metadata: authors: 0: author: Kee,Hiau Looi
1: author: Tang,Heiwai
count: China
docna: 0: docna: Domestic value added in exports: theory and
            firm evidence from China
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Transport and Trade Logistics,Industrial and Consumer Services and Products,Food & Beverage Industry,Plastics & Rubber Industry,Construction Industry,General Manufacturing,Common Carriers Industry,Business Cycles and Stabilization Policies,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Labor Markets
teratopic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,International Economics and Trade,Transport
abstracts: cdata!: China has defied the declining trend in
            domestic content in exports in many countries. This paper
            studies China’s rising domestic content in exports using
            firm- and customs transaction-level data. The approach
            embraces firm heterogeneity and hence reduces aggregation
            bias. The study finds that the substitution of domestic for
            imported materials by individual processing exporters caused
            China’s domestic content in exports to increase from 65 to
            70 percent in 2000–2007. Such substitution was induced by
            the country’s trade and investment liberalization, which
            deepened its engagement in global value chains and led to a
            greater variety of domestic materials becoming available at
            lower prices.
geo_reg_key: 517191,517198,119225
repnme: repnme: Domestic value added in exports: theory and
            firm evidence from China
display_title: Domestic value added in exports:
            theory and firm evidence from China
listing_relative_url: /research/2015/11/25462516/domestic-value-added-exports-theory-firm-evidence-china
topicv3: Industrial Clusters and Value Chains
            m1326062 1226,Global Value Chains(GVC) m1326472 767,Job Creation and Job Opportunities
            m1326483 1170,Manufacturing Sectors m1326521 1281,Trade m1326924 698
publishtoextweb_dt: 2015-11-13T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Social Protections and Labor,International Economics and Trade,Transport
no_of_pages: 83
author_keys: 0: author_key: 559677
1: author_key: 1243095
model_extractions: WPS7491 Policy Research Working Paper 7491 Domestic Value Added in Exports Theory and Firm Evidence from China _Hiau Looi Kee Heiwai Tang_ Development Research Group Trade and International Integration Team November 2015
Policy Research Working Paper 7491

# **Abstract**

China has defied the declining trend in domestic content in exports in many countries. This paper studies China’s rising domestic content in exports using firm- and customs transaction-level data. The approach embraces firm heterogeneity and hence reduces aggregation bias. The study finds that the substitution of domestic for imported materials by individual processing exporters caused China’s domestic content in exports to increase from 65 to 70 percent in 2000–2007. Such substitution was induced by the country’s trade and investment liberalization, which deepened its engagement in global value chains and led to a greater variety of domestic materials becoming available at lower prices.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hlkee@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Domestic Value Added in Exports: Theory and Firm Evidence from China<sup>�</sup>

Hiau Looi Kee<sup>y</sup> Heiwai Tang<sup>z</sup> World Bank Johns Hopkins University Key Words: Firm heterogeneity, Domestic value added, Value added trade, China, Global value chains, Trade liberalization, FDI liberalization JEL Classi...cation Numbers: F10, F14

> �We thank Richard Baldwin, Andrew Bernard, Thibault Fally, Gordon Hanson, Russell Hillberry, David Hummels, Robert Johnson, Wolfgang Keller, Pravin Krishna, Aaditya Mattoo, Marcelo Olarreaga, Nina Pavcnik, James Riedel, Georg Schaur, Peter Schott, Zhi Wang, and Shang-Jin Wei for discussions and comments. We are also grateful for conference/seminar participants at the AEA, Barcelona GSE Summer Forum, Brandeis, CESIfo, Dartmouth, FREIT (UCSC), Georgetown, Graduate Institute - Geneva, GTDW, IMF/WB/WTO Workshop, Nottingham, Penn State-Tsinghua Conference, Colorado Boulder, USITC, and the World Bank for feedbacks. Research for this paper has in part been supported by the World Bank’s Multidonor Trust Fund for Trade and Development and the Strategic Research Program on Economic Development. The results and opinions present in this paper are our own, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

> yDevelopment Research Group, The World Bank, Washington, DC 20433, USA; Tel.: (202) 473-4155; Fax: (202)522-1159; e-mail: hlkee@worldbank.org.

> zJohns Hopkins University, School of Advanced International Studies, 1717 Massachusetts Ave NW, Washington, DC 20036, USA. Tel.: (202) 663-5679; email: hwtang@jhu.edu.
“Production processes are more and more fragmented ... The nature of trade has changed, but our trade data have not ... Many goods are assembled in China, but their commercial value comes from the numerous countries ... We want to know the value added by each country in the production process of ...nal goods.” –Pascal Lamy, Director-General of WTO, “Made in the World”Initiative, 2011

# 1 Introduction

Over the past two decades, increasing global production fragmentation has allowed exporting ...rms to rely less on domestic inputs for production. Indeed, research ...nds that domestic content in exports has been declining in most countries. China is an intriguing exception.<sup>1</sup> What caused China to defy the declining trend in domestic content in exports in most countries, despite its deep engagement in global value chains? There are several possible answers to this question with con‡icting implications. The rising domestic content could re‡ect the changing composition of Chinese exports, suggesting that China has shifted its comparative advantage towards the industries with high domestic content. It could also be a result of its increasing domestic production costs, which would imply that the country has become less competitive. Yet another possible answer is that it could be due to the gradual substitution of domestic for imported materials by its exporters. This would imply that China has become more competitive, particularly in the intermediate input sectors. Understanding the determinants of China’s rising domestic content in exports can provide important development policy insights for other countries.

This paper uses customs transaction-level data merged with ...rm survey data to measure

> 1Koopman, Wang and Wei (2012) ...nd that China’s DVAR rose between 2002 and 2007. Johnson and Noguera (2014), using the GTAP IO tables, show that from 1970 and 2009, the DVAR of all countries in their sample are declining, except for the Republic of Korea and Indonesia.

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and analyze China’s rising domestic content in exports, or the ratio of domestic value added in exports to gross exports (DVAR). Our transaction-level data cover the universe of Chinese exporters during the period of 2000-2007, allowing us to construct ...rm, industry and aggregate DVARs over time to study their evolution. The recent burgeoning literature on measuring industry and aggregate DVARs relies on input-output (IO) tables. While using IO tables has the advantage of capturing IO linkages within and across countries, the presence of ...rm heterogeneity may result in signi...cant aggregation biases in the estimates of the DVAR. Our ground-up approach embraces ...rm heterogeneity by measuring industry and aggregate DVARs as the weighted averages of the underlying ...rms’DVARs.<sup>2</sup> This unique methodology further allows us to compute bootstrapped standard errors for our aggregate estimates, which are then used to perform statistical tests on the rising trend. Finally, we use the customs data merged with manufacturing ...rm survey data to examine whether changes in export composition, ...rms’production costs and material shares are responsible for China’s rising DVAR.

Our DVAR estimates con...rm existing studies that China’s DVAR has been rising, but are higher than previous estimates. Speci...cally, we ...nd that the DVAR of China’s aggregate exports increased from 65% to 70% between 2000 and 2007, with similar magnitudes of increases in the country’s bilateral exports to its major trading partners. The increase in the aggregate DVAR is statistically signi...cant and con...rms the upward trend found in Koopman, Wang, and Wei (2012) (KWW12 hereafter), which adopts an IO table-based approach. However, our DVAR estimate for processing exports is signi...cantly higher than those of KWW12.

The ...nding that our DVAR estimates are higher than those of KWW12 exempli...es

> 2Ahmad et al. (2014) also allow ...rm heterogeneity to a¤ect the estimates of a country’s aggregate DVAR. They use ...rm-level data to generate indicators by exporter status, which are then used to re...ne IO-table based estimates of domestic value added in exports from Turkey.

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that ignoring ...rm heterogeneity may lead to downward aggregation bias in the IO tablebased approach. Samples that are used to construct IO tables often consist mainly of large ...rms.<sup>3</sup> Given that large ...rms tend to have a lower DVAR due to their high import-to-sales ratios, over-sampling large ...rms in the construction of IO tables can lead to lower estimates of the aggregate DVAR.<sup>4</sup> To illustrate this point, we conduct a decomposition exercise. In particular, we show how our DVAR estimate can be lowered to a level that is not statistically di¤erent from that of KWW12, just by using a sample that includes only those large ...rms that satisfy the sample selection criteria behind the construction of the Chinese IO tables. This suggests that aggregation bias driven by ...rm heterogeneity alone is su¢ cient to explain the wedge between our estimates.

What has caused the rise in China’s aggregate DVAR? Our ...rm-level regressions reveal that it is mainly driven by individual processing exporters substituting domestic for imported materials, both in terms of volume and varieties. Other factors, such as rising production costs due to higher wages, changing composition of Chinese exports towards the high-DVAR industries, or churning of ...rms with di¤erent DVARs, cannot explain the upward trend during the sample period.

We also ...nd that the substitution of domestic for imported materials was induced by the country’s trade and FDI liberalization since the early 2000s. To guide our empirical analysis, we build a model featuring a translog cost function, which permits an estimation of the time-varying elasticity of substitution between domestic and foreign input varieties to study how various government policies may a¤ect a country’s DVAR. We ...nd that for China, increasing FDI and declining input tari¤s have led to a greater variety of domestic materials becoming available at lower prices during the sample period. For the entire processing

> 3See the United Nations Handbook of Input-Output Table Compilation and Analysis (1999).

> 4Recent research by Amiti, Itskhoki and Konings (2014) and Blaum, Lelarge and Peters (2014) shows that larger ...rms tend to have a higher import-to-sales ratio.

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sector and for most industries within that sector, imported and domestic materials are gross substitutes, with the estimated elasticity of substitution ranging between 1.9 and 6.6. These large elasticities explain why lower prices of domestic materials can result in such signi...cant increases in the DVAR at the ...rm and thus the aggregate level in China.

Despite its simplicity, our methodology can be applied widely. In its basic form, our methodology can be used directly to measure the DVAR of processing exporters that operate in many export-oriented industries deeply embedded in global value chains.<sup>5</sup> Furthermore, with additional assumptions, our methodology can be applied to constructing the DVAR for countries that have little dependence on processing exports.<sup>6</sup> Our paper is related to several strands of literature. It relates to the literature on measuring value added trade.<sup>7</sup> In particular, our DVAR estimates complement the IO table-based estimates, by incorporating ...rm heterogeneity and thus minimizing aggregation bias. This paper is also related to the literature on the e¤ects of trade and FDI liberalization on domestic product varieties.<sup>8</sup> Our results con...rm existing ...ndings that the reduction in input tari¤s and increased presence of FDI in downstream sectors could lead to an expansion of domestic product variety. Finally, our paper contributes to the literature on international production sharing and global value chains,<sup>9</sup> as well as the studies on China’s increasing

> 5The industries of garment, shoes, and toys in Bangladesh, Cambodia, Dominican Republic, and Mauritius, as well as the electronics industry in Malaysia, Thailand, and Vietnam are some of the examples.

> 6In research in progress, we apply our current methodology to contruct the DVAR for a wide range of countries, where we obtain matched importer-exporter customs transaction data from the World Bank’s Exporter Dynamics Database (Cebeci, et al., 2012). Preliminary results, based on Bangladesh, Guatemala, Madagascar and Morocco, show an upward trend in the DVAR of these countries’aggregate exports.

> 7This literature starts with Hummels, Ishii and Yi (2001) to use industry input-output (IO) tables to calculate the value added to exports ratios for many countries. Recent related work includes Antràs, Chor, Fally, and Hillberry (2012), Johnson and Noguera (2012 and 2014), Koopman, Wang and Wei (2012, 2014), Antràs and Chor (2013), De la Cruz, Koopman, Wang and Wei (2013), and Johnson (2014).

> 8Goldberg et. at. (2008) studies the impact of trade liberalization of India on its export variety. Kee (2015) shows that the increased presence of FDI in the garment sector of Bangladesh caused a greater variety of domestic materials to become available which led to product scope expansion and productivity gains in domestic garment ...rms.

> 9See Feenstra (1998) for a review of the early literatre on foreign outsourcing. More recent work includes, among others, Baldwin (2012) which postulates how participating in a global supply chain should be viewed as a new strategy of industrialization; and Timmer et al. (2014) which summarizes the main ...ndings in the

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engagement in the global economy.<sup>10</sup> Our results speak to both bodies of work by showing that China’s rising DVAR is due to the substitution of domestic for imported materials. Such substitution indicates that the country is relying less on imports and becoming more competitive in intermediate input sectors. This suggests that China has been moving up the value chains, and thus may have signi...cant implications for world trade and the global economy, given its sheer size.<sup>11</sup> The paper proceeds as follows. Section 2 de...nes our measures of ...rm DVAR. Section 3 shows how we use ...rms’DVAR to compute industry and aggregate DVARs, and analyze their patterns. We also discuss the associated aggregation biases in the standard IO tablebased approach, extend our methodology to include the non-processing sector, and calculate the DVAR of China’s aggregate exports in this section. Section 4 presents the pattern of ...rm DVAR. Section 5 develops a simple model to theoretically and quantitatively study the determinants of ...rm DVAR. Section 6 concludes. In the Appendix, we describe our data sets and the construction of the main variables, such as the number of upstream varieties, import varieties, and industry exchange rates. A theoretical model that features a Cobb-Douglas production function is also presented there.

literature on global value chains.

