019*** 0.013***(23.83)(17.73)(28.81)(20.23)Foreign Tari¤s0.013***
0.005*** 0.012*** 0.007***(20.98)(12.58)(17.21)(13.90)
Log Firm’s TFP
0.363***
0.327*** 0.220*** 0.080***(24.24)(19.51)(18.16)(8.40)
Log China’s GDP
0.056***
0.092***(3.30)(4.65)
Log Weighted GDP of Importers 0.142***0.120*** 0.167*** 0.168***(88.88)(59.60)(95.97)(75.74)
Log Capital-Labor Ratio-0.018*** -0.016***-0.005-0.011***(-5.50)(-4.11)(-1.48)(-3.26)
Foreign Indicator
0.112***
0.155*** 0.040*** 0.111***(13.51)(12.92)(4.07)(9.78)SOE Indicator
0.154***
0.052
0.088**
0.091**(5.08)(1.36)(2.39)(2.40)
Firm-speci…c Fixed E¤ects
No
Yes
No
Yes
Year-speci…c Fixed E¤ects
No
Yes
No
Yes
Prob.> 2
.000
.0,00
.000
.000
Number of Observations
48,441
48,441
39,322
39,322
Notes: Robust t-values corrected for clustering at the …rm level in parentheses.*(**) indicates signi…cance at the 10% (5%)
level.

32

Table 5: IV Poisson Estimates
Measures of Tari¤s
Level
Regressand: Export Product Scope
(1)
(2)
(3)
Home Tari¤s (Firm-Level)
0.004*** 0.005*** 0.003**(2.65)(4.06)(2.56)
Foreign Tari¤s0.005*** 0.007*** 0.007***(3.45)(4.40)(4.01)Log Firm TFP (TFP1)
0.459*** 0.466***(13.24)(12.74)
Log Weighted GDP of Importers
0.152*** 0.155***(39.21)(37.33)Foreign Indicator
0.025
0.045***(1.64)(2.80)SOE Indicator
0.101*
0.105*(1.74)(1.78)
Industry-speci…c Fixed E¤ects
Yes
Yes
Yes
Year-speci…c Fixed E¤ects
Yes
Yes
Yes
Pure Processing Firms Dropped
No
No
Yes
Observations
25,457
24,71822,735First-Di¤erence(4)0.065***(5.10)
0.004**(2.11)
0.019(0.41)
0.126***(6.26)
0.292*(1.81)-0.342(-1.04)
Yes
No
Yes
9,679

Note: T-values in parentheses. *,**,*** indicates signi…cance at the 10%, 5%, and 1% level, respectively. Pure processing …rms
are dropped in columns (3) and (4). Columns (1)-(3) are IV Poisson estimates in which the endogenous variable is home tari¤ withinitial year weight and the instrument is home previous year’s tari¤s with initial year weight. Column (4) is the …rst-di¤erence
IV Poisson estimate where the regressand and regressors are in …rst-di¤erence level. The second-di¤erence …rm-level home tari¤ isused as the instrument for …rst-di¤erence …rm-level home tari¤ with initial year weights.

33

Table 6: Managerial E¢ ciency and Export Product Scope
Cuto¤s for Overhead Indicators
25th
25th
25th
Regressand: Export Product Scope
(1)
(2)
(3)
Home Tari¤s (Firm-Level: ^ 1 )0.011***
0.010***(32.27)(30.12)Home Tari¤s (Industry-Level: ^ 1 )0.008***(13.09)^Foreign Tari¤s ( 3 )0.002***
0.002***
0.002***(7.35)(4.14)(9.28)
Foreign Tari¤s
0.003***
0.004***
0.002***High Overhead Indicator ( ^ 4 )(8.29)(8.52)(7.23)
Foreign Tari¤s-0.005*** -0.005*** -0.006***
Low Overhead Indicator ( ^ 5 )(-7.58)(-5.49)(-9.26)
Log Firm TFP (TFP2: ^ 2 )
0.007
0.016
0.015**(0.93)(1.47)(2.07)
Log Weighted GDP of Importers
0.115***
0.122***
0.118***(109.59)(84.86)(111.88)
Foreign Indicator
0.086***
0.107***
0.095***(9.60)(10.14)(10.44)
SOE Indicator
0.016
0.015
0.032*(0.83)(0.57)(1.76)^^
P-value of Di¤ference between 3 and 5[.000][.000][.000]
Firm-speci…c Fixed E¤ects
Yes
Yes
Yes
Year-speci…c Fixed E¤ects
Yes
Yes
Yes
Pure Processing Firms Dropped
No
No
Yes
Observations
62,37557,65157,49710th(4)0.013***(23.66)0.004***(9.35)
0.008***(12.01)-0.010***(-3.72)0.051***(3.92)
0.162***(88.92)0.125***(13.36)0.026(0.91)[.000]
Yes
Yes
Yes
57,497

