HIAS-E-82

Product Dynamics and Trade Liberalization:
Evidence from the Korea-US FTA
Jung Hur(a),(b) and Haeyeon Yoon(a)(a) Department of Economics, Sogang University(b) Hitotsubashi Insititute for Advanced Study

December, 2018

Hitotsubashi Institute for Advanced Study, Hitotsubashi University
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Product dynamics and Trade liberalization: Evidence from the Korea-US FTA*

Jung Hur†

Haeyeon Yoon‡

Abstract
This study examines resource reallocation within firms by investigating how firms change theirproduct portfolios in response to a fall in trade costs. Using Korean firm-product data, we showthat firms experiencing large tariff reductions under the Korea-US free trade agreement are
more likely to shrink their product scope. They not only decrease the number of products but
also specialize their production in specific products. Furthermore, we show that those specificproducts are relatively more productive than others within firm by estimating a firm-productefficiency. Dropped and added products tend to be less efficient than incumbent products.
However, given the sharp increase in added products’ efficiencies after they enter, the above
result may not indicate the low efficiency of new products, but rather the time needed to become
organized and profitable. After considering the production distributions of incumbent products
within firms, this study finds that firms tend to increase the proportion of efficient products in
response to tariff reductions. In other words, firms allocate resources from less efficient
products to more efficient ones.

Key words: Multi-product firms, Product mix, Product scope, Tariffs, KORUS FTA
JEL classification: F10, F15, L11, L25

We thank Valerie Smeets, Chang Pao-Li, Jota Ishikawa, Eiichi Tomiura and seminar participants at theSingapore Management University and Hitotsubashi University. We also appreciate Statistics Korea and the Korea
Statistics Promotion Institute for allowing us to access the plant-level database in a secured data center. All the
results have been reviewed to ensure that no confidential information is disclosed. All errors are ours.
† Department of Economics, Sogang University and Hitotsubashi Institute for Advanced Study, Hitotsubashi
University, E-mail: ecsjhur@sogang.ac.kr‡ Department of Economics, Sogang University, E-mail: hyyoon@sogang.ac.kr*

1

1. Introduction
Resource allocation is an important issue in economics, given the need to achieve economic
growth under the constraint of exhaustible resources. Although product churning within firmtakes place frequently and explains a large part of economic growth (Bernard et al., 2010;
Goldberg et al., 2010), most studies focus on firm turnover within industry. However,considering that multi-product firms account for the majority of output in the Korean economy4,
this study examines product churning within firm rather than firm turnover within industry.
Because of changes in product prices, marginal costs, and market competition due to shocks intrade costs, several researchers have studied how firms change their product scope when trade
costs fall (Baldwin and Gu, 2009; Bernard et al., 2011; Berthou and Fontagné, 2013; Iacovone
and Javorcik, 2010). However, to examine which products are switched and expanded in firms(i.e., qualitative changes in the product mix) is important to understand the resource
reallocation within firm as well as to investigate quantitative changes in product scope.
Nevertheless, few studies examine the characteristics of dropped products by utilizing productsales as a proxy for product competence (Bernard et al., 2010; Liu, 2010; Ma et al., 2014).
Furthermore, previous studies are silent about which products are added, grow, and shrink,
although product churning includes all four activities: dropping and adding products as well as
increasing and decreasing the production of incumbent products. Instead of the proxy variable,
we estimate firm-product efficiency and explore whether firms increase the production of more
efficient products by considering all churning activities. This study thus contributes to theliterature by examining not only the changes in firms’ product scope in response to the
reduction in trade costs but also the relation between product churning and firm-product

Although multi-product firms account for only 16% of Korean manufacturing firms, they are responsible formore than 70% of total output.

42

efficiency, a topic overlooked in previous studies.
To deepen our understanding of product churning patterns, this study investigates how
Korean manufacturing firms react to a reduction in trade costs. In relation to the Korea-US
Free Trade Agreement (KORUS FTA) of 20125, a large change occurred in the Korean tradeenvironment that should have affected firms’ resource reallocation. Given that the United States
was Korea’s second largest export market and third largest import market in 2011, the effectsof KORUS FTA on Korean firms’ product portfolios are expected to be larger than those ofother FTAs in Korea 6 . Figure 1 shows the trend in average US tariff rates on Korean
manufacturing imports (solid line) and the most-favored-nation tariff rate (dashed line) in
2008–2016. In 2011, the average US tariff rate for Korea was 3.5%. Under KORUS FTA, thetariff rate sharply decreased by 90% from 2011 to 2016, falling to 0.36%. In addition to thetariff reductions, both countries also eliminated any administrative fees for trade7.
KORUS FTA is not only limited to the trade of goods; it also creates new market accessto investment, telecommunications, express delivery, and legal consulting services 8 .
Furthermore, given that both China and Japan, which are the major competitors in exports to
the United States, have not signed any FTAs with the United States, the effects of KORUS FTA