> 10Johnson and Noguera (2012) show that the US-China trade imbalance in 2004 is 30-40 percent smaller when trade is measured in value added. Autor, Dorn, and Hanson (2013) show that increasing Chinese imports cause signi...cantly suppressed job creation, lower wages, lower labor market participation, and higher unemployment in the U.S. Pierce and Schott (2015) ...nd that U.S. industries with the larger decline in tari¤s against imports from China experienced the slower employment growth, lower job creation, and higher job destruction.

> 11These ...ndings are consistent with a recent paper by Constantinescu, Mattoo and Ruta (2015) who suggest that China’s structural transformation may be an important reason for the recent global trade slowdown, as China is relying less on foreign materials, thanks to its increasingly competitive domestic intermediate input industries.

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# 2 De...ning Firm-Level Domestic Value Added

We use two micro data sets in this paper: Chinese customs transaction-level trade data from 2000 to 2007, and the Annual Surveys of Industrial Firms from the National Bureau of Statistics of China over the same period. Readers are referred to the appendix for details.<sup>12</sup> For the ease of exposition, we ...rst focus on processing exporters, which are required by law to sell all their outputs abroad and may import materials free of duties.<sup>13</sup> In Section 3.3, we will extend our methodology to study non-processing exports and thus aggregate exports of China.

Let us ...rst de...ne the main variable of interest –domestic value added in exports (DVA), starting from the accounting identity of a ...rm’s total revenue. A ...rm’s (i) total revenue (PYi), by de...nition, consists of the following components: pro...ts, (�i) ; wages (wLi) ; cost of capital (rKi) ; cost of domestic materials �P<sup>D</sup> Mi<sup>D</sup> �, and cost of imported materials �P<sup>I</sup> Mi<sup>I</sup> �: Some domestic materials may embody foreign content, while some imported materials may embody domestic content. Let us denote the foreign content in domestic materials and domestic content in imported materials by �<sup>F</sup> i<sup>and�D</sup> i<sup>,respectively.ThenP DM D</sup> i can be

> 12We employ the procedures commonly used to organize these data. We remove trade intermediaries, identi...ed by the methods proposed by Ahn et al. (2011), in the customs data. We also remove import and export transactions with China itself. As pointed out by Liu (2013), China’s re-imports from itself accounted for about 9% of its total imports. These abnormal trade ‡ows could arise from tax and transport cost saving incentives.

> 13China’s Customs regulates processing trade under several regimes, with pure assembly (PA) and import and assembly (IA) being the two main types. The main di¤erence between these two regimes lies in the allocation of control rights of the imported inputs. In the PA regime, a foreign ...rm supplies components to a Chinese assembly plant and retains ownership and control over the imported inputs throughout the production process. In the IA regime, a Chinese assembly plant imports components of its own accord and retains control over their use. Readers are referred to Feenstra and Hanson (2005) for a more detailed description of the two regimes. While this distinction will not a¤ect our DVAR estimates, it may a¤ect the way one should model ...rm sourcing decisions. See Fernandes and Tang (2012) which exploits these regulatory di¤erences to study the organizational form of o¤shoring. Later on we will report regression results separately for the two types of processing.

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written as the sum of �<sup>F</sup> i<sup>andapartthatconstitutespurelydomesticcontent,q</sup> i<sup>D:Likewise,</sup> P<sup>I</sup> Mi<sup>Icanbewrittenasthesumof�D</sup> i<sup>andapartthatconstitutespurelyforeigncontent,</sup> qi<sup>F:</sup> Similar to the concept of a country’s gross domestic product, we de...ne the DVA of a ...rm as the total value of domestic goods and services embodied in the ...rm’s output. In other words, a ...rm’s DVA equals the sum of its pro...ts, wages, rental costs of capital, and both direct or indirect domestic materials purchased:<sup>14</sup> For a processing ...rm that exports all its output and imports some of its intermediate inputs and capital equipment, its export (EXPi) equals its revenue, while its import (IMPi) equals the costs of imported materials, P<sup>I</sup> Mi<sup>I,andimportedcapital,�K</sup> i<sup>.Thus,(1)implies</sup> Equation (3) shows that we may use EXPi � IMPi to measure a processing ...rm’s DVA after adjusting for �<sup>D</sup> i<sup>;�F</sup> i<sup>and�K</sup> i<sup>.ForChina,KWW12andWang,Wei,andZhu(2014)</sup> ...nd that �<sup>D</sup> i<sup>isverycloseto0forprocessingexports.15Moreover,inourcurrentdataset,</sup>

> 14Note that while some ...rms may have foreign capital in its ownership, the returns to this foreign capital as well as its pro...ts are still included in its DVA in exports. This is because the service of these capital is rendered within the country’s borders. Moreover, a ...rm’s DVA contains domestic materials produced by other ...rms and is therefore larger than its own value added by de...nition.

> 15Based on the GTAP Multi-Country IO tables, Koopman, Wang, and Wei (2014) estimate that the domestic content embedded in imported materials accounted for 0.7% of China’s ordinary exports in 2004, and essentially 0 for its processing exports. Using the IO tables from the World Input-Output Database (WIOD), Wang, Wei and Zhu (2014) update these estimates and show that the domestic content embedded

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processing ...rms’imports of capital are recorded separately from material imports, implying �<sup>K</sup> i<sup>= 0.16Thus, the only necessary adjustmenthere is to remove foreign content in domestic</sup> materials, �<sup>F</sup> i<sup>, which causes EXPi�IMPito overestimate DV Aiin exports.From (3), ...rm</sup> i’s ratio of domestic value added in exports to gross exports (DVAR) depends only on the share of imported materials in total revenue (P<sup>I</sup> Mi<sup>I=PYi)withadjustmentsfor�F</sup> i<sup>=EXPi:</sup> Without ...rm-level information on �<sup>F</sup> i<sup>=EXPi,werefertoKWW12fortheindustryes-</sup> timates for 2007 and impute the estimates backward for each industry-year between 2000 and 2007, using the weighted average of the growth rates of the number of ordinary (nonprocessing) importers across upstream industries.<sup>17</sup> These industry estimates range from 0.4 to 5.7 percent, which we use to proxy for �<sup>F</sup> i<sup>=EXPiin(4)toconstructa...rm’sDVAR.18</sup> in imports used by Chinese exporters ( EXP�<sup>D</sup> <u>i</u> i<sup>)increasedfrom0.1%in1995to1.3%in2007.Theyalsoshow</sup> that these estimates can vary widely across sectors, ranging from 2.5% for the Chemical Products sector to 0.2% for the Leather and Footwear sector. Unfortunately, such estimates are not available separately for processing and ordinary exports. Nevertheless, given the low estimated domestic content in imported materials at the aggregate level, adjusting for it is unlikely to have a signi...cant e¤ect on both the levels and the trends of our aggregate DVAR estimates. Our approach therefore may underestimate the DVAR for sectors that use imported material with high domestic content. Given that our DVAR estimates for most sectors are already higher than the existing estimates based on IO tables, accounting for returned domestic content in imports will only strengthen our point that the existing estimates are subject to a downward aggregation bias.

16The Chinese customs data record material and capital imports separately from a ...rm’s total imports, in a category called “Equipment for Processing Trade”(code number = 20). We thank a referee for pointing this out.

17The rationale is that the net entry of ordinary importers, stimulated by China’s continuous trade liberalization, may increase the supply of intermediate inputs that embody more foreign content. This assumption is grounded on the ...ndings in Brandt et al. (2015), which shows that while the cost share of imports in total materials has been stable, the aggregate import share has increased substantially due to a large entry of new importers since China’s accession to the WTO in late 2001.

18Table A7 in the appendix reports the estimates of �<sup>F</sup> i<sup>=EXPiby industry-year.Notice that our approach</sup> does not double count DVA as long as we exclude indirect trade between processing ...rms and focus on mesauring DVA of the processing trade regime. We need additional assumptions to deal with the double8
Equation (5) shows that, once we control for the share of materials in total revenue P<sup>M</sup> Mi=PYi, factors that do not a¤ect the share of imported materials in total materials will not a¤ect a ...rm’s DVAR. This is an accounting identity, independent of the choice of production functions. It highlights that in order to understand a ...rm’s DVAR, one should focus on the determinants of the share of imported materials in total materials. In Section 5, we will develop a simple but general model that features a translog cost function to formally study these determinants.<sup>19</sup>

# 3 From Firm DVAR to Industry and Aggregate DVAR

Inferring the DVAR of an industry or aggregate exports from ...rms’DVAR is straightforward. If ...rms only engage in direct trading (i.e. do not import or export for other ...rms) and only produce in one industry, then we can compute the DVAR of industry j as follows: where �j is the set of ...rms in industry j. Industries are de...ned according to the industry classi...cation by the United Nations.<sup>20</sup> By construction, the DVAR of industry j is a weighted average of the DVAR of all ...rms in industry j with weights equal to the export shares of the ...rms. Likewise, we can sum up all industry imports and exports ...rst and then compute

> counting issue when we measure DVA for non-processing and aggregate exports.

> 19To show that our main theoretical results are not speci...c to the functional form choice, we also solve for a model that features a Cobb-Douglas production function in the Appendix.

> 20See http://unstats.un.org/unsd/tradekb/Knowledgebase/HS-Classi...cation-by-Section for the UN industry classi...cation. There are originally 20 sectors in the UN list. Sectors 1-3, which are agricultural sectors, are excluded since we cannot match most of the transactions to the manufacturing survey data. Sector 5 - Mining and Sector 19 - Arms and Ammunition are excluded for the same reason. Examples of a sector include Chemical Products (HS2 = 28-38), Textiles (HS2 = 50-63), Footwear and Headgear, etc. (HS2 = 64-67), and Machinery, Mechanical, Electrical Equipment (HS2 = 84-85).

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the DVAR of aggregate exports as follows: Similar to an industry’s DVAR, the aggregate DVAR constructed based on (7) is a weighted average of the DVAR of all ...rms, with weights re‡ecting the export shares of the ...rms.<sup>21</sup> While our ground-up approach is appropriate for inferring the aggregate DVAR, there are two caveats. The ...rst caveat is about multi-industry exporters, for whom the allocation of imported materials (IMPij) to the production of output in di¤erent industries (EXPij) is generally unobservable in the data, making the inference of an industry’s DVAR based on (6) impossible. Thus, we only use the subset of single-industry exporters to infer industry DVARs.<sup>22</sup> The second caveat relates to processing exporters importing indirectly through other ...rms in China. Under the current customs regulations in China, processing ...rms can legally sell imported materials to other ...rms and bene...ted from tari¤ exemption, as long as the buyers are also registered processing ...rms. Complicating this problem is that such transactions are not con...ned within the same industry or geographic location.<sup>23</sup> The transactions of imported materials between two processing ...rms in the domestic economy appear to be widespread according to our data.

This practice of indirect importing certainly impacts the way we construct the ...rm-level

> 21In reporting the aggregate DVAR, we ...rst aggregate ...rm DVARs to the industry level. To make sure that the industry-level analysis, particularly the between-and-within analysis, is not driven by potential noises due to merging the customs data with the ...rm data, we use industry weights based on the export value of single-industry exporters in the customs data set.

> 22Nevertheless, since the construction of the ...rm-level DVAR is not restricted by the multi-industry concerns, we will also include multi-industry exporters in the ...rm-level regressions below.

> 23See Regulations Concerning Customs Supervision and Control over the Inward Processing and Assembling Operation by China’s Ministry of Commerce. For example, a shoe processing exporter may import leather and sell it to a handbag processing exporter.

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and industry-level DVAR. In particular, for those ...rms that import more than their needs, which we call excessive importers, using (4) may underestimate their DVARs and in the extreme case result in negative DVARs.<sup>24</sup> On the other hand, for those ...rms that buy imported materials from other processing ...rms locally, which we call excessive exporters, using (4) may overestimate their DVARs, and in the extreme case bias the DVARs towards 1. To address the issue of indirect importing, we ...rst use balance-sheet data to identify both the excessive importers and exporters.

We de...ne excessive importers as those ...rms that import more than their total material costs as recorded in the NBS Annual Survey of Industrial Firms (2000-2007), given that total material costs should equal to the sum of imported materials and domestic materials.<sup>25</sup> These excessive importers import more than their total materials and are dropped from our sample. To identify excessive exporters, we ...rst identify all registered ordinary (nonprocessing) exporters that only export in a single industry. Unlike processing exporters, ordinary exporters are not required by China’s Customs to sell all outputs abroad. They can use imported materials to produce for both domestic and foreign sales. In addition, ordinary exporters need to pay import tari¤s and thus should have less incentive to import materials. The DVAR of ordinary exporters should be on average higher than that of processing exporters in the same industry. Thus, we use the 25th percentile of ordinary exporters’DVARs as an upper bound for processing exporters’DVAR, and identify all processing ...rms that have a DVAR higher than this cuto¤ as excessive exporters. Our ...rm-level regression results

> 24In the raw data, about 10 percent of the single-industry ...rms have negative net exports.

> 25Without a common ...rm identi...er shared by the two data sets, we use ...rm names to merge the customs transaction data with the NBS Annual Surveys of Industrial Firms. For rare cases that have duplicate ...rm names, we use the ...rm’s address to improve the merging. See Ma, Tang, and Zhang (2014) for details about the merging procedures. Tables A2 and A3 in the Appendix present the representation of the merged and ...ltered samples, relative to the original customs sample. In terms of the number of exporters, about 39% of the single-industry processing exporters from the customs data sets can be merged with the NBS data, and about 22% survive our ...lters that remove excessive importers and exporters. In terms of export value, our ...nal sample covers over 46% of exports based on the original customs data.