Note: T-values in parentheses. * (**) indicates signi…cance at the 10% (5%) level. Columns (1)-(3) use top and bottom 25 thquantiles of …rm’s overhead expenses within its industry as cuto¤s to de…ne the low(high) overhead indicators whereas column (4)uses the top and bottom 10 th quantiles as cuto¤s. All pure domestic …rms and pure exporters are dropped in all estimates. Pureprocessing …rms are dropped in columns (2)-(4).

34Table 7: IV Poisson Estimates with Alternative Measure of Managerial E¢ ciency
Econometric Methods:
Neg. Binomial
IV Poisson
Regressand: Export Product Scope
(1)
(2)
(3)
(4)
Home Tari¤s (Firm-Level: ^ 1 )0.011***
0.023*** 0.027*** 0.026***(27.01)(21.57)(21.71)(19.98)Foreign Tari¤s ( ^ 3 )0.006***
0.003**
0.003**
0.003**(15.41)(2.26)(2.02)(2.04)
Foreign Tari¤s
0.002***
0.006*** 0.005*** 0.006***
High Overhead Indicator ( ^ 4 )(4.43)(4.19)(3.17)(3.21)
Foreign Tari¤s-0.001***-0.004** -0.004** -0.005***
Low Overhead Indicator ( ^ 5 )(-2.66)(-2.47)(-2.55)(-2.60)
Log Firm TFP (TFP2: ^ 2 )0.173***
0.311*** 0.226*** 0.210***(16.05)(15.03)(9.65)(8.70)
Log Weighted GDP of Importers
0.152***
0.146*** 0.146*** 0.148***(104.67)(45.55)(39.95)(38.31)
Foreign Indicator
0.161***
0.106*** 0.088*** 0.096***(20.54)(8.33)(6.23)(6.64)SOE Indicator-0.042*-0.038-0.051-0.051(-1.65)(-0.74)(-0.94)(-0.93)
Log Firm Sales
0.139***
0.187*** 0.191*** 0.191***(50.24)(37.99)(35.14)(33.77)
P-value of Di¤ference between ^ 3 and ^ 5[.000][.001][.001][.001]
Firm-speci…c Fixed E¤ects
Yes
Yes
Yes
Yes
Year-speci…c Fixed E¤ects
Yes
Yes
Yes
Yes
Pure Domestic Firms Dropped
No
Yes
No
Yes
Pure Exporting Firms Dropped
No
No
Yes
Yes
Pure Processing Firms Dropped
No
No
No
Yes
Observations
87,81735,56527,08325,353
Note: T-values in parentheses. * (**) indicates signi…cance at the 10% (5%) level. All columns use top and bottom 25 thquantiles of …rm’s overhead-pro…t ratio as cuto¤s to de…ne the low(high) overhead indicators in each industry. Column (1) is
negative binomial estimate. Columns (2)-(4) are IV Poisson estimates in which the endogenous variable is home tari¤ with initial
year weight and the instrument is home previous year’s tari¤s with initial year weight.