KORUS FTA took effect on March 15, 2012.
Following the FTA with Chile in 2004, Korea has actively agreed FTAs with several trade partners. By June
2018, Korea had signed 16 FTAs: Chile in 2004, Singapore in 2006, Ireland, Liechtenstein, Norway, andSwitzerland in 2007, ASEAN (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines,
Singapore, Thailand, and Vietnam) in 2007, India in 2010, Peru in 2011, the United States in 2012, Turkey in2013, Australia in 2014, Canada in 2015, China in 2015, New Zealand in 2015, Vietnam in 2015, the EU (Austria,
Belgium, Bulgaria, Croatia, Cyprus, Czech, Denmark, Estonia, Finland, France, Germany, Greece, Hungary,
Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Spain, Slovakia,
Slovenia, Sweden, and the United Kingdom) in 2015, and Colombia in 2016. Although Korea contracted an FTA
in 2015 with China, the largest exporting and importing market for Korea, because of the lack of data after 2017,
we do not examine the effects of the China-Korea FTA on firms’ product churning in this study.
7 Article 2.10 describes this as follows: “Neither Party require consular transactions, including related fees andcharges… Neither Party may adopt or maintain a merchandise processing fee on originating goods.”
8 According to Article 11.3, the United States treats investments from Korea the same as those from in-state
investors.
3
5
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on Korean firms seem to be large.
To assess how Korea’s exports change in this period, we examine the export amount andnumber of exported products in 2011 and 2016 using data from the International Trade Centre.
Table 1 reports the exports to Korea’s top three export destinations in 2011 and to the world.
Korea’s exports to the United States show the highest increases in both the export amount andthe number of exported products, highlighting the considerable effects of KORUS FTA on
Korea’s exports. Given the importance of the United States to Korean trade and substantial
reduction in trade costs between the two countries in 2012, we therefore examine how Korean
firms reassigned their product portfolios between 2011 and 20169.
Before investigating the qualitative changes within firms, we examine whether firms
increase or decrease their product scope. Using Korean firm-product-level data, we investigatechanges in the number of produced products and distribution of product sales within firms.
After examining the quantitative changes in product portfolios, we examine the relation
between product churning and firm-product efficiency. While previous studies explore therelation between the probability of dropping a product and that product’s size (Bernard et al.,2010; Tan et al., 2015), we utilize firm-product efficiency by estimating the single- and multiproduct production functions of Foster et al. (2008) and Dhyne et al. (2017), respectively.
Furthermore, we contribute to the literature by examining the characteristics of not only the
dropped product but also the added, growing, and shrinking products to consider the wholeprocess of product churning. Hence, with these estimated firm-product efficiencies, weinvestigate whether less efficient products are more likely to be switched and whether more
efficient products show higher growth within firms. This study thus contributes to the literature

The largest decline occurred in 2012 when KORUS FTA came into effect. However, since it takes time toreallocate products, we investigate changes in the product mix between 2011 and 2016.

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by investigating the mechanism of product churning within firms in response to tariff
reductions.
The results show that firms that experience larger tariff reductions are more likely toshrink their product scope by decreasing the number of produced products and focusing theirproduction on specific products. The estimation results on firm-product efficiencies shed lighton how firms reassign their inputs in response to falling trade costs. The results report thatfirms tend to drop and add relatively less efficient products. We additionally trace theefficiencies of added products and find a sharp increase in their efficiencies after they start tobe produced. In other words, the former results show that it takes time for new products tobecome organized and profitable. Therefore, the low level of added products’ efficiencies when
they enter does not indicate that firms become less productive by adding new products. Among
incumbent products, the sales of more efficient products and their share within firms rise when
trade costs fall. Considering the churning in both switched and incumbent products, we findthat firms are more likely to increase their output of efficient products as trade costs decrease.
This finding indicates that firms tend to decrease their product scope in response to tariff
reductions by shrinking and dropping less efficient products and reallocate resources fromthose products to more efficient ones.
The remainder of this paper is organized as follows. Section 2 reviews the related literature.
Section 3 describes the data and summary statistics of the Korean firm-product data. Section 4
investigates the estimation of changes in firms’ product scope and the relationship between
firm-product efficiency and product churning in response to a reduction in trade costs. Section
5 concludes.

2. Literature
5

The literature on product churning has expanded with the availability of firm-product-level
data as well as the growing importance of multi-product firms and product churning ineconomies. According to Bernard et al. (2010), 39% of US manufacturing firms produce morethan two products and those firms account for 87% of manufacturing output. The prevalence
and importance of multi-product firms have also been shown in developing countries such as
India10 (Goldberg et al., 2010). Given that more than half of all manufacturing firms add or
drop products every five years (Bernard et al., 2010; Goldberg et al., 2010), active productchurning is not limited to multi-product firms; single-product firms switch their product mix
as well.
Given the effects of changes in trade costs on a product’s prices, costs, and market
competition, several studies have examined the response of firms to trade cost shocks. Most
theoretical studies predict that firms decrease their product scope when trade costs fall (Bernard
et al., 2011; Eckel and Neary, 2010; Mayer et al., 2011). Several empirical studies find evidence
that supports the arguments from these models. Bernard et al. (2011) show that US
manufacturing firms that experience above-median tariff reductions through the Canada-US
FTA decrease their product scope relative to firms experiencing below-median reductions. The
shrinkage of the product scope is also shown in small Canadian firms under the bilateral tariff
reduction between Canada and the United States (Baldwin and Gu, 2009)11.
Studies of product churning examine how firms reallocate their resources when trade costschange. Several theoretical studies investigate the mechanism of firms’ product churning
decisions. Most predict that firms specialize in their most competent products in response to

Indian multi-product firms account for 47% of manufacturing firms and 80% of manufacturing output.
However, since the reduction in trade costs increases both market competition in domestic countries and market
access to foreign countries, the effects on firms’ product scope differ according to a firm’s productivity (Dhingra,
2013; Qiu and Zhou, 2013), a firm’s export activity (Lopresti, 2016; Nocke and Yeaple, 2014), and the destinationof products (Berthou and Fontagné, 2013; Iacovone and Javorcik, 2010).
6

10
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falling trade costs and increased competition in the domestic market (Bernard et al., 2011;
Eckel and Neary, 2010; Mayer et al., 2011). Theoretical studies characterize production costs
within firms by defining a core product as one that has the lowest marginal cost. According to
the models, a firm’s product scope expands from the core product, since the farther is the
distance from the core product, the higher is the marginal cost. Therefore, as domestic market
competition increases, firms are more likely to drop peripheral products that have the highest
marginal cost. Several empirical studies find a tendency to skew production to the core productwithin firms and a higher exit rate for peripheral products (Bernard et al., 2010; Liu, 2010; Ma
et al., 2014). As a proxy variable for a product’s competence, they utilize relative product sales
by assuming that the core product accounts for the largest proportion of sales within firms.
However, the size does not precisely capture products’ competence within firms, since low
marginal costs in theoretical studies indicate high efficiencies as opposed to relatively large
sales. Although products’ size and efficiency may be positively related, researchers cannot
identify whether a large size is driven by the high productivity or the large quantity of inputs.
In other words, the relative size is a vague variable in that it cannot separate products that have
low efficiencies and need many input resources from those having high efficiencies that needsmall input resources to produce one unit of the product. Investigating the efficiencies ofchurned products, thus, sheds light on how firms reallocate their resources and reorganize theirproduct portfolios.
Different from previous research, this study measures firm-product efficiency andinvestigates whether the production volume of less efficient products shrunk when the UnitedStates decreased tariff rates on Korean imports. We measure firm-product efficiency using two
methodologies: a single-product production function and a multi-product production function.
For the estimation, firm-product-level input variables are needed but only firm-level input data
are available. To capture the firm-product-level inputs, Foster et al. (2008) allocate firms’ inputs
7