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below are robust to using higher percentiles of ordinary ...rms’DVAR as ...lters.

In summary, we focus on a subset of single-industry processing exporters that have their <u>IMP</u><sup>betweenthetwocuto¤s:</sup> EXP<sup>bounded</sup> where DV AR(25)<sup>OT= 1 �</sup> � EXPIMP �OT(25)<sup>isthe25percentileoftheDVARofordinaryexportersin</sup> the same industry.<sup>26</sup> Table 1 summarizes the main issues, assumptions and solutions of our approach to constructing the DVAR at the ...rm, industry and aggregate levels.<sup>27</sup>

# 3.1 Movement of the Industry and Aggregate DVAR of Processing Exports

The ...nal data set is an unbalanced panel of 17,903 observations for 8,459 single-industry processing exporters over 8 years (2000-2007).<sup>28</sup> Our sample covers a balanced panel of 15 industries throughout the sample period. An advantage of using the micro approach is that we can construct random samples drawn from the ...rm sample and compute bootstrapped standard errors for our estimates of the aggregate DVAR. Figure 1 shows our benchmark

> 26Table A8 in the appendix reports ~~�~~ EXPIMP �OT(25)<sup>byindustry-year.Wewillcheckthesensitivityofour</sup> regression results by including both excessive importers and exporters in the sample below.

> 27Sometimes, ...rms have incentives to stock up imported materials when the international prices of commodities are low, particularly in those industries that use a lot of commodities, such as iron, copper and crude oil, as inputs. Thus, imports may not be fully used to produce goods in the same period. For these ...rms, the calculation of the DVAR based on (4) may not be accurate. However, there is no easy way to resolve the issue of inventory management. As we will show in the next section, all ...rm observations with negative DVA are no longer negative once we use (8) to restrict our sample. This suggests that inventory management does not appear to drive our results.

> 28Our sample covers both types of processing trade in China – pure assembly (PA) and import-andassembly (IA). While we will check the robustness of our regression results below by repeating the analysis separately for the two regimes, it is important to point out that IA accounts for a much larger share of processing, in terms of the volume as well as the number of exporters, compared to PA. In our regression sample, over 90% of the observations belong to IA, in which exporters take control and hold ownership over the imported materials. We show in Figure A5 in the Appendix that even at its peak in 2000, PA never accounts for more than 30% of total processing exports, and continuously declined to less than 20% by 2007.

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estimates of the DVAR of Chinese processing exports, along with the 95-percent con...dence intervals based on 100 randomly drawn samples with replacement. Chinese processing exports’DVAR has been increasing from 0.46 in 2000 to 0.55 percent in 2007. Depending on the year, the 95-percent con...dence interval is between 5 to 11 percentage-point wide, with an average of 7 percentage points over the 8 years in our sample. Most importantly, based on the bootstrapped standard errors, the di¤erence between the DVAR of Chinese aggregate exports in 2007 and that of 2000 is statistically signi...cant, lending strong support for KWW12, who also ...nd an upward trend of similar magnitude based on IO tables and aggregate trade data. Figures A4 and A6 in the Appendix show similar trends, despite using samples with di¤erent cuto¤s from (8), and a sample that includes multi-industry ...rms.

Figure 2 plots the DVAR of processing exports across time for di¤erent industries, together with the 95-percent con...dence intervals based on 100 random samples drawn with replacement. The DVAR increased for all industries besides two (wood and articles; and base metals). For the industries that exhibit an upward DVAR trend, the tight con...dence intervals convincingly reject the null hypothesis that the DVAR estimates are the same between 2000 and 2007 (see Table A9 in the Appendix for details). For wood and articles, and base metals industries, their DVAR are not statistically di¤erent between 2000 and 2007. Overall, none of the industries exhibits a declining trend in the DVAR that is statistically signi...cant during the sample period.

The micro data also permit a decomposition of the aggregate trend into between- and within-industry changes. Speci...cally, the change in the aggregate DVAR (from year t � 1 to t) can be decomposed according to the following identity: 13
where <u>wjt</u> =<sup><u>1</u></sup> 2 � EXPEXPjtt<sup>+EXP</sup> EXP<sup><u>j</u></sup> t<sup>t</sup> �<sup>�</sup> 1<sup>1</sup> <u>�</u> is the average share of industry j in total exports over year t � 1 and t, while DV ARjt =<sup><u>1</u></sup> 2<sup>(DV ARjt + DV ARjt�1)isthesimpleaverageofindustry</sup> j’s DVAR over year t � 1 and t. Figure 3 shows that the increase in the aggregate DVAR over the sample period is all driven by within-industry increases in the DVAR rather than a between-industry reallocation of resources from the low-DVAR industries to the high-DVAR industries.

With these estimates, we further construct the bilateral DVAR with respect to China’s major trading partners. For each country-year, we compute the weighted average of the DVAR across industries, with weights equal to each industry’s share in total exports to the destination. Figure 4 shows that in all top 5 trading partners (i.e., the U.S., Hong Kong SAR, China, Japan, the Republic of Korea, and Germany), there is a clear upward trend in the bilateral DVAR. In particular, the DVAR of Chinese processing exports to the US has increased from 0.47 to 0.55 between 2000 and 2007.

# 3.2 Firm Heterogeneity and Aggregation Bias

How may ...rm heterogeneity a¤ect the aggregate DVAR estimates? In a nutshell, ...rm heterogeneity may lead to aggregation bias when the underlying sample used to construct the aggregate DVAR is not representative. This could happen if the following two conditions hold: (i) ...rm size is used as the sample selection criteria and (ii) there is a systematic relationship between ...rm size and import intensity.

The above two conditions may hold in the samples used to construct IO tables in general, and speci...cally for China. According to the United Nations Handbook of Input-Output Table Compilation and Analysis (1999, section V: Compilation of Production Accounts of Industries), the intermediate input consumption and input structure of large establishments could be applied to small establishments, given that they are often not covered by industry 14
statistics (p. 110). This suggests that small and medium size ...rms are routinely omitted from the IO table samples, and that the industry import intensities inferred are often based on data of mostly large ...rms. For China, according to the National Input-Output Survey Methods of China (2007) published by the National Bureau of Statistics of the People’s Republic of China, the sample used to construct the IO tables consists of all large ...rms that have at least 300 million yuan in revenue (about 38 million USD during the sample period), along with some small- and medium-sized ...rms sampled with unknown proportions (see item 5 on p. 3 about sample selection and item 4 on p. 27 about size cuto¤s). In other words, the sampling method behind the construction of Chinese IO tables is heavily biased towards the very large ...rms.

Second, recent research by Amiti, Itskhoki and Konings (2014) and Blaum, Lelarge and Peters (2014) shows that large ...rms tend to have a higher import-to-sales ratio. This is also con...rmed by our sample of Chinese ...rms. When we regress a ...rm’s import intensity on ...rm size (measured by log(sales)), controlling for industry-year ...xed e¤ects, we ...nd that doubling ...rm sales is associated with a 0.5 percentage-point increase in the ...rm’s import intensity (signi...cant at the 1% level).<sup>29</sup> Given that ...rms’import intensity and DVAR are negatively correlated, by omitting the smaller ...rms, IO tables by construction tend to include ...rms with a lower DVAR. Such sample selection criteria could cause the aggregate DVAR estimates to be signi...cantly biased downward.

To demonstrate the signi...cant aggregation bias driven by sample selection when the underlying population of ...rms are heterogeneous in size and import intensity, we conduct the following decomposition exercise relying on China’s 2004 ...rm census data, which covers the universe of all manufacturing ...rms and is therefore much larger than our original ...rm survey data set which only includes manufacturing ...rms with a minimum 5 million RMB

> 29 Results are available upon request.

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revenue.<sup>30</sup> The ...rst row of Table 3 shows the aggregate DVAR estimates based on the total population of ...rms from the manufacturing census in 2004. The estimated DVAR is 0.479. In the next row, we restrict the census sample to include only ...rms that overlap with our original manufacturing survey. The estimated DVAR dropped slightly to 0.478, which is not statistically di¤erent from the previous row. This con...rms that sample selection bias is not an issue in our manufacturing survey sample, despite the exclusion of ...rms with less than 5 million RMB revenue in our sample.<sup>31</sup> Listed in Row (3) is the IO table-based DVAR estimate of 0.408 from KWW12. Consistent with our results in previous section, the IO table-based DVAR estimate is statistically lower than the DVAR estimates in Rows (1) and (2), based on their respective bootstrapped standard errors. In Row (4), we further restrict the census sample, according to the sample selection criteria speci...ed in the Chinese IO table manual –...rms with over 300 million RMB revenue. The aggregate DVAR estimate drops to 0.453. Not only is the resulting DVAR estimate based on this large ...rm sample lower than the estimates in Rows (1) and (2), it is also not statistically di¤erent from the IO table-based estimate by KWW12 in Row (3), based on a standard error of 0.034 from bootstrapping with 100 repetitions. This exercise con...rms that DVAR decreases when samples that only include larger ...rms are used, due to ...rms’heterogenous input sourcing.

Thus, the result in Table 3 nicely shows that ignoring ...rm heterogeneity may lead to downward aggregation bias in the IO table-based approach. While there can be many reasons why our ...rm-based estimates and the IO table-based estimates of KWW12 are di¤erent, such

> 30 Unlike our industrial survey dataset, the census dataset does not provide direct information on ...rms’ costs of materials. We follow the guideline of the user manual of the census dataset to compute a ...rm’s cost of materials by substracting its total sales by its value added. As such, our DVAR estimates based on the census dataset is not directly comparable to the estimates in the previous sections based on the industrial surveys, which provide direct information on ...rms’costs of materials.

> 31Recall that our sample consists of ...rms from the Annual Surveys of Industrial Firms, which has sales cuto¤ of 5 million RMB (about 600,000 USD) and above, while the 2004 census covers all industrial ...rms.

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as di¤erences in methodology or estimation errors, this decomposition exercise focuses solely on the role of ...rm heterogeneity in explaining the wedge. Firm heterogeneity matters because ...rms of di¤erent sizes have di¤erent import intensities. By restricting the census sample to large ...rms according to the IO cuto¤ criteria, we are able to account for the di¤erence between our aggregate DVAR and that of the IO table-based estimate of KWW12.

# 3.3 Extension to Non-Processing and Aggregate Exports

The methodology we have developed above is suitable for pure exporters who export all their products, and that the products are produced by using up all the materials they have imported. It requires the condition that no ...nal products or imported materials may leak to the domestic economy. A lot of exporters that engage in global value chains should satisfy this condition, in the form of processing trade, such as garment producers in Bangladesh, Guatemala, and other emerging economies.

However, many exporters are not processing exporters. Unlike processing exporters, nonprocessing exporters do not export all their outputs. In addition, they often use some of their imported materials to produce goods for domestic sales. Thus, the condition that no ...nal output or imported materials leak to the domestic economy is not met. How ...rms split their imported inputs between production for domestic sales and exports is generally unknown.

To extend our methodology to measure the DVAR of the non-processing exporters, we need to make one proportionality assumption at the ...rm level: the allocation of the ...rm’s inputs to the production for exports is proportional to the share of exports in total sales, which we may infer from our industrial survey data. This assumption is equivalent to assuming that the DVAR is the same between exports and domestic sales of the ...rms. Our proportionality assumption will likely be non-binding if ...rms produce the same products for 17
both the domestic and export markets. In addition, it is considerably less restrictive than the industry-level proportionality assumption commonly made by existing studies, as we still allow ...rms to be heterogeneous in terms of their shares of exports in total sales.

Thus, the DVA and DVAR of a non-processing exporter are: where the superscript ‘O’stands for ordinary exports. Similar to processing exports, there are transactions between non-processing exporters and the rest of the economy. After the adjustment based on the proportionality assumption, we follow the same procedures as outlined in Table 1 to adjust the estimates of the DVAR, similar to what we did for processing exporters. We ...rst obtain imputed �<sup>F</sup> based on the estimates from KWW12. Then we identify imported capital based on the United Nations Broad Economic Categories (BEC) list of capital goods, and adjust for �<sup>K</sup> i<sup>.Finally, we drop excessive importers.However, unlike</sup> what we can do for processing exporters that export excessively, there is no corresponding ...lter we can use to drop the excessive ordinary exporters. Including them in the sample will result in an overestimation of the DVAR of ordinary exports. With this caveat in mind, our approach is transparent and general enough to be applied to estimate the DVAR of di¤erent types of exporting ...rms and thus countries with varying prevalence of processing trade.

We use the ground-up approach to measure the DVAR of Chinese aggregate exports, by taking the weighted average over the DVARs of processing and ordinary exports, with weights equal to the corresponding export shares.<sup>32</sup> As shown in Table 2, the average DVAR

> 32Here we measure the DVAR for single-industry exporters only. As we have done for processing exports, we can also do it for multiple-industry ...rms as well. The drawback is that excessive processing importers are identi...ed as those that have import-export less the 25th percentile of the DVAR of ordinary exporters in the same year, but not the same sector-year. These numbers are available upon request.