35

Figure 1: Distribution of Chinese Firms’Export Product Scope

Appendix A: Matching Transaction-Level Trade Data and Firm-Level Production Data
A.1. Transaction-Level Trade Dataset
The extremely disaggregated transaction-level monthly trade data during 2000-2006 are obtained from
China’s General Administration of Custom. Each transaction is described at the HS 8-digit level. The numberof monthly observations increases from around 78 thousand in January 2000 to more than 230 thousand in
December 2006. As shown in Column (1) of Table A1, the annual number of observations is more than 10
million in 2000 and 16 million in 2006, with more than 118 million in total for seven years. Column (2) of Table
A1 exhibits that there are 286,819 …rms that ever engage in international trade during this period.
For each transaction, the dataset consists of three types of information: (1) 5 variables on basic trade
information. They are value (in current US dollar), trade status (export or import), quantity, trade unit, value
per unit (i.e., value divided by quantity). (2) 6 variables on trade mode and pattern. These include country ofdestination for exports, country of origin for imports, routing (i.e., whether the product is shipped through anintermediate country/regime), customs regime (e.g., processing trade or ordinary trade), trade mode (i.e., by sea,by truck, by air, or by post), customs port (i.e., where the product departs or arrives). (3) Firms’information
associated with each transaction. In particular, it includes 7 variables such as …rm’s name, identi…cation number
set by the customs, city where the …rm is located, telephone, zip code, name of the manager/CEO, ownership
type of …rm (e.g., foreign a¢ liate, private, or state-own-enterprises).
A.2. Firm-Level Production Dataset
The …rm-level panel dataset covers around an average 230,000 manufacturing …rms per year in 2000-2006.
The number of …rms doubled from 162,885 in 2000 to 301,961 in 2006. The data are collected and maintainedby China’s National Bureau of Statistics in an annual survey of manufacturing enterprises. It contains all
information of three accounting sheets (i.e., Balance Sheet, Loss & Bene…t Sheet, and Cash Flow Sheet). On
average, the total value of industrial production covered in such a dataset accounts for around 95% of China’s
total industrial production in each year. In fact, the aggregated data on the industrial sector in China’s
Statistical Yearbook published by the Natural Bureau of Statistics (NBS) are compiled from this dataset. The
dataset includes more than 100 …nancial variables listed in the main accounting sheets of all these …rms. It
covers two types of manufacturing …rms: (1) all SOEs; (2) non-SOEs whose annual sales are more than …ve
million RMB. The number of …rms increased from more than 160 thousand in 2000 to 301 thousand in 2006. As
shown in column (3) of Table A1, the total number of …rms that ever appear in the dataset during 2000-2006
is 615,951.
36

The raw production dataset is still quite noisy given that many unquali…ed …rms are included, largely becauseof mis-reporting by some …rms. For example, information on some family-based …rms, which usually have noformal accounting system in place, is based on a unit of one RMB, whereas the o¢ cial requirement is a unit of1000 RMB. We hence …lter the raw production data as introduced in the text. Accordingly, the total numberof …rms covered in the dataset is reduced to 438,165, around 1/3 of …rms are dropped from the sample after
such a …lter process. As shown in column (4) of Table A1, such a …lter ratio is even higher in the initial years:
around 1/2 of …rms are dropped in 2000.
A.3. Matching Method
Although these two datasets have rich information on production and trade, it is challenging to matchthem. Although both datasets contain a variable of each …rm’s identi…cation number, their coding systems arecompletely di¤erent and share no common characteristics. For example, the lengths of the …rm’s ID variable
in transaction-level dataset are 10 digits whereas those in …rm-levels only have 9 digits. China’s customs
administration just constructs a complete coding system di¤erent from the one adopted by the National Bureauof Statistics.
To address this challenge, we take two approaches to match transaction-level trade data and …rm-levelproduction data. First, we match the two datasets by …rm’s name and year. That is, if a …rm from one dataset
has exactly the same Chinese name as a …rm in another dataset in a particular year, they must be the same
…rm. The year variable is necessary to use for an auxiliary identi…cation variable since some …rms could change
their name in di¤erent years and new comers could possibly take other …rms’original names. As a result, thenumber of matched …rms is 83,679 in total by using the raw production dataset, and reduced to 69,623 in total
by using the more accurate …ltered production dataset.
Second, we rely on two other common variables to further identify …rms, namely, zip code and the last sevendigits of a …rm’s phone number. The rationale is that …rms should have di¤erent and unique phone numbers
within a postal district. Although this method seems straightforward, subtle technical and practical di¢ culties
still remain. For example, the phone numbers in the product-level trade data include both area phone codes
and a hyphen, whereas those in the …rm-level production data do not. We use the last seven digits of the phone
number to serve a proxy for …rm identi…cation for two reasons. The …rst reason is that during 2000–2006, somelarge Chinese cities changed their phone number digits from seven to eight, which usually added one more digitat the beginning of the number. Therefore, sticking to the last seven digits of the number would not confuse the…rm’s identi…cation. The second reason is that in the original dataset, phone number is de…ned as a string ofcharacters with the phone zip code. However, it is inappropriate to de-string such characters to numerals sincea hyphen bar is used to connect the zip code and phone number. Using the last seven-digit substring solves thisproblem neatly.
A …rm could miss its name information in either trade or production dataset. Similarly, a …rm could lose
information on phone and/or zip code. To secure that our matched dataset can cover common …rms as many as
possible, we include the observations in the matched dataset if a …rm appears in either one of the two approaches
just described above. As a result, the number of matched …rms increases to 90,558 when the raw production
dataset is used, as shown in column (7) of Table A1. By way of comparison, such a matching performance is in
the same magnitude to (or even better than) other similar studies (See Yu (2013) for detailed discussions).