to each product as a proportion of the product’s revenue share and estimate the single-productproduction function. Instead of measuring the inputs for each product as a proportion of theirsales share, Dhyne et al. (2017) control for other product sales within the same firms in a
production equation and estimate the multi-product production function based on the models
of Diewert (1973) and Lau (1976). Different from the single-product production function, themulti-product production function does not directly restrict the input information but considers
the production of other products within the same firm. In the empirical estimation, we estimate
both production functions and utilize two estimated firm-product efficiencies. With theseestimated firm-product efficiencies, we then investigate whether dropped or added products
have lower efficiency than incumbent products within the same firms. Furthermore, we
examine whether efficient products grow or shrink under firms’ product churning.

3. Data
To investigate product churning within firm, we utilize Korean plant-product data , the Mining
and Manufacturing Survey (MMS) from Statistics Korea for 2008–2016 except for 2010 and201512. Statistics Korea annually surveys plants in Korean mining and manufacturing sectors
with at least 10 workers. The MMS includes information on a plant’s inputs, outputs, and firm
identification number (ID). The output information contains a plant’s total sales, a set of
produced products, and the sales of each product. To control for a plant’s global activity13, we
additionally utilize the plant’s export data from the Korea Customs Service, because theinformation on the plant’s export activity is not included in the MMS14. Although we utilize

Statistics Korea did not publish the MMS in 2010 and 2015 when the Economic Census was carried out.
Globalized firms tend to expand their product scope in response to changes in trade costs (Lopresti, 2016;
Nocke and Yeaple, 2014).
14 By combining the two data sets by matching the plant’s ID, we can identify an exporting plant and control forthat characteristic.
8
12
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plant data, decisions about product portfolios and resource reallocation tend to be made at thefirm level rather than the plant level. Therefore, we aggregate the plant information to the firmlevel by using the ID of the firms to which the plants belong.
Before introducing the empirical model, we briefly examine the characteristics of multiproduct firms and product churning in Korea. We define a product based on the eight-digit
KSIC (Korea Standard Industry Classification) categories. Four-digit and two-digit KSIC
categories are used to define industry and sectors, respectively. For example, an industry of
Handbags, Luggage, and Other Protective Cases (KSIC 1512) consists of eight productcategories: Handbag (KSIC 15121101), Purse (KSIC 15121102), Briefcase (KSIC 15129101),
Suitcase (KSIC 15129102), School bag (KSIC 15129103), Hiking bag (KSIC 15129104),
Other bag (KSIC 15129109), and Case (KSIC 15129200). According to these definitions,Korean manufacturing consists of 24 sectors, 180 industries, and 2,139 products15. We define
a firm that produces more than two products as a multi-product firm.
By employing the abovementioned definitions, we examine the prevalence of multiproduct, -industry, and -sector firms in 2008–2016 in Table 2. Multi-product firms account for
only 16% of Korean manufacturing firms but produce 71% of manufacturing output. Hence,
although the number of multi-product firms is small, they represent a large proportion of the
Korean economy. The last column of Table 2 indicates that multi-product firms produce 2.66
products on average. Multi-industry and multi-sector firms account for only 10% and 6% offirms, but explain 59% and 49% of manufacturing output, respectively.
To see how firms organize their product mix, we examine the distribution of product sales

In Bernard et al. (2010), US manufacturing is composed of 20 sectors, 455 industries, and 1,440 products
according to SIC (Standard Industry Classification) categorization. They use the first five digits of the SIC todefine a product.
915

within firms. In the last column of Table 3, we merge all firms producing more than 10 products
into one group. The distribution of sales across products is skewed towards a firm’s coreproduct (i.e., the highest ranked product). The largest product accounts for more than 70% ofsales for firms producing two products and that share decreases gradually to 40% for firms withnine products. The distribution of products except for the highest ranked one is relatively
constant across the number of products produced by firms.
Table 4 shows the activeness of Korean manufacturing firms’ product switching between
2011 and 2016 by separating firms into four groups. The sample is restricted to firms thatsurvived in both 2011 and 2016. The first group, None, includes firms which neither add nor
drop any products between 2011 and 2016. The Drop only and Add only groups contain firmsthat only drop or only add products, respectively. Lastly, firms that both add and drop products
are classified into the Both group. In other words, the firms not included in the None group
switch their product mix in these periods. Table 4 describes firms’ product switching activity
in the first row and the switching activity weighted by firms’ output share in the second row.
On average, 39% of Korean manufacturing firms switch their product portfolios between 2011
and 2016. Among all firms, 4.8% only drop products, 6.7% only add new products, and 27.5%
both drop and add products during the study period. When we employ weights using the output
share in the second row, the share of firms that switch their products, 77.6%, becomes muchlarger than that before weighted. The results in the second row indicate that firms with large
output shares change their sets of products more actively than those with small output shares.
For the tariff data, we utilize the US tariff rates on Korean imports obtained from the US
International Trade Commission. These tariff rates are reported using the eight-digit
Harmonized Tariff Schedule of the United States (HTS-US) code. We aggregate these eightdigit tariff rates at the six-digit level using eight-digit US imports from Korea in 2011. For
10example, trunks, suitcases, vanity & attaché cases, occupational luggage, and similar containers(HTS 420212) consist of trunks, suitcases, vanity & attaché cases, occupational luggage, andsimilar containers with an outer surface of plastic (HTS 42021220), cotton (HTS 42021240),
fibers (HTS 42021260), and textiles (HTS 42021280). The tariff rates on those eight-digit-levelproducts in 2011 are 20%, 6.3%, 5.7%, and 17.6%, respectively. US imports for the four
products from Korea in 2011 are valued at USD 62,000, 5,000, 10,000, and 105,000,
respectively. Thus, the weighted average tariff rate for trunks, suitcases, vanity & attaché cases,!"