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of ordinary exports during the sample period is around 0.9, substantially higher than that of processing exports but consistent with similar ...ndings by KWW12. Moreover, the DVAR of ordinary exports has declined slightly between 2000 and 2007, from 0.92 to 0.90. However, given the small decline compared to the much larger increase in the DVAR of processing exports, coupled with the fact that the share of processing exports in China’s total exports has been stabilized at around 55% throughout the sample period, the DVAR of Chinese aggregate exports increased from 0.65 to 0.70 between 2000 and 2007 (see Figure 5, Table 2 and Figure A7 in the appendix). In short, China’s DVAR has increased signi...cantly in recent years, almost entirely driven by the rise in the DVAR in the processing export sector.

# 4 Time-series Trend of Firm DVAR

In this section, we provide reduced-form evidence of the time-series changes in ...rms’DVAR and other related variables. A formal analysis of the determinants of China’s rising DVAR will be presented in the next section. Given the ...nding in the previous section that the entire increase in the DVAR is caused by processing exports instead of ordinary exports, we will focus on providing ...rm-level evidence based on processing exporters only from this section and on. We start o¤ by estimating the following speci...cation at the ...rm level: where i stands for ...rm, t represents year, and �it is the regression residual. The ...rm and year ...xed e¤ects are �i and �t respectively, with the year e¤ect for 2000 dropped to avoid the dummy variable trap. Thus, positive and rising �<sup>0</sup> t<sup>s(i.e.0 < �</sup> t<sup>< �</sup> t+1<sup>;8t > 2000)will</sup> imply a within-...rm increase in the DVAR over time.

Control variables in Xit include a ...rm’s material-to-sales ratio, <u>PPY</u><sup>M</sup> <u>M</u><sup>itslabor</sup> � �it<sup>and</sup> 19
cost (total wages or the ratio of wages to total sales). The inclusion of a ...rm’s <u>PPY</u><sup>M</sup> <u>M</u> � �it<sup>is</sup> to examine whether the ...rm substitutes between domestic and imported materials, keeping the total material cost share constant, according to (5). Labor cost is included to verify the popular claim that increasing labor costs are a main reason behind China’s rising DVAR in exports. Controlling for <u>PPY</u><sup>M</sup> <u>M</u><sup>if�0</sup> t<sup>sarepositive,signi...cantandrising,while�0</sup> X<sup>sare</sup> � �it<sup>;</sup> not positive or insigni...cant, then it implies that the DVAR is rising within ...rms, due to a substitution of domestic materials for imported materials.

Table 4 presents our baseline results. Bootstrapped standard errors, clustered at the industry level, are used for all the regressions reported in this section. Column (1) shows positive, signi...cant, and increasing year ...xed e¤ects, suggesting that ...rms’DVAR is rising during the sample period. On average, ...rm DVAR increases by 15 percentage points between 2000 and 2007. This within-...rm increase is larger than the 9 percentage-point increase at the aggregate level (see column 4 in Table 2), implying that exiting ...rms have a higher DVAR than new entrants on average. In other words, the upward trend of the aggregate DVAR of Chinese exports is entirely driven by the rising DVAR among the surviving exporters, not due to the exit of low-DVA ...rms.<sup>33</sup> Furthermore, by controlling for the ...rm’s <u>PPY</u><sup>M</sup> <u>M</u> � �it<sup>,</sup> we con...rm that the rising DVAR is due to ...rms’ substitution of domestic for imported materials.

In column (2), we add the ...rm’s wage-to-sales ratio � PYwL �it<sup>as a control.The insigni...cant</sup> coe¢ cient on <u>wL</u><sup>thepredictionbasedon(4)thatonce</sup> <u>P</u><sup>M</sup> <u>M</u><sup>controlled</sup> � PY �it<sup>supports</sup> � PY �it<sup>is</sup> for, labor costs should not have any direct impact on a ...rm’s DVAR. Columns (3) to (5) show the same upward trend for three di¤erent samples –domestic exporters only, foreign-invested exporters only, and multi-industry exporters included. In column (6), we repeat the same

> 33According to Table A10 in the appendix, the exiting ...rms tend to be smaller in terms of sales and exports. Given that ...rm size and DVAR are negatively correlated, it is not surprising to see that the exiters have higher DVAR as shown in the table. Furthermore, to the extent that ...rm size proxies for ...rm productivity, it is also not surprising that these smaller and thus high-DVAR ...rms are more likely to exit.

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analysis using an un...ltered sample that includes both excessive importers and exporters. The magnitudes of the estimated year ...xed e¤ects are very close to those in column (2) when the ...ltered sample is used, suggesting that our ...ndings are not driven by the removal of excessive importers and exporters. In summary, we ...nd that the within-...rm increase in the DVAR is widespread and it is not driven by sample selection.

The within-...rm increase in the DVAR over time should arise from exporters’substituting domestic for imported materials, at both the intensive and extensive margins. To examine this claim, we estimate the following speci...cations: <u>P</u><sup>I</sup> <u>M</u><sup>I</sup> where<sup>theshareofimportedmaterialsintotalmaterialcostfor...rmiinyear</sup> P<sup>M</sup> M � �it<sup>is</sup> t, while ln(import_varietyit) stands for the (log) number of import variety, measured by the number of imported HS6-country pairs.<sup>34</sup> Firm ...xed e¤ects are denoted by �i and �i in the respective speci...cations, while �t and �t are the year ...xed e¤ects, with the year e¤ects for 2000 omitted to avoid the dummy variable trap. Control variables in Xit include ...rm’s wage-to-sales ratio, � PYwL �it<sup>,(log)capital-laborratio,ln</sup> � <u>KL</u> �it<sup>,andmaterial-to-sales</sup> ratio, <u>PPY</u><sup>M</sup> <u>M</u><sup>We include these controls to capture the e¤ects of changing labor costs and</sup> � �it<sup>.</sup> capital deepening of the ...rm on imports. The residuals for each of the speci...cations are �it and !it; respectively. If ...rms are using more domestic materials for imported materials, the year ...xed e¤ects are expected to be negative, signi...cant and declining (i.e., �t < �t�1 < 0 and �t < �t�1 < 0; 8t > 2000).

Column (1) in Table 5 shows that the share of imported materials is gradually declining

> 34The HS classi...cation has changed twice (2002 and 2007) during our sample period. We use the concordance ...le created by Cebeci et al. (2012) to de...ne a consistent set of varieties over time.

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<u>P</u><sup>I</sup> <u>M</u><sup>I</sup> within ...rms over time. In particular, ...rm’s P<sup>M</sup> M<sup>byabout17percentage</sup> � �it<sup>dropped</sup> points on average in 2007 compared to 2000. This result supports our ...nding that Chinese processing exporters are substituting more domestic materials for imported materials over time. Firm wage-sales ratio and capital-labor ratio do not appear to be related to its import share. The results remain robust when we split the sample into the domestic private and foreign ...rm samples (columns (2)-(3)) or include multi-industry ...rms (column (4)).

Consistent with the ...ndings that ...rms decrease their imports, Table 6 shows negative, signi...cant and declining year ...xed e¤ects, suggesting that on average, processing ...rms also import fewer input varieties over time. At the sample mean, the number of import varieties decreased by 0.35 log points in 2007 relative to 2000.<sup>35</sup> Other ...rm-level controls are insigni...cant. Columns (2) and (3) show that the decline mostly happens for foreign ...rms but not domestic private ...rms. The results remain robust to including multi-industry ...rms in the sample (column (4)). Along with the results from the previous tables, we ...nd that ...rms’average DVAR is rising through substitution of domestic inputs for foreign inputs, at both the intensive (the cost share of imported materials) and extensive margins (import variety).<sup>36</sup> For processing ...rms to substitute domestic for imported input varieties, an increased availability of the latter is expected. Unfortunately, data on domestic input variety in China are not available. To examine the phenomenon, we rely on the number of varieties exported by ordinary (non-processing) ...rms as proxies instead. Note that unlike processing exporters, ordinary exporters consist mainly of the indigenous Chinese ...rms that also sell in the do-

> 35In unreported results, we ...nd that most of the decline is due to ...rms importing fewer products (HS6) instead of importing from fewer countries.

> 36It is interesting to note that many of the dropped import varieties are parts and components from the neighboring countries, such as parts of refrigerators, computers, and electric conductors from Singapre and Japan, pick-up cartridges from Hong Kong SAR, China, iron and steels products from the Republic of Korea, and television cameras from Taiwan, China. Other varieties also include parts of electrical machines from Italy, and cathode-ray tubes from Germany. These observations are consistent with our hypothesis that processing exporters are substituting domestic for imported materials.

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mestic market. Some of these local ...rms become big and start exporting. By tracking the number of varieties exported by ordinary ...rms, we are picking up the tip of the iceberg as some of these domestic varieties may not make it to the foreign markets.<sup>37</sup> Nevertheless, the following evidence is insightful. Table A12 in the appendix lists 67 products that were imported by processing exporters and were not exported by ordinary exporters in 2000, but were exported by ordinary exporters in 2007. Some of them are important inputs used by large exporters across many industries, accounting for an import value of close to US$392 million. By 2007, not only were these products no longer imported by processing ...rms, ordinary exporters have started exporting them with a total value of over US$1.55 billion. These results suggest that processing exporters’demand for these imported products is now being met by local suppliers.<sup>38</sup> To verify that the decline in import variety is not due to exporters’specialization in their core competencies, we estimate the following speci...cation: where Xit includes � PYwL �it<sup>and</sup> � <u>PPY</u><sup>M</sup> <u>M</u> �it<sup>as in (11).Dependent variable,export_varietyitis</sup> measured by ...rm i’s number of exported HS6-country pairs.<sup>39</sup> Firm ...xed e¤ects (�i), year

> 37We use products produced by ordinary (non-processing) exporters to proxy for domestic variety, in the belief that a ...rm’s export product scope is a subset of its domestic product scope. There could be export varieties that were not sold domestically or vice versa. There could also be domestic varieties produced by non-exporters that were not exported. In these regards, our proxy should be considered as a lower bound of domestic variety.

> 38In the last column of Table A12 in the appendix, we also report the share of exports by foreign ...rms for each product in 2007. Out of the 67 products listed in the table, 15 products have over 20% of exports by foreign ...rms in 2007, and 5 products were exported solely by them. These results suggest that foreign ...rms may have moved into some of the intermediate good sectors in China. These results are also consistent with the assertions of recent studies, such as Autor et al. (2013) and Pierce and Schott (2015), that changes in policies in the U.S. and China may have encouraged foreign ...rms to o¤shore production to China, potentially contributing to China’s growing competitiveness. That said, the majority of these new export products are actually produced by indigenous domestic Chinese ...rms. We thank David Hummels for suggesting this exercise.

> 39We also repeat the same analysis using the number of HS6 (without the country dimension) to measure export variety. The results remain robust.

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...xed e¤ects (�t), and other ...rm controls are included as before. As Table 7 shows, despite the declining cost share of imported materials and decreasing variety, processing ...rms’export variety is rising over time, particularly after 2002, one year after China joined the WTO.

In summary, our results suggest that the domestic content in Chinese processing exports is rising over time. The rise is mainly driven by ...rms actively substituting domestic for imported materials, but not rising production costs. Chinese exporters have been expanding their product scope while reducing imports, both at the intensive and extensive margins.<sup>40</sup>

# 5 Determinants of Firm DVAR

In the rest of the paper, we will focus on studying whether China’s trade and FDI liberalization since 2000 could explain its rising DVAR. We ...rst develop a simple model to guide our empirical exploration of the determinants of the rising ...rm DVAR. This model focuses on the time-series movement of ...rms’DVAR and thus the aggregate DVAR, and deliberately abstains from explaining the cross-sectional di¤erences in the DVAR.<sup>41</sup>

## 5.1 A Simple Model

Recall the accounting identity (4): 40There can be concerns that the regression results are di¤erent between the two processing trade regimes, as described in Section 3. To this end, we repeat all four regression analysis using the sample of importand-assembly (IA) and pure-assembly (PA) ...rms, respectively. As reported in Table A11 in the appendix, results remain robust and quatitatively identical to the results reported so far. This is not surprising given that 90% of the observations in our sample belong to the IA regime. It is assuring to see that ...rm DVAR is also increasing within PA exporters. The magnitude of the coe¢ cients on the year ...xed e¤ects are similar. Similar trends are also found using this sample for other dependent variables of interest, though the statistical signi...cance may sometimes be smaller due to the much smaller sample of PA ...rms.

> 41In the Appendix, we derive a model that features a Cobb-Douglas production function, and show how ...rm heterogeneity in price-cost margins may lead to a cross-sectional variation in ...rm DVAR.

24
~~(—)~~ ~~(—)~~ ( ( ) a a Qa ~~a~~ (yo a (YC) ~~a~~ ()
unit material cost function with respect to the price of imported or domestic materials: Thus, when the unit cost function is translog, the share of imported materials in total materials is a log-linear function of the relative input prices: <u>t</u> where P<sup>P</sup> t<sup>D I</sup> is the ratio of the price index of imported input varieties to that of domestic input varieties. From (4), once we control for the share of materials in total sales,<sup>P</sup> Pt<sup>M</sup> itYMitit<sup>,...rm</sup> <u>t</u> DVAR depends only on P<sup>P</sup> t<sup>D I</sup> positively (given that �ID > 0): Thus, by assuming a translog cost function, we show that the only factor that a¤ects a ...rm’s DVAR is Pt<sup>Iaftercontrollingfortheshareoftotalmaterialcostintotalsales,</sup> Pt<sup>D,</sup> Pt<sup>M</sup> Mit<sup>Otherfactors,suchaswages,productivityandothercostsofproductiondonot</sup> PitYit<sup>.42</sup> directly enter (18), as long as the share of total materials in total sales is controlled for. We explore three obvious factors that can a¤ect ...rm DVAR, namely import tari¤s facing upstream suppliers, foreign direct investment (FDI), and exchange rates in the next section.