Appendix B: Proof of Proposition 1.
(i). Rewrite (8) and (14) as

2[ sl + (1

A

sl

A

sh

)sh + 1 + ]Ap
2 b(ml + k) = 0;p
2 b(mh + k) = 0;
[ s2l + (1)s2h ]

22t = 0:Di¤erentiating the three equations with respect to t and doing some manipulation yields
dsl
dsh
1
=
=
dt
dt
A+1+

> 0:

(ii). We now turn to foreign tari¤ cuts. Rewrite (10) and (15) aspAt el 2 bml = 0;pAt eh 2 bmh = 0;2[ el + (1)eh + 1 + ]A

t [

37

e2l + (1(A1)(A2))e2h ]

2

= 0:

(A3)

Di¤erentiating (A1) and (A2), respectively, with respect to t yields
deh
del
el eh
=
+
:
dt
dt
t

(A4)

del
h
Hence, dedt > dt because eh < el .
hDi¤erentiating (A3) with respect to t and using (A4) to substitute dedt , we obtain

Kl
del
=
< 0;
dt
2(1 + ) t + 2A
where Kl = 2 (el eh )A =t
2 (1 + )el[ e2l + (1)e2h ] < 0. Note that el > eh ; thus, Kl < 0 andhence the above inequality holds.
Similarly, we can also obtain
deh
Kh
=
;
dt
2(1 + ) t + 2A
where Kh =2 (eh2 (1 + )eh[ e2l + (1
p
4 bmh
(el
Kh = (el eh )t)e2h ]. Using (A2) to substitute A , we can simplifyel )A =teh )

2 (1 + )ehe2h :From (A1) and (A2), we also obtainelp p
2 b( mh
eh =
t

p

ml )

:

Substituting the above in Kh to reduce toKh =4 b(mh ml )t 2

2 (1 + )eh

e2h :

@eh
h
< 0, it is clear that Kh > 0 for su¢ ciently large mh , and so under the same condition, de
> 0.
As @m
dt
h
Q.E.D.

Appendix C: Measuring Ex-ante TFP (TFP2)
This section discusses how we construct and measure TFP using two di¤erent approaches: ex-post TFP(TFP1) and ex-ante TFP (TFP2) inspired by Feenstra et al. (2013).
We extend the Olley–Pakes (1996) approach to …t with China’s reality in the following ways. Firstly, giventhat the measure of TFP requires real terms of …rm’s inputs (labor and capital) and output, we adopt di¤erent
price de‡ators for inputs and outputs from Brandt et al. (2012) in which the output de‡ators are constructedusing "reference price" information from China’s Statistical Yearbooks whereas input de‡ators are constructedbased on output de‡ators and China’s national input-output table (2002).
Secondly, we take China’s WTO accession in 2001 into account since such a positive demand shock would
push Chinese …rms to expand their economic scales, which in turn can exaggerate the simultaneous bias of theirmeasured TFP. Thirdly, it is essential to construct the real investment variable when using the Olley-Pakes(1996) approach. As usual, we adopt the perpetual inventory method to investigate the law of motion for realcapital and real investment. Di¤erent from assigning an arbitrary number for the depreciation ratio, we use theexact …rm’s real depreciation provided by the Chinese …rm-level data set.
Finally, we also consider …rm’s processing behavior in the TFP realization by constructing a processing
export indicator (one denotes processing export and zero otherwise). The idea is that processing …rms may use
di¤erent technology than non-processing …rms (Feenstra and Hanson, 2005).
Thus, a …rm’s investment function is Vit = g1 (xit ; ln Kit ; EXit ; P Eit ; W T Ot ) where EXit (P Eit ) is theexport (processing export) indicator to measure whether …rm i exports (engages in processing exports) in year38