occupational luggage, and similar containers (HTS 420212) in 2011 is about 17.5% (= 20%*#$"
%

#&

#&%

+ 6.3%*#$" + 5.7%*#$" + 17.6%*#$"). Since the tariff rate from the US International Trade
Commission and product data in the MMS are reported using two different classification codes(i.e., HTS and KSIC), we match these two classifications at the most disaggregated level,
namely the five-digit KSIC level16.
Using the product-level tariff rate data, we measure the change in the firm-specific tariff
rate as the sales-weighted tariff change placed on firm i between 2011 and 2016:"&##(1) ∆()*+,,- = ∑3 0ℎ)*2-3∗ ∆()*+,,3"&##
where sℎ)*2-3
is the share of the sales of goods g (at the five-digit level) by firm i in 2011.

∆()*+,,3 (=()*+,,3"&#! − ()*+,,3"&## ) represents the US tariff rate changes on Korean
manufacturing imports for goods g between 2011 and 2016. Since the goods produced by firmi do not experience the same tariff changes and they have different degrees of importance within
the firm, the weighted average tariff reductions capture the difference in changes well. On
average, firms experience a -2.14 percentage point tariff rate change between 2011 and 2016

Since the first six digits of the HTS code take the same form as those of the Harmonized System (HS) code,
we match the six-digit HS code to the five-digit KSIC code.
1116

with a standard deviation of 2.80 percentage points. The largest tariff rate reduction faced byfirms is -21.98 percentage points.

4. Empirical Evidence
4-1 Firms’ Product Scope
This section examines the changes in the number of manufactured products and productdiversification to assess how firms adjust their product scope in the period of KORUS FTA
application. The diversification variable is measured as one minus the sum of the squared
product sales share within firms17 (Lopresti, 2016). This index captures not only the numberof produced products but also the distribution of product sales in the product mix. In otherwords, the diversification index increases when a firm diversifies its product mix by raising thenumber of products or widening the distribution of product sales. Equation (2) reports theestimation model for the changes in firms’ product scope in response to tariff reductions:(2) 89- = )& + )# ∆()*+,,- + )" ;<((>?- ) + )@ ;<(A- ) + )B ;<(CD2- ) + )% EFGH*I- + )! JK;I+ −G*HLKMI- + )N ∆O?P + 2-

The dependent variable, 89Q , is the change in the number of eight-digit-level products
produced by firm i (or the change in the product diversification of firm i) between 2011 and2016. If firm i reduces (increases) the number of products, then the dependent variable takes anegative (positive) value. ∆()*+,,- is the change in firm i’s specific tariff rate between 2011
and 2016. (>?- is firm i’s value-added total factor productivity (TFP) measured by the Cobb–

17

WXYZ[\]^

R+S2*0+,+M)I+H<-T = 1 − ∑cde# V∑_\`a WXYZ[\]^"b where i is the firm, t is the year, and p (p=1,…, P) is the eight-

digit-level product produced by firm i.
12Douglas production function with a two-thirds labor share in 2011. A- is the number ofpermanent workers and CD2- is the age of firm i in 2011. EFGH*I- and JK;I+ − G*HLKMIare dummy variables that take the value of 1 if firm i exports and produces more than two
products in 2011, respectively; otherwise, they are 0. ∆O?P is the change in import penetration
between 2011 and 2016 in the four-digit-level industry j to which firm i belongs18.
Table 5 presents the summary statistics of the explanatory variables. On average, firms
experience -2.137 percentage point changes in the firm-specific tariff rate. The average numberof permanent workers is 60 and mean value of firms’ age is 15 years. About 35% of firms inthe sample export and 19% manufacture more than two products in 2011. The mean change in
the import penetration variable has a small negative value, implying that, on average, theincrease in import penetration is not severe.
Column (1) of Table 6 shows how firms change the number of their products in response
to tariff reductions. The coefficient of the firm-specific tariff change variable is positive andstatistically significant. This finding indicates that firms facing large tariff reductions are more
likely to reduce the number of manufactured products. Column (2) reports the change inproduct diversification upon tariff reduction. The tariff change variable has a positive andsignificant coefficient as well. The result shows that firms experiencing large tariff reductions
tend to specialize their production in specific products. Considering that the reduction in trade
costs increases market competition in the home country, we can infer that firms focus on theircompetent products and drop less competent products. In the next section, we check whether
firms drop less efficient products and specialize in more efficient products by estimating firm-

18

O?P = (

fg

fg hig), where JP is the total value of Korean imports in the four-digit-level industry j and jP is

total plant sales in industry j.
13

product efficiency.
Table 6 shows that firms facing large tariff reductions are more likely to shrink theirproduct scope by decreasing the number of products and focusing their production on specificproducts 19 . Since we utilize a sample of surviving firms between 2011 and 2016, we also
conduct an additional estimation including exiting firms. The results are similar to those in
Table 620. For the results of the other variables in Table 6, the coefficients of firms’ generalcharacteristics show that large or young firms are more likely to expand their product scope.
The coefficients of the TFP and export dummy variables do not have significant coefficients.
The coefficients of the multi-product firm variables indicate that such firms tend to decrease
their product scope relative to single-product firms 21 . Lastly, the changes in the importpenetration variable do not significantly affect firms’ product scope.
While Table 6 shows that Korean firms experiencing larger tariff reductions under
KORUS FTA are more likely to narrow their range of production, one concern about examining
the change in firms’ product scope remains: if this phenomenon was shown before KORUS
FTA, it would be unrelated to changes in trade costs. To handle this issue, we check the placebo
effect by regressing the changes in product scope between 2008 and 2011 on the tariff changes
between 2011 and 2016 (see Table 7). The coefficients of the tariff changes variable are notstatistically significant, showing no pre-trends in firms’ product scope. In the next section, we

Since tariff reductions induce heterogeneous effects on product scope by increasing both market competitionin the domestic country and market expansion into foreign countries, we examine these different effects byinteracting the tariff change variables and firms’ TFP (Dhingra, 2013; Qiu and Zhou, 2013) or export dummy
variables (Lopresti, 2016; Nocke and Yeaple, 2014). However, the interaction terms do not have statisticallysignificant coefficients.
20 The estimation is limited to the change in the number of products because the diversification index cannot bemeasured with one period’s production information. The estimation result is available upon request.
21 If a single-product firm drops its product, it means that the firm exits the market. We check the estimation
results by dividing the sample into two groups: single- and multi-product firms. The coefficients of tariff changes
only reach statistical significance in multi-product firms.
19

14

examine which products are dropped and added and which products grow and shrink withinfirms by using the estimated firm-product efficiencies.