> 42Note that it is the ...rm’s total sales in the denominator, not output. Thus, a ...rm’s mark-up, which we do not aim to estimate, is already embedded in the formula.

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In addition to its ‡exibility of providing a second order approximation to any cost function, the translog cost function (15) has the advantage of not restricting the elasticity of substitution between domestic and imported materials to be a constant.<sup>43</sup> This modeling ‡exibility is particularly important since a rising ...rm DVAR could be driven by a rising elasticity of substitution between imported and domestic input varieties. By using a translog speci...cation, we let the data reveal whether and how the elasticity was changing over the sample period.

Speci...cally, let �t be the elasticity of substitution between domestic and imported materials in year t. According to Blackorby and Russell (1989), the elasticity of substitution between the two variables equals the cross-price elasticity �"<sup>ID</sup> t � minus the own price elasticity �"<sup>DD</sup> t �: In this case, using (15), we can express both "<sup>ID</sup> t and "<sup>DD</sup> t as functions of �ID and s<sup>D</sup> t<sup>:44</sup> which according to (19) gives since �ID > 0. We will be able to test these restrictions when we estimate �ID based on (17) and construct �t from (20). Note that �t could change over time (and across industries) due

> 43This property is in contrast with the case of a constant-elasticity-of-substitution (CES) production function. Readers are referred to the Appendix for a derivation of a ...rm’s DVAR when the production function is Cobb-Douglas.

> 44See Kee, Nicita and Olarreaga (2008) for the derivation.

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to changing s<sup>D</sup> t<sup>.Beforediscussingourestimationof�tindetaillater,letusreturntothe</sup> <u>t</u> discussion about the determinants of<sup>P I</sup> and thus a ...rm’s DVAR. Pt<sup>D</sup>

# 5.1.1 Exchange Rates

One obvious factor that could cause ...rm DVAR to increase is the exchange rate. De...ne the exchange rate, Et, as the foreign-currency price of a Chinese yuan. The price of imported materials in yuan is equal to the world price of foreign materials, Pt<sup>I�,dividedbyEt,i.e.,</sup> Pt<sup>I=P</sup> Et<sup>I</sup> t<sup>�.A yuan appreciation (a higher Et) decreases the yuan price of imported materials,</sup> possibly lowering ...rm DVAR according to (18):

# 5.1.2 Input Tari¤s Facing Domestic Input Suppliers

The relative price of materials could change due to the varying supply of input varieties. We assume that sector-level materials are CES aggregates of di¤erent varieties of domestic and imported inputs as follows: where Vt<sup>D</sup> and Vt<sup>Iarethenumbersofdomesticandforeigninputvarietiesavailabletothe</sup> ...rm. Let us assume that the elasticities of substitution, �, between any two varieties of imported materials, as well as between any two varieties of domestic materials, are constant.

28
will raise the relative price of imported materials, which in turn raise ...rm DVAR: The intuition is similar to the positive e¤ects of an increase in import varieties on aggregate productivity and welfare (e.g., Broda and Weinstein, 2006 and Feenstra and Kee, 2008).

What caused an increase in domestic and imported material varieties? We explore two factors previously explored in the literature. The ...rst factor is China’s gradual trade liberalization. Goldberg et al. (2010) show that in India, input tari¤ liberalization results in domestic ...rms’expansion of product scope. The main reason is that after trade liberalization, domestic ...rms have access to cheaper and new imported input varieties. Over our sample period (2000-2007), China experienced a continuous decline in import tari¤s and other trade restrictions, which was accelerated after the country’s accession to the WTO in December 2001. It is worth noting that such liberalization does not directly a¤ect processing ...rms, which have always been exempted from tari¤s on imported inputs. That said, tari¤ reduction could have a signi...cant impact on those non-processing ...rms that supply materials to the downstream processing exporters.<sup>45</sup> With access to new, cheaper, or better imported materials after tari¤ liberalization, non-processing ...rms experience lower production costs and may produce more varieties. Processing exporters in downstream sectors can now purchase these varieties domestically, replacing previously imported input varieties. This substitution at the extensive margin, as we will show below, plays an important role in driving the DVAR of the downstream processing exporters. More formally, let � t denote the (average) input tari¤ of the upstream industries. Tari¤ reduction may increase domestic input varieties, which in turn raise the relative price of imported materials and thus the

> 45As long as the imported materials stay inside the processing regime, domestic transactions are still exempted from tari¤s.

29
DVAR of downstream exporters. These relationships can be expressed as:

## 5.1.3 Foreign Direct Investment

The last factor is related to the rising FDI in the processing sector, as China increased its engagement in global value chains, due to its FDI liberalization since 2000.<sup>46</sup> Participating in global value chains has been proposed to be a new and e¤ective way of industrialization (Baldwin, 2012). In particular, Rodriguez-Clare (1996) and Kee (2015) show that more own-industry FDI can increase the demand for domestic materials, raising the supply and quality of domestic material varieties from the upstream industries.<sup>47</sup> Given �> 1 in our model, a higher demand by downstream exporters will lower the price of domestic materials, which in turn increase the DVAR for all exporters. More formally, we have The following section will empirically examine how the three factors discussed in this section shape the movement of ...rm DVAR.

# 5.2 Three-Stage Least Squares Regressions

Our model shows that factors such as exchange rates, FDI and upstream input tari¤s may raise ...rms’DVAR, through a¤ecting domestic input varieties and hence the relative price

> 46With China’s accession to the WTO in December 2001, the government has committed to a deeper and more comprehensive liberalization to FDI, though revising the Law on Foreign Capital Enterprises in October 2000. In particular, the revised law lifted the requirement for foreign enterprises to export the majority of their output.

> 47For example, FDI in the garment industry may increase the demand for domestic textile products and cause the domestic textile industry to increase their product varieties.

30
of imported materials. We ...rst empirically establish these channels without imposing the translog cost structure and let the data show the relationship between these variables. In the next section, we will formally estimate the translog parameters to assess how well our highly stylized model may explain ...rm DVAR.

We ...rst isolate the part of the within-...rm changes in the DVAR that is common across all ...rms within an industry, given that PPjtj<sup>DI</sup> t<sup>is industry-speci...c.To this end,we estimate the</sup> average within-...rm change in the DVAR by industry according to (11) and allow year ...xed e¤ects to be industry-speci...c: The estimated �jt, �<sup>^</sup> jt, captures the average within-...rm change in DVAR of each industry j in each year relative to 2000.

We then estimate the following system of three equations using 3SLS: where !<sup>1</sup> j<sup>, !2</sup> j<sup>, and !3</sup> j<sup>stand for industry ...xed e¤ects in three di¤erent equations, and �1</sup> jt<sup>, �2</sup> jt<sup>,</sup> and �<sup>3</sup> jt<sup>arethecorrespondingerrorterms.</sup> The ...rst equation uses the change in the price of imported materials relative to domestic materials, 4 ln PPj I<sup>D</sup> t ; to explain the within-...rm change in the DVAR that is common across � jt � all ...rms within an industry. The second equation explains how 4 ln PPj I<sup>D</sup> t can be caused by � jt � the change in the exchange rate, 4 ln Ejt, de...ned as the increase in the foreign price of the 31
yuan, and the change in domestic upstream variety, 4 ln Vjt<sup>D.The last equation explains how</sup> 4 ln Vjt<sup>Dcanbecausedbythechangeinown-industryFDI,4 ln FDIjt;thechangeinthe</sup> average input tari¤s facing ...rms in the upstream industry, 4e�<sup>U</sup> kt<sup>;and4 ln Ejt.Weinclude</sup> the exchange rate in (27) to test the hypothesis that a stronger yuan, in addition to a¤ecting import prices directly as speci...ed by (26), may also decrease the demand for domestic inputs as ...rms may choose to increase imported inputs. The ways that we measure imported input prices, domestic input prices, exchange rates, and domestic upstream variety are discussed in detail the Appendix. Our model predicts that !<sup>1</sup> p<sup>> 0in (25);!2</sup> E<sup>< 0and !2</sup> v<sup>> 0in (26);</sup> !<sup>3</sup> T<sup>< 0,!3</sup> F<sup>> 0,and!3</sup> E<sup>< 0in(27).</sup> Table 8 reports the results. Since �<sup>^</sup> jt are estimated with errors, bootstrapped standard errors (with 500 repetitions) are used in all equations. Column (1) shows a positive and signi...cant correlation between the relative price index of imported materials, PPjtj<sup>DI</sup> t<sup>,andthe</sup> average within-...rm change in the DVAR in the same industry. Column (2) presents the results of (26) ; which shows that controlling for industry ...xed e¤ects, upstream variety has a strong and positive in‡uence on the relative price of materials. On the other hand, the estimated coe¢ cient on exchange rate has a wrong sign, but is only marginally signi...cant with a t-stat of 1.66. At any rate, given that the average annual change in Ejt is close to zero during the sample period, the exchange rate is economically insigni...cant in a¤ecting the relative price of imported materials. This result suggests that empirically most of the changes in the relative price of materials during the sample period were driven by the expansion of domestic upstream variety and not necessarily due to exchange rate changes. Column (3) reports the estimates of (27). The result shows that all three factors (own-industry FDI, upstream input tari¤ liberalization, and the exchange rate) are statistically signi...cant in explaining the expansion of upstream domestic variety. In particular, the result that input tari¤ liberalization in the upstream industry is associated with an expansion of the variety 32
of upstream materials is consistent with the ...ndings by Goldberg et al. (2010). Over our sample period, Chinese ordinary exporters experienced a continuous decline in input tari¤s, which was accelerated by the country’s accession to the WTO in 2002. From 2000 to 2007, the average input tari¤ facing suppliers in the upstream sectors declined by about 55%. The coe¢ cient of -0.012 implies that the reduction in tari¤s is associated with a 0.7% increase in domestic input varieties, about one-...fth of the average increase across sectors from 2000 to 2007. It is worth noting again that processing ...rms are exempted from tari¤s for imported materials, so tari¤ reduction will not a¤ect their production costs directly but only indirectly through other general equilibrium e¤ects in the domestic economy. Tari¤ reduction leads to an increased supply of input varieties, which in turn lowers the average domestic material price and contribute to the rise in the DVAR of processing exporters. Likewise, the presence of own-industry FDI has a positive impact on the variety of upstream materials, supporting the ...ndings of Rodriguez-Clare (1996) and Kee (2015). Speci...cally, given that the average FDI stock in an industry is about 1.16 log-point higher in 2007 compared to 2000, the coe¢ cient of 0.017 implies that the increase in FDI in the downstream sectors is associated with a 2% increase in domestic input varieties.

Finally, the negative sign on 4 ln Ejt is consistent with the hypothesis that a stronger Chinese yuan will lead to more imported variety and thus less domestic variety due to import competition. However, during the sample period, the average annual change in Ejt is close to zero, implying that the exchange rate plays an economically insigni...cant role in the expansion of the domestic input market.

Overall, the results presented in Table 8 is consistent with our model, highlighting that the change in ...rm DVAR is driven by the changes in the relative prices of imported and domestic materials, due to the underlying expansion of domestic input variety, in response to the upstream input tari¤ liberalization and the increased presence of own-industry FDI 33
in downstream industries.<sup>48</sup>

# 5.3 Quantitative Analysis

In this section, we estimate our model structurally in order to assess how much of the change in the DVARs at the ...rm and aggregate levels can be explained by our model. We need to ...rst estimate the translog parameter, �ID: According to (18), a ...rm’s DVAR depends on the share of materials in total sales,<sup>P</sup> Pt<sup>M</sup> itYMitit<sup>;andthetranslogparameter,�ID,asfollows:</sup> The partial impact of a change in ln � PPt<sup>D</sup> <u>t</u><sup>I</sup> � on ...rm DVAR is With the estimate of �ID and the actual data on<sup>P</sup> Pt<sup>M</sup> itYMitit<sup>,wecancalculatehowmuchof</sup> the change in ...rm and industry DVAR is due to the change in the relative price as predicted

> 48Per a referee’s request, we have also checked whether FDI into upstream sectors could a¤ect the DVAR of downstream industries. To this end, we regress the change in the upstream input variety of an industry on the change in the weighted average of FDI across upstream industries, in addition to all the right-hand side variables included in column (3) of Table 8 (i.e., changes in own-industry FDI, upstream input tari¤s, and exchange rates). We ...nd that upstream FDI does not explain the increase in upstream input variety, while all the other variables remain signi...cant and have the same sign as those reported in Table 8. In particular, the coe¢ cient on upstream FDI presence is -.0038 and is not statistically signi...cant. This ...nding is consistent with our previous results that most of the new intermediate inputs were produced by indigenous Chinese ...rms and not foreign ...rms (see Table A12). Interpreting this result through the lens of our model would suggest that upstream FDI does not a¤ect the relative price of imported materials and hence industry DVAR. However, it is plausible that upstream FDI may have an independent e¤ect on industry DVAR, not through a¤ecting domestic input variety, but that is beyond the scope of our model and paper.