t, and W T Ot is an indicator that equals one if the WTO agreement has occurred after 2001 and zero before
that. Therefore, inverting the investment function with respect to its …rst argument we obtain:25
xit = g1 1 (Vit ; ln Kit ; EXit ; P Eit ; W T Ot ):(22)

Given the gross production function
ln Yit =k ln Kit +l ln Lit +m ln Mit + xit + "it(23)

and de…ning the function g2 ( ) as k ln Kjt + g1 1 (Vit ; ln Kit ; EXit ; P Eit ; W T Ot ), the estimation of the labor(materials) coe¢ cients l ( m ) are obtained as:ln Yit =l ln Lit +m ln Mit + g2 (Vit ; ln Kit ; EXit ; P Eit ; W T Ot ) + "it :The next step is to obtain an unbiased estimated coe¢ cient ofspeci…cation:ln Yit^ l ln Lit^ m ln Mit =

k.

(24)

Olley-Pakes (1996) use the following

k ln Kit + E(xit jxit 1 ; prit ) + [xitE(xit jxit 1 ; prit )] + "it ;(25)

where the estimated values of the labor coe¢ cient and materials coe¢ cient are used on the left. The expectationof productivity appearing in (25) is modeled as a forth-order polynomial function of lagged productivity, which
can be obtained as (g2i;t 1
k ln Ki;t 1 ), and also the predicted probability of the …rm’s survival into the yeart, prit ; based on year t 1 information. The predicted probability is obtained from Probit estimation.26 The
term [xit E(xit jxit 1 ; prit )] is the productivity shock for surviving …rms, but does not a¤ect the investment
or exit choice so it is treated as an error.
Once the coe¢ cient of capital ^ k is estimated in Eq. (25), it is ready to obtain the standard ex-post TFP:T F P 1itxit = ln Yit^ k ln Kit^ l ln Lit^ m ln Mit :In this way, TFP1 includes both true production productivity and managerial e¢ ciency. By contrast, theex-ante productivity (TFP2) which only capture true production productivity is given by
T F P 2it = g1 1 (Vit ; ln Kit ; EXit ; P Eit ; W T Ot ):2 5 Olley and Pakes (1996) show that the investment demand function is monotonically increasing in the productivity shock x ,jt
by making some mild assumptions about the …rm’s production technology.
2 6 Note that here the non-linear least squares approach is adopted to estimate (25) since it requires the estimated coe¢ cients of
the log-capital in the …rst and second term to be identical (Pavcnik, 2002).

39

Year
# of

Trade Data
Transactions

2000
2001
2002
2003
2004
2005
2006
All Year(1)
10,586,696
12,667,685
14,032,675
18,069,404
21,402,355
24,889,639
26,685,377
128,333,831

Table A1: Matched Statistics
Production Data
Matched Data
Firms
Raw
Filtered w/ Raw w/ Filtered
Firms
Firms
Firms
Firms
(2)
(3)
(4)
(5)
(6)
80,232
162,883 83,628
18,58012,84287,404
169,031 100,100 21,583
15,64595,579181,557 110,530 24,696
18,140113,147 196,222 129,508 28,898
21,837
134,895 277,004 199,927 44,338
35,007
136,604 271,835 198,302 44,387
34,958
197,806 301,960 224,854 53,748
42,833286819 615,951 438,165 83,679
69,623w/ Raw
Firms(7)
21,66525,282
29,144
34,38650,79850,426
59,13390,558w/ Filtered
Firms
(8)
15,74819,091
22,291
26,93040,711
40,38747,591
76,946