4-2 Relation between Product Churning and Firm-Product Efficiencies
In this section, we first examine whether less efficient products are more likely to be switchedand then assess whether the production of more efficient incumbent products grows withinfirms when trade costs fall. According to the model of Bernard et al. (2011), as trade costschange, firms reallocate their resources by dropping less efficient and adding more efficient
products. Empirical studies utilize product relative sales as a proxy variable for productefficiency by assuming that the most competent product accounts for the largest share of sales
within firms (Bernard et al., 2010; Liu, 2010; Ma et al., 2014). Since sales depend on not only
the efficiency but also the quantity of inputs, this study instead utilizes the firm-productefficiencies estimated from the two production functions.
Generally, productivity is defined as a residual that cannot be explained by the input
factors in the production function:(3) 9 = ,(k) ∙ 2 m
where 9 is output, ,(∙) is the production function, X is the input factors, and n is
productivity (which is unobservable). To measure firm-product efficiency, information on the
input factors at the firm-product level is needed. The key challenge to estimating firm-productefficiencies is the lack of input information on each product. Hence, how to capture firmproduct-level inputs is critical. One study measuring firm-product efficiency utilizes the
product’s output share to distribute the firm’s input factors at the firm-product level. Foster etal. (2008) allocate firms’ input to each product as a proportion of the product’s revenue share.
15

Following their method, we estimate the single-product production function below:d(4) ;< (pd- ) = q&d + qYd ;<(0ℎd- ∗ ;- ) + qrd ;<(0ℎd- ∗ s- ) + qt;<(0ℎd- ∗ u- ) + 2d-

where pd- is the output of product p in firm i; 0ℎd- represents the sales share of product p infirm i; ;- , s- , and u- are the labor, capital, and material inputs of firm i; 2d- is composed of
the specification error, <d- , and the efficiency shock, nd- . By interacting the firm-level inputs
with the share of product sales, we estimate the single-product production function and obtain
the residuals, firm-product efficiency (nd- ). Since the number of observations for an eightdigit-level product mix is insufficient for the estimation, we pool observations at the three-digit
level (83). In the empirical estimation, the dependent variable is product p’s sales (pd- ). To
manage the between-industry price differences, we divide product sales based on the two-digit
industry Output Price Indexes from the Bank of Korea. Although we control for cross-industry
price differences, revenue-based productivity may positively relate to prices and thus, demand(Foster et al., 2008; Syverson, 2011). However, since this study investigates the effects of achange in trade costs, which relates to the opening up of the market and demand shifts, we
utilize revenue-based productivity rather than quantity-based productivity 22 . As the input
factors, we use the number of full-time workers (;- ), tangible fixed assets (s- ), and material
inputs (u- ) of firm i. s- and u- are deflated with the two-digit-level sector deflator from the
Bank of Korea. Since the output information about dropped (added) products is only available
in 2011 (2016), the efficiency estimation of dropped (added) products utilizes the input andoutput information from the 2011 (2016) data.
However, allocating inputs using the product sales share could be a restrictive assumption

It would be better to check whether our results are robust with quantity-based productivity. However, our data
only report a product’s sales and not a product’s quantity or price.
16

22

since input intensity may differ across products. Hence, in the next estimation, we do not
restrict the inputs directly but rather control for the sales of other products within the samefirms. This method of measuring firm-product efficiency follows Dhyne et al. (2017) by
estimating the following multi-product production function:dd(5) ;< (pd- ) = v&d + vYd ;< (;- ) + vrd ;< (s- ) + vt;< (u- ) + vwd;< (pwd- ) + 2d-

This function differs from the single-product production function because it eliminates theinteracted product sales share and instead includes the sales of other products in the same firms.
In multi-product firms, since the sales of product p depend on not only firm i’s inputs but also
other products’ outputs, the estimation aggregates the sales of all other products in firm i
excluding those of product p, pwd- 23. By utilizing the multi-product production function, thisstudy estimates another firm-product efficiency value. We report the estimation results ofmulti-product production function in the Appendix since the signs of coefficient of the inputs
and the sales of other products have to be checked in the estimation of the multi-productproduction function24. Tables A1 and A2 describe the results of the top 10 three-digit goods
according to the number of firm-product observations in 2011 and 2016, respectively. All thecoefficients of the input factors are positive, whereas those of the sales of other products arenegative, because as the sales of other products increase, the proportion of input factors to
product p decreases. In short, Tables A1 and A2 show that all the coefficients have the correct
sign.
Using the estimated firm-product efficiencies, we investigate which products a firm drops

Since we use a product’s sales as the output variable, we divide sales by the output price index as well.
According to the models of Diewert (1973) and Lau (1976), the sales of product p are non-decreasing in the
input factors holding the sales of other products (-p) constant and non-increasing in the sales of other products (p) holding the input factors constant.
17