34
by our model: where the change in industry j<sup>0</sup> s DVAR equals the weighted average of the changes in the DVAR of all ...rms in industry j (i 2 �j), derived from (6) ; and j subscript is added to the relative price for clarity. In addition, with the estimate of �ID; we can also construct the elasticity of substitution between imported and domestic materials, �jt, for each industry j and year according to (20). Such estimates allow us to assess the time-series variation in �jt and examine whether the rise in ...rm DVAR is driven by an increasing �jt or not.

To estimate �ID, we estimate the following econometric counterpart of (17) : where ai is the ...rm ...xed e¤ect that subsumes �0I in (17) and �it is the residual. In other words, �ID is estimated from the within-...rm variation in the relative price between imported and domestic materials. Since the dependent variables are measured with errors, we bootstrap the standard errors (based on 500 randomly drawn samples). Moreover, we use the exchange rate indices, (log) FDI and (log) upstream input tari¤s of the sector as the instrumental variables for ln<sup>P</sup> <u>t</u><sup>I</sup> Pt<sup>D.</sup> Table 9 reports the estimated �bID, ...rm average share of imported materials in total b b material cost, and the implied �jt for 15 industries and both 2000 and 2007. Estimated �ID for all industries and years are positive and the resulting �<sup>0</sup> s are all greater than 1, satisfying 35
the theoretical restrictions speci...ed in (16) and (20). In addition, when the entire sample of ...rms is used, the F-statistics for the ...rst stage is highly signi...cant with p-value of 0 and thus passing the weak instrument test of Stock and Yogo (2005) by a wide margin.<sup>49</sup> . Likewise, across all industries, most of the F-statistics are larger than 100 with the minimum ...rst stage F-statistics is 44. Of the 15 industries, the IV estimates of �ID are signi...cant for 12 industries at the 1% signi...cance level. The estimated �t for the whole sample is 2.68 for 2000 and 2.83 for 2007. Both of them are statistically signi...cant at the 1% level. Of the 12 b b industries for which �2007 is signi...cantly di¤erent from 0, �2007 ranges from 1.90 for “plastic & rubber (HS2 = 39-40)”to 6.56 for “beverages and spirit (HS2 = 16-24)”. Even for the industries for which the estimates are imprecise, the coe¢ cients are positive, implying that the implied � is larger than 1. In other words, foreign and domestic input varieties are gross substitutes for processing exports in all industries in China. Based on the estimates of �jt for both 2000 and 2007, we perform simple t-tests and con...rm that �<sup>0</sup> jt<sup>sarestatistically</sup> constant within the sample period and for each industry.<sup>50</sup> Using these estimates, we can do the following back-of-the-envelope calculations. In 2007, the average (across industries) PPt<sup>D</sup> <u>t</u><sup>I</sup> is about 0.419 log-points higher that that of 2000. The estimated average b�ID for the pooled sample, based on the instrumental variables estimation, is 0.376; while the average (across ...rms) share of material cost in total sales in 2007 is 0.786. Using (28), the predicted increase in DV ARit is 0:376 � 0:786 � 0:419 � 12%, which is not statistically di¤erent from 14:7%, the estimated within-...rm increase in the DVAR from 2000

> 49According to Table 1 of Stock and Yogo (2005), the critical value of the ...rst stage F-statistics for the weak instruments test for three instrumental variables used for one endogeneous variable is 13.91, if the bias of the IV estimator is restricted to be no more than 5 percent of the OLS bias.

> 50To assess the time series movement of �; we test H0 : �2007 � �2000 = 0: T-tests are performed based on the following variance for � :

which is derived from (20). We construct the standard errors based on data from both 2000 and 2007. None of the t-statistics is statistically signi...cant. These test statistics are available upon request.

36
to 2007 as reported in Table 4.<sup>51</sup> Likewise, the predicted change in industry DVAR is also about 13%, according to (29), which explains fully the average increase in the industry DVAR during the sample period. This suggests that our simple translog model of using the relative price of materials (driven by upstream input tari¤, FDI, and exchange rates) to explain the ...rm’s and aggregate DVAR ...ts the data very well.

Let us summarize the main ...ndings of the paper. First, the DVAR of processing ...rms is increasing within ...rms across time in our sample and has led to an upward trend in both the industry and aggregate DVAR. Firm entry and exit do not explain the upward trend. Second, such an increase is mainly driven by ...rms substituting domestic for imported materials. Third, such a substitution is a response to the declining relative prices of domestic to imported materials caused by the expansion of domestic input variety. Fourth, the expansion of domestic input variety is induced by an increasing presence of foreign ...rms in processing exports and decreasing input tari¤s facing upstream suppliers. Fifth, based on the decrease in the relative price of domestic to imported materials, our model explains nearly all of the increase in the ...rm’s and aggregate DVAR from 2000 to 2007.

# 6 Concluding Remarks

This paper provides micro-level evidence of China’s rising ratio of domestic value added in exports to gross exports (DVAR). We use China’s customs transaction data over the 20002007 period to measure a ...rm’s DVAR and show how the increase in ...rm DVAR might explain the aggregate trend. We ...nd that the drastic increase in the DVAR of Chinese processing exports is observed across all industries and trading partners, and accounts for almost the entire rise in the DVAR of the country’s aggregate exports during the period.

> 51The 95% con...dence interval of �DV AR\ it is (11.2%, 13.6%), which overlaps with (11.3%, 18.0%), the 95% con...dence interval of the 2007 ...xed e¤ect in Table 4.

37
These ...ndings resonate well with the existing IO table-based studies, such as Koopman, Wang, and Wei (2012).

We exploit our ...rm-level data to con...rm that the increase in the DVAR not only exists within industries, but also within ...rms. Neither reallocation of resources across industries nor ...rm entry and exit contributes to the increase in the DVAR of aggregate exports. Firmlevel regressions show that the rising DVAR is due to an active substitution of domestic for imported materials by individual processing exporters. Such substitution is revealed at both the intensive margin, represented by a lower cost share of imported materials, and the extensive margin, manifested by decreasing import varieties. Behind this substitution is a continuous decline in the relative prices of domestic to imported input varieties.

We build a simple model to analyze the time-series determinants of a ...rm’s DVAR and show that during the sample period, the continuous tari¤ reduction facing upstream ...rms and the rising FDI since 2000 have contributed signi...cantly to the increase in domestic input varieties and thus the decline in their prices. These micro-level ...ndings provide comprehensive explanations about how Chinese exporters have expanded their activities along global value chains away from the ...nal stages of production. They also highlight that trade and FDI liberalization may actually raise a country’s DVAR, through input-output linkages and spillovers that go beyond the targeted industries.

While it is beyond the scope of the current paper, our approach is general enough to examine the micro-foundation and mechanism of a host of interesting economic issues. It can be used to study the relationship between ...rm DVAR and productivity, and shed light on the desirability for a developing nation to promote high value-added exports as a growth strategy. It can also be used to assess the validity of the proposal for emerging markets to “move up the value chains”or to raise the DVAR.

Regarding the last remark, we have started a new project on measuring the DVAR for a 38
wide range of countries, based on the matched importer-exporter customs transaction data from the World Bank’s Exporter Dynamic Database. Preliminary results show an upward trend in the DVAR for countries such as Bangladesh, Guatemala, Madagascar and Morocco. One common trait of these countries is their conducive trade and FDI policies that allow their participation in global value chains, similar to the case of China. This is clearly a promising avenue for future research.

# References

- [1] Ahmad, Nadim, Sonia Araujo, Alessia Lo Turco, and Daniela Maggioni (2013). “Using Trade Microdata to Improve Trade in Value-Added Measures: Proof of Concept Using Turkish Data.” in Trade in Value Added: Developing New Measures of Cross-Border Trade, Edited by Mattoo, Aaditya, Zhi Wang, and Shang-Jin Wei, World Bank.

- [2] Amiti, Mary, Oleg Itskhoki and Jozef Konings (2014). “Importers, Exporters and Exchange Rate Disconnect,”American Economic Review, 104(7): 1942-1978.

- [3] Antràs, Pol, Davin Chor, Thibault Fally, and Russell Hillberry (2012). “Measuring the Upstreamness of Production and Trade Flows.”American Economic Review Papers and Proceedings 102, no. 3: 412-416.

- [4] Antràs, Pol, and Davin Chor (2013). “Organizing the Global Value Chain.”Econometrica 81, no. 6: 2127–2204.

- [5] Autor, David, David Dorn, and Gordon Hanson (2013). “The China Syndrome: Local Labor Market E¤ects of Import Competition in the United States,”American Economic Review 103, no. 6: 2121-2168.

39
- [6] Baldwin, Richard (2012) “Trade and Industrialization after Globalization’s Second Unbundling: How Building and Joining a Supply Chain are Di¤erent and Why it Matters.” in Globalization in an Age of Crisis: Multilateral Economic Cooperation in the TwentyFirst Century. University of Chicago Press.

- [7] Blackorby, Charles and Robert Russell (1989). “Will the Real Elasticity of Substitution Please Stand Up? (A Comparison of the Allen/Uzawa and Morishima Elasticities),” American Economic Review 79(4), 882-888.

- [8] Blaum, Joaquin, Claire Lelarge, and Michael Peters (2014). "Estimating the Productivity Gains from Importing." Yale University Working Paper.

- [9] Brandt, Loren, Johannes Van Biesebroeck, Luhang Wang, and Yifan Zhang (2015). “WTO Accession and Performance of Chinese Manufacturing Firms,”Working Paper, University of Toronto.

- [10] Broda, Christian and David Weinstein (2006). “Globalization and the Gains from Variety,”Quarterly Journal of Economics, 121(2), 541-585.

- [11] Cebeci, Tolga, Fernandes, Ana, Freund, Caroline. and Martha Pierola (2012). “Exporter Dynamics Database,”World Bank Policy Research Working Paper 6229.

- [12] Constantinescu, C, Aaditya Mattoo and Michele Ruta (2015) “The Global Trade Slowdown: Cyclical or Structural?”World Bank Policy Research Working Paper, WPS 7158.

- [13] De La Cruz, Justino, Robert B. Koopman, Zhi Wang, and Shang-Jin Wei. (2011). “Estimating Foreign Value-added in Mexico’s Manufacturing Exports,” US International Trade Commission Working Paper 2011-04A.

40
- [14] Fernandes, Ana and Heiwai Tang (2012). “Determinants of Vertical Integration in Export Processing: Theory and Evidence from China,” Journal of Development Economics, 99(2), 396-414.

- [15] Feenstra, Robert (1998) “Integration of Trade and Disintegration of Production in the Global Economy,”Journal of Economic Perspectives, 12(4): 31-50.

- [16] Feenstra, Robert and Hiau Looi Kee (2008). “Export Variety and Country Productivity: Estimating the Monopolistic Competition Model with Endogenous Productivity,” Journal of International Economics, 74(2), 500–518.

- [17] Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova (2010). “Imported Intermediate Inputs and Domestic Product Growth: Evidence from India,” Quarterly Journal of Economics, 125 (4), 1727-1767.

- [18] Hummels, David, Jun Ishii and Kei-mu Yi (2001). “The Nature and Growth of Vertical Specialization in World Trade,”Journal of International Economics, 54:75-96.

- [19] Johnson, Robert (2014). Five Facts about Value-Added Exports and Implications for Macroeconomics and Trade Research, Journal of Economics Perspectives, 28 (2).

- [20] Johnson, Robert and Guillermo Noguera (2012). “Accounting for Intermediates: Production Sharing and Trade in Value Added,” Journal of International Economics, 86 (2), 224-236.

- [21] Johnson, Robert and Guillermo Noguera (2014). “A Portrait of Trade in Value Added over Four Decades.”Dartmouth College Working Paper.

- [22] Kee, Hiau Looi (2015). “Local intermediate inputs and the shared supplier spillovers of foreign direct investment.”Journal of Development Economics, 112, 56-71.

41
- [23] Kee, Hiau Looi, Nicita, Alessandro, & Olarreaga, Marcelo (2008). “Import demand elasticities and trade distortions.”Review of Economics and Statistics, 90(4), 666-682.

- [24] Koopman, Robert, Zhi Wang, and Shang-Jin Wei (2012). “Estimating Domestic Content in Exports When Processing Trade Is Pervasive,” Journal of Development Economics, 99:1, pp.178-89.

- [25] Koopman, Robert, Zhi Wang, and Shang-Jin Wei (2014). “Tracing Value-Added and Double Counting in Gross Exports.”American Economic Review, 104(2): 459-94.

- [26] Liu, Xuepeng (2013). “Tax avoidance through re-imports: The case of redundant trade,” Journal of Development Economics, 104: 152-164.

- [27] Ma, Yue, Heiwai Tang, and Yifan Zhang (2014). “Factor Intensity, Product Switching, and Productivity: Evidence from Chinese Exporters,” Journal of International Economics, 92 (2), 349-362.

- [28] Ministry of Commerce of China Regulations Concerning Customs Supervision and Control over the Inward Processing and Assembling Operation URL: english.mofcom.gov.cn/aarticle/lawsdata/chineselaw/200211/20021100053665.html

- [29] National Bureau of Statistics of the People’s Republic of China (2007). National InputOutput Survey Methods of China.

- [30] Pierce, Justin and Peter Schott (2015). “The Surprisingly Swift Decline of U.S. Manufacturing Employment,”Working Paper, Yale University.

