This document summarizes Andrew Witkowski's research on firm-level heterogeneity in China's textile industry during its unilateral trade liberalization. He analyzed transactional trade data between China and the US from 2000-2006 to compare export volumes and quotas. His analysis found differences in export performance between state-owned, private and foreign-owned textile firms after trade barriers were reduced. While inconclusive due to differences in product coding, the research highlighted opportunities for further study on how firm ownership impacts responses to trade liberalization.

1. Andrew Witkowski
February 11, 2013
Prof. Mark Dallas
Union College Summer Research Fellowship Final Paper
Firm Level Heterogeneity in China During Unilateral Trade Liberalization
Traditional models of trade are focused on input structures and their affect
on a given country’s comparative advantage in producing a good or service over
another country. The Ricardian Theory of Comparative Advantage establishes that
import-export arrangements are built upon a given country’s ability to produce a
good at a technical comparative advantage to another country holding inputs
constant. Building upon this, the Heckscher-Ohlin Model of Trade (H-O Model)
introduces dynamic inputs to Ricardian Theory stating that a country’s endowed
factors of production (land, labor, and capital) determine its comparative advantage
and thus strongly effect the basket of goods it exports have a direct affect on its
comparative advantage (Brackman et al 2006).
In real-world application, the H-O model would suggest that developing
countries with low labor costs (China, India, Mexico) would maintain a comparative
advantage in unskilled, manufactured goods such as clothing or non-electronic toys
while developed countries (U.S., Japan, South Korea) would focus their efforts
towards R&D intensive products such as cell phones, computers, and cameras.
Despite this, China posted a $380 billion trade surplus in 2014 with its major
exports being computers (9.9%), broadcasting equipment (5.2%), and telephones
(4.3%) (MIT). Adding to theory misalignment, China’s primary export destination at
19% of total exports is the United States (Ibid). However, in an ever-globalizing

2. world, these statistics could be misleading in measuring domestic value for reasons
discussed below.
China’s unilateral trade liberalization and accession into the World Trade
Organization in 2001, coupled with improvements in supply chain management
brought on by computing power advancements and container ship design have
greatly reduced costly barriers of international logistics (Lardy 2003; Jones and
Kierzkowski 2003). As a result, firms have taken to “fragmentation of production” or
the breaking up production processes through de-verticalization, de-agglomeration,
and internationalization of manufacturing. Essentially, firms see that the
international trade climate is conducive for profit-maximization by outsourcing
intermediate input stages in an international production network that resembles an
assembly line.
The nature of production fragmentation may cause misleading trade
statistics because conventional trade flows are measured on a gross scale that may
cause over-calculated total global value of trade (Brakman et al.). Countries that
conduct final-stage manufacturing (i.e. assembly) generate high export gains when a
finished product like a phone or a computer leaves their borders even if value-
added to the product in country was relatively insignificant. For example, research
on Apple’s iPad and iPhone production network estimates that Chinese labor
accounts for only 1.8% of the total value added to an Apple iPhone (Dedrick et al
2011). To account for this discrepancy, better metrics must be utilized to measure
the contribution of trade to nations’ “value-added” to a product such that trade

3. flows can be decomposed into domestic and foreign value-added components
through a Global Value Chain (GVC) (OECD-WTO 2012).
To do so trade flows must be examined at decomposed, intra-industry
level—a task nearly impossible to conduct with publically available trade data.
Fortunately, Union College purchased a collection of every import and export
transaction conducted by China over the period of 2000 to 2006. The collection
entitled the China Transactional Trade Database (CTTD) offers immense insight into
Sino-International GVCs amid China’s transition into the WTO on a firm-to-firm
basis.
In his preliminary research findings, Professor Dallas organized and analyzed
the transactional data of one light industry—footwear—operating in China. His
findings suggest that, contrary to traditional trade theory implications,
heterogeneity on a firm-to-firm basis is much more important on an international
level. He finds that, even in a “perfect-market” industry like footwear, firms are
“channeled” in such a way that those focused on specialized, quality production
enjoy the benefits of transactional stability, final markets, and market niches while
firms that produce generic, commoditized footwear are left in the dust.
To broaden the scope of Professor Dallas’s initial research findings, I
awarded a four-week Summer Research Grant. Together, Professor Dallas and I
conducted analysis on another light industry: completed articles of clothing. Our
research focused on the implications of foreign direct investment in the textile
industry during a time in which China’s trade barriers were liberalized. To do so, we

4. had to organize our data in a way that allowed us to test the presence of firm level
heterogeneity in China’s exported textile industry.
We first reached out to the American Office for Textiles and Apparel (OTEXA)
data utilized by the National Bureau of Economic Research to study China’s
experience under the Multifiber Agreement (MFA) and the Agreement on Textiles
and Clothing (ATC). The data consisted of raw performance reports stating quotas
on products covered by MFA-specific codes and percentage of that quota filled
categorized by country. Below is a sample of the performance reports.
Exhibit 1: OTEXA Performance Report for Year 2000
Source: American Office for Textiles and Apparel
Since the data was given to us in a text file, one of my first tasks was to reorganize
the data into a coherent excel spreadsheet. A sample of my spreadsheet is below.
Exhibit 2: OTEXA Performance Report for Year 2000 (Excel)