23
24

or adds between 2011 and 2016 by estimating the model in equation (6):(6) Rd- = M& + M# E,,+M+2<Mpd- + zP + {- + |dThe dependent variable, Rd- , takes the value of 1 if the eight-digit-level product p of firm i is
dropped (added) following sharp tariff reductions. E,,+M+2<Mpd- is the estimated firmproduct efficiency of product p in firm i in 2011 (2016). We use the two efficiency variables
estimated from the single- and multi-product production functions. Four-digit-level industry,zP , and firm, {- , fixed effects are employed to capture the unobservable effects of industry andfirm. We utilize standard errors clustered at the eight-digit product level.
Columns (1) and (2) of Table 8 show the efficiency of dropped products relative to that ofincumbent products. The negative coefficient of the firm-product efficiency variable estimatedby the single-product production function in column (1) indicates that a product with lower
efficiency has a higher probability of being dropped from firms’ product mix. The result incolumn (2) for the multi-product production function looks similar. Columns (1) and (2) thus
show that firms tend to drop less efficient products among their product portfolios in response
to falling trade costs. Columns (3) and (4) describe the relative efficiencies of added products.
Both the efficiency variables report significantly negative coefficients, meaning that added
products tend to have lower estimated efficiencies than incumbent products. However, the low
efficiencies of added products do not coincide with the prediction of Bernard et al. (2011).
When we consider the time needed for new products to become organized and profitable,columns (3) and (4) of Table 8 may not imply that added products are less efficient thanincumbent products. Thus, we trace the increase in the efficiency and output of added products.
In Table 9, we regress the changes in efficiencies, the log of product sales, and the sales sharebetween 2013 and 2016 on the added product dummy, which takes a value of 1 if a product is18

added between 2011 and 2013. The coefficients of this added product dummy variable incolumns (1) and (2) indicate that they show a higher increase in efficiencies than incumbent
products after they start to be produced. Columns (3) and (4) show that added products’ outputs
increase more sharply than those of incumbent products. Although added products seem tohave lower efficiency than incumbent products as shown in Table 8, the efficiency of added
products may be comparable with that of incumbent products when they are organized properly.
In other words, firms may not become less productive by adding new products.
Since resource reallocation also occurs among incumbent products, we next examine therelation between firm-product efficiencies and the change in the proportion of incumbent
products within firms (see columns (1)–(4) of Table 10). We regress the increase in products’
importance within firms between 2011 and 2016 on firm-product efficiencies in 2016. A
product’s importance is measured by the log of product sales and product sales share withinfirms. The coefficients of the efficiency variables indicate the growth of more efficient products
and shrinkage of less efficient products. Table 6 previously showed that firms facing large tariff
reductions tend to specialize their production in specific products. Columns (1) to (4) suggestthat those specific products may have high efficiencies relative to other products within firms.
In other words, firms reassign their incumbent products by increasing the share of efficient
products in response to tariff reductions.
Lastly, considering both switched and incumbent products, we investigate the relation
between product churning and product efficiencies within firms in columns (5)–(8) of Table
1025. For the dependent variables, we utilize the growth in product sales and sales share. The
growth variable is the ratio of the output changes between 2011 and 2016 to the mean of the

The efficiencies of dropped products are measured using 2011 data and those of added and incumbent products
are measured using 2016 data.
19

25

two periods’ values (Davis and Haltiwanger, 1992), since the switched products only have
output information from one period. Thus, the dropped and added products have the values of-2 and 2, respectively. The positive coefficients of the efficiency variables in columns (5)–(8)indicate that firms increase the proportion of more efficient products by reallocating theirresources from less efficient products when trade costs fall.

5. Conclusion
Following the conclusion of KORUS FTA, the United States decreased its tariff rate on Korean
manufacturing imports by 90% between 2011 and 2016. This study examines how firms adjust
their product portfolios in response to such a reduction in trade costs using Korean firm-productdata.
First, we find that firms experiencing large tariff reductions are more likely to narrow theirproduct scopes by reducing the number of manufactured products and specializing theirproduction in some products. After investigating the quantitative changes in firms’ product mix,
we examine the efficiencies of dropped, added, growing, and shrinking products. Ourestimation results show that dropped and added products are less efficient than incumbent
products. However, given the high increases in efficiency and output of added products after
they start to be produced relative to those of incumbent products, the former results suggestthat time is needed for new products to become organized and profitable. Among incumbent
products, more efficient products show higher increases in sales and in their sales share withinfirms. Considering both switched and incumbent products, we find that firms reallocate theirresources by increasing the sales and sales share of efficient products in response to tariff
reductions.

20This study contributes to the literature by examining the mechanism of product churning
within firms using estimated firm-product efficiencies and by considering all product churning
activities (i.e., dropping, adding, expanding, and shrinking products) in response to tariff
reductions. The results suggest that firms adjust their product mix as well as reallocate theirresources. In addition, the empirical results on firms’ responses to a reduction in trade costs
have policy-relevant contributions with regard to the rise in protectionism globally.
Strengthened protectionism may induce firms to restructure their product mix in the opposite
way from that when trade costs fall.

21

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Dunne, J. B. Jensen and M. J. Roberts (Chicago: University of Chicago Press).
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Bernard, A. B., S. J. Redding, and P. K. Schott (2011), “Multiproduct firms and tradeliberalization”, Quarterly Journal of Economics, 126, 1271-1318.
Berthou, A. and L. Fontagné (2013), “How do multiproduct exporters react to a change in trade
costs?”, Scandinavian Journal of Economics, 115 (2), 326-353.
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and Statistics, 92 (4), 1042-1049.
22

Iacovone, L. and B. Javorcik (2010), “Multi-product exporters: Product churning, uncertainty
and export discoveries”, Economics Journal, 120 (544), 481-499.
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23

2
0

1

US Tariff Rates

3

4

Figure 1. Trend in average US tariff rates, 2008–2016

2008

2010

2012
Year
Korea

2014

2016

MFN

Notes: Tariff rates are the average eight-digit (HTS)-level US tariff rates from the
US International Trade Commission. Accessed May 10, 2018.

24

Table 1. Amount and number of exported products from Korea
United
To
China
Japan
World
StatesAmount of exports (US dollars, million)
2011
2016
Growth56,421
66,757
18%

134,185
124,432-7%

39,67924,356-39%555,208
495,465
-11%

3,375
3,283
-3%

4,514
4,646
3%

Number of exported products (six-digit HS code)
2011
2016
Growth

3,047
3,318
9%

3,646
3,721
2%

Notes: Data from the International Trade Centre. Accessed October 19, 2018.