- [31] Rodriguez-Clare, Andres (1996). “Multinationals, Linkages, and Economic Development,”American Economic Review, 86(4), 852-873.

42
- [32] Stock, James H and Motohiro Yogo (2005). “Testing for Weak Instruments in Linear IV Regression,”In: Andrews DWK Identi...cation and Inference for Econometric Models. New York: Cambridge University Press, 80-108.

- [33] Timmer, Marcel P., Abdul Azeez Erumban, Bart Los, Robert Stehrer, and Gaaitzen J. de Vries. (2014) “Slicing up global value chains.” Journal of Economic Perspectives, 28(2), 99-118.

- [34] United Nations (1999). Handbook of Input-Output Table Compilation and Analysis.

- [35] Wang, Zhi, Shang-Jin Wei, and Kunfu Zhu (2014). “Quantifying International Production Sharing at the Bilateral and Sector Level,”NBER Working Paper, 19677.

43
Figure 1: DVAR of Processing Exports (2000-2007), with 95% (Bootstrapped) Con...dence Intervals <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.6<br>.55<br>.5<br>DVAR<br>.45<br>.4<br><!-- End of picture text --> Table 1: Issues and Assumptions or Solutions

||Issues|Assumptions or Solutions|
|---|---|---|
|1|Domestic content in imported materials.|Negligible according to KWW (2012).|
|2|Imported content in domestic materials.|Lower DVAR by 1.5% - 5.7%.|
|3|Firms import capital equipment.|Remove equipment from ...rm imports.|
|4|Firms buy imported materials from ...rms.|Drop excessive exporters.|
|5|Firms sell imported materials to other ...rms.|Drop excessive importers.|
|6|Multi-industry ...rms hinder the calculation|Restrict the sample to single-industry|
||of industry DVAR.|...rms.|

<u>Table 2: Domestic Value Added Ratio</u>

|||Processing (P)||Ordinary (O)|Aggregate (A)|
|---|---|---|---|---|---|
|Year|DVAR (Filter 1)|DVAR (Filter 2)|DVAR (Filter 3)|DVAR (Filter 1)|DVAR (Filter 3)|
|2000|0.487|0.475|0.459|0.924|0.650|
|2001|0.495|0.488|0.468|0.915|0.652|
|2002|0.517|0.505|0.488|0.918|0.668|
|2003|0.502|0.494|0.478|0.914|0.661|
|2004|0.539|0.531|0.507|0.900|0.674|
|2005|0.579|0.571|0.544|0.893|0.695|
|2006|0.565|0.558|0.520|0.904|0.697|
|2007|0.599|0.587|0.548|0.900|0.701|

Notes: Filter 1: Include exporters that have material > imports, exports >= imports. Filter 2: Include exporters that satisfy Filter 1 and DVAR < 50<sup>th</sup> Pct(DVARO). Filter 3: Include exporters that satisfy Filter 1 and DVAR < 25<sup>th</sup> Pct(DVARO). DVARA= Processing_Shr�DVARP+(1-Processing_Shr)�DVARO 44
Figure 2: DVAR Trend (2000-2007) by Industry with 95% (Bootstrapped) Con...dence Intervals <!-- Start of picture text --> 04:beverages & spirit 06:chemical products 07:plastics & rubber 08:raw hides & skins<br>09:wood & articles * 10:pulp of wood 11:textiles 12:footwear & headgear, etc.<br>13:stone, plaster, cement, etc. 14:precious metals 15:base metals * 16 : machinery, mechnical & elec eqm t<br>2000 2002 2004 2006<br>17:vehicles & aircrafts 18:optical, photographic, etc. 20:misc manufacturing<br>2000 2002 2004 2006 2000 2002 2004 2006 2000 2002 2004 2006<br>year<br>Dashed lines = 95% confidence interval. * = industries with average DVAR lower in 2007 than 2000.<br>1<br>.5<br>0<br>1<br>.5<br>0<br>1<br>.5<br>0<br>1<br>.5<br>0<br><!-- End of picture text --> Figure 3: Decomposing the DVAR Growth into Within- and Between-industry Growth <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>within between<br>total change<br>.1<br>.08<br>.06<br>.04<br>.02<br>0<br>-.02<br><!-- End of picture text --> 45
Figure 4: DVAR of China’s Exports to its Top 5 Trading Partners <!-- Start of picture text --> Germany Hong Kong, SAR Japan<br>2000 2002 2004 2006<br>Korea United States<br>2000 2002 2004 2006 2000 2002 2004 2006<br>year<br>Graphs by two-digit iso code<br>.55<br>.5<br>.45<br>.55<br>DVAR of Exports<br>.5<br>.45<br><!-- End of picture text --> Figure 5: DVAR of China’s Aggregate (Processing + Ordinary) Exports <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>DVAR (Processing + Ordinary Exp) DVAR (Processing Exp)<br>All firms with materials < imp & exp < imp, and processing firms with DVAR>DVAR(25 percentile of Ord Exporters) are excluded.<br>.75<br>.7<br>.65<br>.6<br>.55<br>.5<br>.45<br><!-- End of picture text --> 46
Table 3: Decomposition Exercise: Firm Heterogeneity and Aggregation Bias

|DVAR of Total Exports|Number of ...rms in the sample|
|---|---|
|(1) Census<br>0.479 (0.021)|3419|
|(2) Original Sample<br>0.478 (0.023)|2623|
|(3) KWW (2012) Estimates<br>0.408|N/A|
|(4) Large Firms Only<br>0.453 (0.034)|123|

Notes: With the exception of (3), all numbers are calculated by the authors based on di¤erent samples.

(1) refers to the 2004 Census of Manufacturing Plants; (2) restricts the sample in (1) to the original survey dataset.

(3) is the IO table-based estimate from KWW (2012); (4) restricts the sample in (2) to only ...rms with total exports larger than 300 million RMB. Bootstrapped standard errors are reported in parentheses.

47
Table 4: Dependent Variable: The Ratio of Domestic Value Added in Exports to Gross Exports <u>(DVAR)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Sample|All|All|Dom private|Foreign|Multiple Ind|Un...ltered|
|�2001|0.0301***|0.0299***|0.0764|0.0327***|0.0256***|0.0268***|
||(0.007)|(0.006)|(0.080)|(0.006)|(0.005)|(0.005)|
|�2002|0.0490***|0.0493***|0.0810|0.0492***|0.0466***|0.0493***|
||(0.004)|(0.004)|(0.106)|(0.004)|(0.006)|(0.004)|
|�2003|0.0657***|0.0663***|0.190**|0.0656***|0.0709***|0.0681***|
||(0.008)|(0.008)|(0.078)|(0.008)|(0.005)|(0.005)|
|�2004|0.0669***|0.0674***|0.140|0.0677***|0.0749***|0.0715***|
||(0.008)|(0.011)|(0.127)|(0.011)|(0.005)|(0.010)|
|�2005|0.0962***|0.0969***|0.198|0.0978***|0.117***|0.101***|
||(0.007)|(0.009)|(0.124)|(0.008)|(0.005)|(0.010)|
|�2006|0.135***|0.136***|0.257*|0.136***|0.146***|0.133***|
||(0.010)|(0.011)|(0.133)|(0.012)|(0.005)|(0.010)|
|�2007|0.147***|0.147***|0.300**|0.146***|0.161***|0.150***|
||(0.013)|(0.017)|(0.140)|(0.016)|(0.006)|(0.014)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|-0.0236***|-0.0234***|0.0190|-0.0230**|-0.0207***|-0.0108***|
||(0.007)|(0.008)|(0.060)|(0.010)|(0.006)|(0.004)|
|�wL<br>PY<br>�<br>it||-0.0010|0.0522|-0.0010|-0.0040|-0.0032|
|||(0.016)|(0.155)|(0.017)|(0.009)|(0.006)|
|N|17903|17871|858|16726|28925|31965|
|R-sq|.0729|.0733|.104|.074|.0955|.0597|

Notes: Firm and year ...xed e¤ects are always included. Data set: merged NBS-customs data. Columns (1) and (2) use the whole sample; columns (3) and (4) include only domestic private and foreign-invested ...rms, respectively. Column (5) includes ...rms that operate in multiple industries as well. Column (6) includes single-industry ...rms that do not satisfy our rules to ...lter ...rms that engage in indirect trade. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

48
Table 5: Dependent Variable: Share of imports in total materials

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.0237**|0.0538|-0.0241**|-0.0200***|
||(0.010)|(0.047)|(0.011)|(0.006)|
|�2002|-0.0278***|0.137**|-0.0293***|-0.0223***|
||(0.006)|(0.062)|(0.007)|(0.007)|
|�2003|-0.0674***|0.0761|-0.0695***|-0.0678***|
||(0.007)|(0.067)|(0.007)|(0.007)|
|�2004|-0.0837***|0.0813|-0.0852***|-0.0830***|
||(0.008)|(0.061)|(0.008)|(0.006)|
|�2005|-0.114***|0.0483|-0.115***|-0.116***|
||(0.010)|(0.066)|(0.009)|(0.006)|
|�2006|-0.155***|0.0312|-0.157***|-0.144***|
||(0.011)|(0.081)|(0.009)|(0.007)|
|�2007|-0.170***|-0.00236|-0.171***|-0.154***|
||(0.017)|(0.086)|(0.013)|(0.007)|
|�wL<br>PY<br>�<br>it|0.0336|0.417**|0.0328|0.0481|
||(0.042)|(0.190)|(0.039)|(0.042)|
|ln (K=L)it|-0.0035|-0.0252|-0.0040|-0.0040|
||(0.003)|(0.030)|(0.003)|(0.003)|
|N|17831|858|16688|28875|
|R-sq|.0898|.104|.0918|.0896|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

49
Table 6: Dependent Variable: ln(number of import varieties)

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.114***|-0.208*|-0.106***|-0.134***|
||(0.016)|(0.124)|(0.018)|(0.013)|
|�2002|-0.110***|0.216|-0.0990***|-0.128***|
||(0.016)|(0.284)|(0.016)|(0.016)|
|�2003|-0.217***|-0.0606|-0.208***|-0.240***|
||(0.029)|(0.419)|(0.026)|(0.016)|
|�2004|-0.274***|0.186|-0.267***|-0.279***|
||(0.039)|(0.352)|(0.035)|(0.015)|
|�2005|-0.342***|0.0535|-0.335***|-0.360***|
||(0.046)|(0.367)|(0.045)|(0.016)|
|�2006|-0.197***|0.122|-0.183***|-0.215***|
||(0.054)|(0.336)|(0.054)|(0.019)|
|�2007|-0.351***|0.131|-0.344***|-0.356***|
||(0.090)|(0.345)|(0.081)|(0.020)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|0.0144|-0.106|0.0171|0.0104|
||(0.025)|(0.332)|(0.019)|(0.020)|
|�wL<br>PY<br>�<br>it|-0.0327|0.899|-0.0374|-0.0608|
||(0.038)|(1.033)|(0.054)|(0.059)|
|N|17871|858|16726|28925|
|R-sq|.0571|.0609|.0589|.0565|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

50
Table 7: Dependent Variable: ln(number of export varieties)

|Sample|All|Dom private|Foreign|Multiple Ind|
|---|---|---|---|---|
|�2001|-0.0280|0.138|-0.0233*|-0.0272|
||(0.022)|(0.223)|(0.012)|(0.021)|
|�2002|0.0599|0.318|0.0712**|0.0729***|
||(0.042)|(0.221)|(0.029)|(0.020)|
|�2003|0.103**|0.479|0.107***|0.130***|
||(0.049)|(0.318)|(0.035)|(0.018)|
|�2004|0.124**|0.598**|0.126***|0.161***|
||(0.056)|(0.267)|(0.039)|(0.016)|
|�2005|0.210***|0.821***|0.210***|0.236***|
||(0.040)|(0.310)|(0.029)|(0.019)|
|�2006|0.286***|0.945***|0.283***|0.316***|
||(0.050)|(0.316)|(0.033)|(0.017)|
|�2007|0.275***|1.086***|0.267***|0.306***|
||(0.046)|(0.338)|(0.030)|(0.022)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|0.0130|0.182|0.0110|0.0017|
||(0.018)|(0.266)|(0.017)|(0.018)|
|�wL<br>PY<br>�<br>it|-0.0474|-0.126|-0.0517*|-0.0606*|
||(0.059)|(0.726)|(0.028)|(0.031)|
|N|17871|858|16726|28925|
|R-sq|.0399|.121|.0388|.0486|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS and customs data. Column (1) uses the whole sample; columns (2) and (3) include only domestic private and foreign-invested ...rms, respectively. Column (4) includes ...rms that operate in multiple industries as well. Bootstrapped standard errors, clustered at the industry level, are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

51
<u>Table 8: Determinants of the Within-...rm Increase in the DVAR</u>

||(1)|(2)|(3)|
|---|---|---|---|
|Dep. Var<br><br>|4t;00DV ARjt|4t;00 ln(P <sup>I</sup>=P <sup>D</sup>)jt|4t;00 ln<br>~~�~~<br>V <sup>D</sup><br>jt<br>~~�~~|
|4t;00ln<br>~~�~~<br>P <sup>I</sup>=P <sup>D</sup><sup>~~�~~</sup><br>jt|0.269***<br>(0.026)|||
|4t;00ln(Ejt) (RMB appreciation)||1.479*<br>(0.891)|-0.089***<br>(0.031)|
|4t;00ln<br>�<br>V <sup>D</sup><br>jt<br>�||17.108***<br>(3.177)||
|4t;00ln<br>�<br>e�<sup>U</sup><br>jt<br>�|||-0.012*<br>(0.007)|
|4t;00ln (FDIjt)|||0.017***<br>(0.002)|
|Industry Fixed E¤ects||||
|N|105|105|105|
|R-sq|0.030|0.106|0.006|

4t;00 is the operator that subtracts the variable of interest from its corresponding value in 2000. Bootstrapped standard errors (with 500 repetitions) are reported in parentheses. Coe¢ cients are estimated using 3SLS. Columns (1), (2), and (3) are third, second, and ...rst stages, respectively. * p<0.10; ** p<0.05; *** p<0.01.