Notes: Column (1) reports the number of observations of HS eight-digit monthly transaction-level trade data from China’s
General Administration of Customs by year. Column (2) reports the number of …rms covered in the transaction-level trade data by
year. Column (3) reports the number of …rms covered in the …rm-level production dataset compiled by China’s National Bureau of
Statistics without any …lter and cleaning. By contrast, column (4) presents the number of …rms covered in the …rm-level production
dataset with careful …lter according to the requirement of GAAP. Accordingly, column (5) reports the number of matched …rms
using exactly identical company’s names in both trade dataset and raw production dataset. By contrast, column (6) reports thenumber of matched …rms using exactly identical company’s names in both trade dataset and …ltered production dataset. Finally,column (7) reports number of matched …rms using exactly identical company’s name, zip code and phone number in both thetrade dataset and the raw production dataset. By contrast, column (8) reports number of matched …rms using exactly identicalcompany’s name, zip code and phone number in both trade dataset and …ltered production dataset.

40

Table A2: Production Productivity and Overhead Expenses
Industry Labor Capital Materials TFP1 TFP2
Log ofOverheadOverhead Pro…t Ratio
13
0.077
0.060
0.814
1.191 1.231
7.018
3.165
14
0.055
0.071
0.857
0.799 0.838
7.436
3.497
15
0.094
0.113
0.799
0.817 0.830
8.134
1.453
16
0.020
0.270
0.783
0.287 0.185
9.929
1.100
17
0.066
0.044
0.868
0.802 0.857
7.458
13.153
18
0.110
0.039
0.798
1.344 1.403
7.154
6.357
19
0.084
0.041
0.857
0.872 0.902
7.195
5.173
20
0.099
0.071
0.841
0.686 0.717
6.793
5.633
21
0.103
0.055
0.814
1.113 1.124
7.179
2.323
22
0.063
0.053
0.867
0.781 0.792
8.159
6.236
23
0.065
0.068
0.815
1.199 1.290
7.735
4.484
24
0.091
0.039
0.823
1.181 1.219
7.262
4.000
25
0.014
0.069
0.865
0.663 0.642
9.101
3.205
26
0.063
0.058
0.820
1.187 1.218
8.091
7.118
27
0.062
0.064
0.790
1.555 1.643
8.440
5.049
28
0.040
0.060
0.889
0.517 0.584
8.239
9.786
29
0.087
0.081
0.769
1.404 1.469
7.702
9.464
30
0.069
0.046
0.836
1.094 1.155
7.381
3.605
31
0.046
0.059
0.844
1.054 1.129
7.439
6.945
32
0.061
0.029
0.891
0.682 0.766
8.727
6.167
33
0.080
0.079
0.850
0.451 0.497
8.198
3.143
34
0.062
0.037
0.841
1.150 1.147
7.402
9.275
35
0.061
0.055
0.837
1.086 1.176
7.768
16.228
36
0.053
0.049
0.841
1.152 1.191
8.082
22.114
37
0.063
0.045
0.835
1.290 1.394
8.274
5.947
39
0.077
0.066
0.836
0.900 0.913
7.865
10.409
40
0.109
0.075
0.806
1.175 1.243
8.136
5.269
41
0.049
0.054
0.806
1.639 1.703
8.041
14.045
42
0.091
0.039
0.857
0.834 0.839
7.044
4.713
Notes: The Chinese industries and associated codes are classi…ed as follows: Processing of foods (13), Manufacture of foods(14), Beverages (15), Textile (17), Apparel (18), Leather (19), Timber (20), Furniture (21), Paper (22), Printing(23), Articles for
cultures and sports (24), Petroleum (25), Raw Chemicals (26), Medicines (27), Chemical Fibers (28), Rubber (29), Plastics (30),Non-metallic Mineral (31), Smelting of ferrous metals (32), Smelting of non-ferrous metals (33), Metal (34), General machinery(35), Special machinery (36), Transport equipment (37), Electrical machinery (39), Communication equipment (40), Measuring
instrument (41), and Manufacture of artwork (42). We do not report standard errors for each coe¢ cient to save space thoughavailable upon request. The logarithm of …rm productivity for Chinese …rms (TFP1 and TFP2) is estimated by industry using theaugmented Olley-Pakes approach introduced in the text. Coe¢ cients of labor, capital, and materials are calculated at the sectoral
average whereas TFP1 and TFP2 is measured at …rm-level using …rm-level output, capital, labor, and materials, respectively. The
last two columns report log of …rm’s overhead expenses and overhead-pro…t ratio.

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