25

Table 2. Prevalence of multi-product, -industry, and -sector firms
Type of firm

Number ofobservations

Percentage
of firms

Percentage
of output

Multi-productMulti-industry
Multi-sector

55,86834,49821,536

16.4
10.2
6.3

71.0
59.1
49.7

Mean products,
industries, or sectors
per firm
2.66
2.31
2.15

Notes: 2008–2009, 2011–2014, and 2016 data are used. 438,101 manufacturing firms. A product(industry or sector) is defined as the eight-digit (four- or two-digit) KSIC category. A multi-product(industry or sector) firm is a firm with more than two products (industries or sectors) in a year.

26

Table 3. Mean distribution of the product sales share within firms
Number of products produced by a firm
2

3

4

8

9

1
100
71
59
52
49
45 44 45
2
29
27
25
24
23 22 203
14
14
14
14 13 12
4
8
8
9
9
9
5
5
6
6
6
6
3
4
4
7
2
3
8
1
9
10+
Observations 247,028 31,605 9,473 3,311 1,361 626 327 145

40
20
13
9
7
4
3
2
1

Average percentage of sales

1

5

6

7

10+

30
18
13
9
7
5
4
3
2
1
66 390Notes: 2008–2009, 2011–2014, and 2016 data are used. In the last column, all data are merged into
one group for firms that produce more than 10 products.

27

Table 4. Product switching between 2011 and 2016
Firm activity(Observations)
Percentage of firms
Output-weighted percentage of
firms

None
(17,476)

Drop only(1,362)

Add only(1,915)Both(7,862)

61.0

4.8

6.7

27.5

22.4

7.6

6.4

63.6

Notes: Continuing firms between 2011 and 2016 are used. The None group includes firms that neither
add nor drop any products between 2011 and 2016. The Drop only and Add only groups contain firmsthat only drop or only add products, respectively. Firms that both add and drop products are classified
into the Both group.

28

Table 5. Summary statistics of the explanatory variables
Mean
Std. Dev.
Min
ΔTariff-2.1372.802-21.982
TFP
27.48727.9990.101
L60.094698.8821Age14.5949.751
1
Export
0.354
0.478
0
Multi-product
0.187
0.390
0
ΔIP
-0.00001
0.013-0.040Max
0.266
1314.559
87112
86
1
1
0.333

Notes: ∆()*+,,- is the change in firm i’s specific tariff rate between 2011 and 2016. (>?- is firm i’svalue-added TFP measured by the Cobb–Douglas production function with a two-thirds labor share in
2011. A- is the number of permanent workers and CD2- is the age of firm I in 2011. EFGH*I- andJK;I+ − G*HLKMI- are dummy variables that take the value of 1 if firm i exports and produces morethan two products in 2011, respectively; otherwise, they are 0. ∆O?P is the change in import penetration
between 2011 and 2016 in the four-digit-level industry j to which firm i belongs.

29

Variable

Table 6. Changes in product scope and tariff reductions△The number of products△Diversification(1)(2)

ΔTariffln(TFP)ln(L)ln(Age)ExportMulti-product
ΔIP
Constant
Observations
R-squared

0.005**(0.002)0.008(0.006)0.100***(0.011)-0.012**(0.006)-0.008(0.010)-0.505***(0.015)0.197(0.331)-0.183***(0.048)0.001**(0.0004)-0.001(0.001)0.020***(0.001)-0.003*(0.002)-0.001(0.002)-0.169***(0.004)0.089(0.074)-0.012(0.007)28,4890.078

28,4890.140

Notes: The dependent variable is the change in the number of eight-digit KSIC products producedby firm i (or the change in the product diversification of firm i) between 2011 and 2016. ∆()*+,,is the change in firm i’s specific tariff rate between 2011 and 2016. (>?- is firm i’s value-added
TFP measured by the Cobb–Douglas production function with a two-thirds labor share in 2011.
A- is the number of permanent workers and CD2- is the age of firm i in 2011. EFGH*I- andJK;I+ − G*HLKMI- are dummy variables that take the value of 1 if firm i exports and produces
more than two products in 2011, respectively; otherwise, they are 0. ∆O?P is the change in importpenetration between 2011 and 2016 in the four-digit-level industry j to which firm i belongs.
Numbers in parentheses are robust standard errors. ***, **, and * indicate statistical significanceat the 1%, 5%, and 10% levels, respectively.

30

Table 7. Placebo effectVariableΔTariffln(TFP)ln(L)ln(Age)ExportMulti-product
ΔIP
Constant
Observations
R-squared△The number of products(1)△Diversification(2)0.0001(0.002)0.007(0.005)0.040***(0.010)-0.018***(0.005)0.005(0.008)-0.562***(0.013)0.699**(0.308)-0.029(0.044)0.0001(0.0003)
0.004***(0.001)0.017***(0.001)
0.002(0.001)
0.002(0.002)-0.156***(0.003)0.055(0.071)-0.049***(0.006)30,4580.121

30,4580.149

Notes: Dependent variables are changes in the number of products or the product diversification
indices between 2008 and 2011. ∆()*+,,- is the change in firm i’s specific tariff rate between
2011 and 2016. (>?- is firm i’s value-added TFP measured by the Cobb–Douglas production
function with a two-thirds labor share in 2011. A- is the number of permanent workers and CD2is the age of firm i in 2011. EFGH*I- and JK;I+ − G*HLKMI- are dummy variables that take thevalue of 1 if firm i exports and produces more than two products in 2011, respectively; otherwise,
they are 0. ∆O?P is the change in import penetration between 2011 and 2016 in the four-digit-levelindustry j to which firm i belongs. Numbers in parentheses are robust standard errors. ***, **, and* indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

31

Table 8. Efficiencies of the switched products
Dropped products(1)(2)
Efficiency (Single)-0.100***(0.010)Efficiency (Multi)-0.077***(0.009)Constant

0.044(0.259)-0.095***(0.008)0.083(0.267)

Industry fixed effectFirm fixed effectObservation

OO
30,3630.509

OO
30,3630.520

R-squared

Added products
(3)
(4)

0.037
(0.231)-0.085***(0.008)0.089(0.218)OO
31,3280.494

OO
31,3280.508

Notes: The dependent variable in columns (1) and (2) is 1 if a product is produced by a firm in 2011 but
not produced in 2016 (in columns (3) and (4), this is 1 if a product is not produced by a firm in 2011 but
produced in 2016) and it takes 0 if a product is produced both in 2011 and in 2016. Efficiency (Single) is
the firm-product efficiency estimated by the single-product production function. Efficiency (Multi) is thefirm-product efficiency estimated by the multi-product production function. All the columns include thefour-digit-level industry and firm dummy variables. Numbers in parentheses are standard errors clusteredat the eight-digit product level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10%
levels, respectively.