5. These data allow us to see both efficiency and volume improvements after trade
liberalization on an aggregate level. Our next step was to compare these quotas to
the transactions in the CTTD.
One challenge we ran into was differences in product codes between
different databases. The three-digit codes above were specific to the MFA. The CTTD
utilizes 8-digit Harmonized System (HS) generalized trade code. Conventionally, the
greater the digits, the more precise the code. To further complicate things, these
codes change year to year. Luckily, the US government releases code concordances
that coordinate the codes. My next task was to download the concordance
spreadsheets from OTEXA and then create a string of HS codes for every MFA code.
A sample of this work is depicted below.
Exhibit 3: MFA-HS Code Concordances

6. Once the appropriate concordances were gathered, we now were able to
begin our CTTD downloads. Because there is such a large amount of data points to
be downloaded for each HS code, we decided to narrow the scope of our research to
exports of articles of apparel (HS codes 61 and 62) to the United States in years
2005 and 2006.
Once the raw data was downloaded, we could now manipulate the data in
excel. We created a PivotTable in excel that allowed us to filter differences in factors
such as firm ownership, country destination, and volume. The bar chart below
shows volume exported to the United States between July and December of 2005.

7. From here, we can filter out firm ownership (state, foreign, private, etc.). Firm
ownership differences have distinct implications as to which firms respond more
positively to trade liberation.
Exhibit 4: Volume of Chinese Exports to the United States
Below is the bar chart after combining firms into categories based on
ownership.
Exhibit 5: HS61 Export Volume by Firm Ownership July-December 2005
0
10000000
20000000
30000000
40000000
50000000
60000000
1101910024
2101911348
3109965198
3122210450
3122449001
3204962021
3210960869
3224930238
3301960817
3302969042
3306941138
3319940029
3502160566
3702911640
3707962764
4101910276
4403060168
4403062370
4403064421
4403163296
4403168582
4404943164
4415942010
4420944071
HS61 July to December, 2005
Sum of USDAmount
Sum of qty

8. When we compare the above chart to those in 2006, we can begin to see
changes made firms backed by different owners. These changes may be brought on
by government connections, trade connections overseas, and more. To truly
measure whether a firm ownership is a factor, event study regression analysis
would have to be conducted. However, this analysis could not move on to this area
of focus in the four-week time constraint.
Instead, through our pivot filters, we began examining the HS codes that
make up MFA codes utilized in the OTEXA performance reports. By comparing these
codes, we could realize which firms were primarily filling American trade quotas.
Below is an example of the spreadsheet doing so using MFA Code 340/640 (men’s
knitted shirts).
Exhibit 6: CTTD Data by MFA Code 340/640
0 1E+09 2E+09 3E+09 4E+09
Collective
Foreign Owned
Individual Businesses
Joint Capital
Joint Cooperative
Private
State Owned
Grand Total
Volume by Firm Ownership
Sum of Qty Sum of USDAmount

9. Unfortunately, these comparisons are inconclusive for the reason of
precision differences in the CTTD and the countries required HS precision. The
CTTD is precise to 8-digits of HS while the China only requires its trade data to have
6-digits. The unknown 2 digits of precision cause for quite of a bit of miscounting
that was not counted for in the OTEXA performance reports. For that reason, this
research leaves a lot of questions unanswered and potential for future research.
Professor Dallas and I intend to continue research on throughout the coming
academic year with hopes to one day have it published in an economic journal. I
would like to thank Union College, its supporters, and Professor Dallas for the
opportunity to conduct my research. I am looking forward to future work on this
subject topic.

10. Bibliography
Bair, Jennifer. "Global Capitalism and Commodity Chains: Looking Back, Going
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Batson, Andrew. "Not Really 'Made in China': The iPhones's Complex Supply Chain
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2010.
Brakman, Steven, Harry Garretsen, Charles van Marrewijk, and Arjen van
Witteloostuijn. Nations and Firms in the Global Economy: An Introduction to
International Economics and Business. New York: Cambridge University Press,
2006.
Jones, Ronald, and Henryk Kierzkowski. International Fragmentation and the New
Economic Geography. University of Rochester, 2003.
Lardy, Nicholas. Trade Liberalization and Its Role in Chinese Economic Growth.
Washington D.C.: Institute for International Economics, 2003.
Massachusettes Institute of Technology. Learn More About Trade in China. 2015.
http://atlas.media.mit.edu/profile/country/chn/ (accessed Februrary 11,
2015).
OECD-WTO. "Trade in Value-Added: Concepts, Methodologies and Challenges."
2012.