32

Table 9. Tracing the added products, 2013–2016
Δ Efficiency Δ Efficiency
Δ ln(Sales) Δ Sales share(Single)(Multi)
(1)
(2)
(3)
(4)
Added productConstant

Industry fixed effectFirm fixed effectObservation
R-squared

0.041(0.068)-0.622(0.385)0.194***(0.083)-0.516(0.757)0.229***(0.087)-0.272(0.863)0.064***(0.018)0.007(0.259)OO
26,8710.673

OO
26,8710.639

OO
26,8710.606

OO
26,871
0.039

Notes: The dependent variables are the changes in firm-product efficiencies, log of product sales,and product sales share between 2013 and 2016. The Added product variable is 1 if a product is notproduced by a firm in 2011 but produced in 2013 and it takes 0 if a product is produced both in2011 and in 2013. All the columns include the four-digit-level industry and firm dummy variables.
Numbers in parentheses are standard errors clustered at the eight-digit product level. ***, **, and* indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

33

0.565***(0.043)-1.561(1.366)OO
26,5170.074-0.299(0.251)OO
26,5170.190

0.103***(0.011)-0.322(0.220)OO50,4570.172

0.868(0.535)OO
50,4570.171

0.112***(0.020)0.841(0.532)OO
50,4570.113

0.756(0.527)

OO
50,4570.113

0.095***(0.020)0.734(0.526)

Efficiency (Single)

Table 10. Product growth and efficiency
Continuing products
All products
Growth in the share of
Growth in the share of
Growth in sales
Growth in sales
sales
sales
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.069***
0.160***
0.138***(0.012)(0.024)(0.023)-1.357(1.576)OO
26,5170.792(1)0.636***(0.069)Constant

OO
26,5170.762

Efficiency (Multi)

Industry fixed effectFirm fixed effectObservation
R-squared

Notes: The sample in columns (1)–(4) consists of products produced both in 2011 and in 2016. Growth variables in columns (1)-(4) are measured by
the log differences between 2011 and 2016. The growth variables in columns (5) and (6) are measured by the output changes between 2011 and 2016
divided by the mean of the two periods’ values. Efficiency (Single) is the firm-product efficiency estimated by the single-product production function.
Efficiency (Multi) is the firm-product efficiency estimated by the multi-product production function. For the efficiency of incumbent products, 2016
data are utilized. All the columns include the four-digit-level industry and firm dummy variables. Numbers in parentheses are standard errors clusteredat the eight-digit product level. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

34

Appendix

l
k
m
!"#

107

Other Food
Products(6)281Electrical
Equipment

(7)

(8)
Other
Chemical
Products
204(9)Structural
Metal
Products
251(10)
Cement,Lime, and
Plaster
233

Table A1. Estimation results of the multi-product production function at the three-digit industry level in 2011
(5)
Parts for
Motor
Vehicles303

0.742(0.052)0.046**(0.021)0.752***(0.029)-0.136***(0.007)(4)259

0.207***(0.035)0.087***(0.016)
0.713***(0.022)-0.146***(0.005)(2)(3)
SpecialPurpose
Machinery292

0.119**(0.053)0.040(0.028)0.755***(0.030)-0.181***(0.007)

Other Metal
Products

222

0.091**(0.040)0.055***(0.016)
0.718***(0.026)-0.144***(0.005)

Plastic
Products(1)
GeneralPurposeMachinery291

0.349***(0.061)0.0001(0.027)0.693***(0.042)-0.212***(0.009)940

0.251***(0.040)0.109***(0.021)0.614***(0.025)-0.136***(0.005)1,024

0.291***(0.023)0.072***(0.010)0.652***(0.014)-0.131***(0.003)1,145

0.284***(0.033)0.050***(0.015)0.594***(0.019)-0.158***(0.004)1,164

0.348***(0.025)0.042***(0.012)0.647***(0.016)-0.146***(0.003)1,188

0.149***(0.027)0.072***(0.012)0.680***(0.017)-0.147***(0.004)

2,471
2,430
2,228
1,968
1,521
Observation
Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

35

l
k
m
!"#

(9)
Structural
Metal
Products
251

0.284***(0.067)0.119***(0.028)0.560***(0.037)-0.153***(0.008)(10)
Cement,Lime, and
Plaster
233

Table A2. Estimation results of the multi-product production function at the three-digit industry level in 2016

281

0.515***(0.046)0.145***(0.022)0.405***(0.025)-0.139***(0.007)

(8)

(7)
Other
Chemical
Products
204

0.522***(0.046)0.081***(0.021)
0.342***(0.030)-0.145***(0.006)(6)

107

0.303***(0.060)0.165***(0.031)0.479***(0.029)-0.161***(0.008)(4)(5)
Parts for
Motor
Vehicles303

0.467***(0.062)
0.098***(0.034)
0.398***(0.042)-0.188***(0.010)(2)259

0.424***(0.049)0.233***(0.029)0.363***(0.028)-0.154***(0.006)

1,218

1,181

1,044

960

Electrical
Equipment(3)
SpecialPurpose
Machinery292
0.583***(0.029)0.084***(0.013)0.397***(0.016)-0.131***(0.004)

1,257

Other Food
Products

222
0.684***(0.033)0.057***(0.016)0.312***(0.019)-0.155***(0.004)

Other Metal
Products(1)
GeneralPurposeMachinery291
0.722***(0.028)0.068***(0.014)0.324***(0.016)-0.140***(0.004)

Plastic
Products

0.537***(0.032)0.062***(0.015)0.393***(0.020)-0.151***(0.004)

2,253
2,461
2,306
1,991
1,729
Observation
Notes: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

36