52
||b�2007<br>2.705<br>6.434<br>2.475<br>1.897<br>2.986<br>2.478<br>4.198<br>6.046<br>3.413<br>1.669<br>2.025<br>2.611<br>2.191<br>4.140<br>2.444<br>2.863|||
|---|---|---|---|
||b�2000<br>2.566<br>8.600<br>2.671<br>1.901<br>2.683<br>2.201<br>3.955<br>5.165<br>3.405<br>1.894<br>1.905<br>3.758<br>2.114<br>2.626<br>2.398<br>2.569|is||
||b�IV<br>2007<br>2.826<br>6.561<br>2.539<br>1.892<br>2.552<br>3.967<br>4.083<br>6.313<br>3.418<br>1.570<br>2.208<br>2.639<br>2.213<br>4.212<br>2.434<br>2.619|ID: �ID <br>i¤s, and||
||b�IV<br>2000<br>2.678<br>8.779<br>2.745<br>1.896<br>2.316<br>3.411<br>3.848<br>5.385<br>3.411<br>1.762<br>2.067<br>3.806<br>2.135<br>2.663<br>2.388<br>2.363|imating �<br> input tar|0.01.|
|Varieties|s.e.<br>(0.017)<br>(0.170)<br>(0.066)<br>(0.069)<br>(0.110)<br>(0.434)<br>(0.183)<br>(0.066)<br>(0.062)<br>(0.154)<br>(0.166)<br>(0.118)<br>(0.027)<br>(0.076)<br>(0.050)<br>(0.061)|d when est<br> upstream|5; *** p<|
|eign Input|�ID<br>0.351***<br>0.553***<br>0.296***<br>0.176**<br>0.403***<br>0.283<br>0.525***<br>0.891***<br>0.426***<br>0.142<br>0.202<br>0.287**<br>0.273***<br>0.396***<br>0.286***<br>0.335***|ways include<br>change rates,|.10; ** p<0.0|
|and For|s.e.<br>(0.019)<br>(0.211)<br>(0.072)<br>(0.058)<br>(0.112)<br>(0.529)<br>(0.180)<br>(0.066)<br>(0.059)<br>(0.121)<br>(0.155)<br>(0.091)<br>(0.024)<br>(0.069)<br>(0.048)<br>(0.057)|ects are al<br>ighted ex|nts. * p<0|
|en Domestic|�IV<br>ID<br>0.376***<br>0.566***<br>0.309***<br>0.175***<br>0.315***<br>0.568<br>0.506***<br>0.938***<br>0.427***<br>0.121<br>0.238<br>0.292***<br>0.278***<br>0.405***<br>0.284***<br>0.291***|Firm ...xed e¤<br>ding import-we|hese instrume|
|betwe|sD<br>2007<br>0.710<br>0.885<br>0.722<br>0.732<br>0.717<br>0.742<br>0.793<br>0.771<br>0.771<br>0.694<br>0.730<br>0.768<br>0.644<br>0.852<br>0.728<br>0.765|ntheses. <br>nts inclu|tails of t|
|stitution|sD<br>2000<br>0.661<br>0.921<br>0.770<br>0.734<br>0.603<br>0.620<br>0.769<br>0.690<br>0.770<br>0.802<br>0.664<br>0.882<br>0.571<br>0.580<br>0.713<br>0.691|ed in pare<br> instrume|for the de|
|Table 9: Estimated Elasticity of Sub|e<br>s & spirit (16-24)<br> products (28-38)<br>& rubber (39-40)<br>s & skins (41-43)<br> articles (44-46)<br>wood (47-49)<br>(50-63)<br> & headgear, etc. (64-67)<br>aster, cement, etc. (68-70)<br> metals (71)<br>tals (72-83)<br>ry, mechanical electrical & equipmt (84-85)<br> & aircraft (86-89)<br>photographic, etc. (90-92)<br>nufacturing (94-96)|tandard errors (with 500 repetitions) are report<br>g OLS, while �IV<br>ID is estimated using 2SLS with|of FDI in the same industry. See the Appendix|
||pl<br>ge<br>al<br>s <br>de<br>& <br> <br> <br>ar<br>pl<br>s <br>e<br>e<br>s <br>, <br>a|s<br>n|l|
||Industry<br>whole sam<br>04: bevera<br>06: chemic<br>07: plastic<br>08: raw hi<br>09: wood <br>10: pulp of<br>11: textiles<br>12: footwe<br>13: stone, <br>14: preciou<br>15: base m<br>16: machin<br>17: vehicle<br>18: optical<br>20: misc m|Bootstrapped<br>estimated usi|the (log) leve|

53
# 1 Appendix (Not for Publication)

## 1.1 Data Description

The main data set for this paper covers the universe of Chinese import and export transactions in each month between 2000 and 2007. It reports values (in US dollars) of a ...rm’s exports (and imports) at the HS 8-digit level (over 7000 products) to each destination (from each source) country. We drop trading companies (intermediaries) in our sample, using the methods proposed by Ahn et al. (2013) to identify them. This level of disaggregation is the ...nest for empirical studies in international trade –i.e., transactions at the ...rm-productcountry-month level.

Processing trade has been playing a signi...cant role in driving China’s export growth. From 2000 to 2007, processing exports have increased by over four folds from 138 billion USD to 680 billion USD with the share of processing exports in total exports held steadily around 55 percent, as shown in Figure A1. In addition, Table A1 shows that, the U.S. consistently ranked as the top destination, accounting for about 25 percent of Chinese total processing exports. Following the U.S. is Hong Kong SAR, China, which accounted for slightly over 20 percent of the total. Japan has been the third largest market for Chinese processing exports, but its prominence has declined from 18 percent in 2000 to 10 percent in 2007. Processing exports are widespread among China’s top 10 export destinations, as seen in Figure A2. It accounted for 63 percent of Chinese exports to the U.S. in 2007 and 81 percent for Hong Kong SAR, China, the highest share among the top 10 destinations.

We present in Figure ?? the share of processing exports in 2007 by industry sector, according to the United Nations groupings of HS2 categories. There exists a substantial heterogeneity in the prevalence of processing exports across industries. The share is about 20 percent for the “wood & articles” sector (HS2 = 6 -14) and is over 80 percent for the 1
“machinery, mechanical, and electrical equipment”sector (HS2 = 84-85).

The advantage of focusing on processing exporters is that we need not worry about their imports for ...nal consumption, as by de...nition, all imports in processing trade have to be used as intermediate inputs.<sup>52</sup> However, not all processing exporters import for their own use. Some of them import for other processing ...rms, which also implies that some processing ...rms must export more than what their imported materials can support. We develop systematic rules to identify processing ...rms that potentially import from and export for other ...rms. To this end, we merge the customs transaction data with the ...rm-level data from the Annual Surveys of Industrial Firms conducted by China’s National Bureau of Statistics (NBS hereafter). The surveys cover all state-owned enterprises (SOEs) and non-state-owned ...rms that have sales above 5 million yuan in a given year.<sup>53</sup> The NBS data contain detailed information for most of the standard balance sheet information, such as ...rm ownership, output, value added, industry code (480 categories), exports, employment, original value of ...xed asset, and intermediate inputs. Tables A2 and A3 present the percentages of ...rms and sales that are covered by the merged data. Table A4 presents the industry’s median of ...rm materials-to-sales ratios.

# 1.1.1 Transforming Chinese I/O Tables to One Based on UN Industry Code

1. Use the concordance from China’s National Bureau of Statistics to match multiple IO codes with multiple HS 6-digit codes (revision 2002).

2. Match multiple HS6 codes to multiple UN industry sector codes (20 of them).

> 52Manova and Yu (2013) examine how ...nancial constraints a¤ect exporters positions in global supply chains in China and thus their pro...ts. In this paper, we simply take advantage of the special features of the processing regime without getting into the details about ...rms’transition from one regime to another.

> 53The industry section in the o¢ cial statistical yearbooks of China is constructed based on the same data source. The unit of analysis is a ...rm, and not the plant, but other information in the survey suggests that more than 95% of all observations in our sample are single-plant ...rms. 5 million yuan is roughly exchanged to 600,000 US dollars during the sample period.

2
3. For each IO code, pick the UN code that has the largest number of HS6 shared. This will guarantee that all IO codes will be covered.

4. For UN codes that are matched with multiple IO codes, manually choose a unique UN code for the match. It happens in only one case.

5. Then add up the values of intermediate inputs for each pair of upstream-downstream relationship. A matrix of 20 groups by 20 groups will be built.

6. Recompute the IO coe¢ cients based on the UN industry sector classi...cation.

# 1.1.2 Computing Domestic Upstream Variety

To compute domestic upstream variety, we use the weighted average of the number of HS6 products exported by non-processing ...rms across all upstream industries as a proxy for domestic upstream varieties, since data on domestic varieties are not available. The belief is that a ...rm’s export product scope is a subset of its domestic product scope.<sup>54</sup> Speci...cally, we compute the weighted average of the number of upstream varieties by Vjt =<sup>PI</sup> i=1<sup>sijVit,</sup> where sij is the share of industry i<sup>0</sup> s goods used in total input costs of industry j, according to the Chinese input-output table for 2002. Vit is the number of HS6 products exported by non-processing ...rms in industry i in year t. Since the HS classi...cations have changed twice (in 2002 and 2007, respectively) during our sample period, we use the concordance ...le created by Cebeci et al. (2012) to de...ne a consistent set of varieties over time. As reported in Table A5, the number of varieties available to the downstream processing exporters is increasing over time for most industries. Some industries have systematically higher input varieties (e.g. machinery, mechanical, and electrical equipment). This industry-speci...c feature is already

> 54There could be export varieties that were not sold domestically or vice versa. There could also be domestic varieties produced by non-exporters that were not exported. In these regards, our proxy should be considered as a lower bound of the number of domestic varieties.

3
controlled for by industry ...xed e¤ects in the regressions.

# 1.1.3 Computing Upstream Input Tari¤s

Computing an industry’s upstream tari¤s involves two steps. For each upstream industry, input tari¤s are measured as a weighted average of tari¤s facing all input suppliers to that industry. Speci...cally, we obtain the share of industry i’s inputs in total material cost of industry j, sij, from the Chinese IO table for 2002. Then for each industry j, we compute e the weighted average of input tari¤s as � jt =<sup>PI</sup> i=1<sup>sij�it,where�itistheaveragetari¤ rate</sup> for industry i in year t and I is the total number of industries. Finally, for each downstream industry k, we use the IO coe¢ cients again to compute the weighted average of upstream input tari¤s e�<sup>U</sup> kt<sup>=PI</sup> j=1<sup>sjke�jt.TheideatousetheIOtablestwiceisthatweneedthe</sup> measure of tari¤s facing domestic input suppliers, not downstream exporters. For example, a garment ...rm uses fabrics, zippers and buttons. Fabrics ...rms use cotton yarns, zipper ...rms use steel, and button ...rms use plastics. Thus, the upstream input tari¤ for a garment ...rm is a weighted average tari¤ rates on cotton yarns, steel and plastics.

# 1.1.4 Computing Industry-speci...c Exchange Rate Indices

We use the Tornqvist method to construct an industry-speci...c time-varying exchange rate. For each industry j, let Ijt be the set of common countries ...rms in industry j import from in two consecutive years, t and t � 1: Denote country c’s currency price of a yuan in year t and t � 1 by Ect and Ect�1; and denote country c’s shares in industry j<sup>0</sup> s total imports in year t and t � 1 by scjt and scj;t�1. The industry-speci...c rate of yuan appreciation with respect to the countries from which industry j imports in year t is de...ned as 4
Using this weighted average of appreciation rates, we de...ne the industry-speci...c exchange rate for imports as with Ejt normalized to 1 in the base year (i.e., 2000) or any starting year for each industry.

# 1.1.5 Computing Industry-speci...c Domestic Input Price Indices

Computing the input price indices involves two steps. First, we use the Tornqvist method to construct an industry-speci...c time-varying domestic input price indices. For each industry j (15 of them), let Ijt be the set of common sub-industries in two consecutive years, t and t � 1: Denote sub-industry s’s output price index in year t and t � 1 by Pst and Ps;t�1; and denote the share of sub-industry s’s sales in industry j<sup>0</sup> s total sales in year t and t � 1 by !sjt and !sj;t�1. Data on output price indices at the 4-digit sector level (based on China’s NBS classi...cation) are obtained from Brandt et al. (2012).<sup>55</sup> The industry-speci...c rate of output price in‡ation in year t is de...ned as Using this weighted average of in‡ation rates, the sector-speci...c output price level is de...ned as with P<sup>e</sup> normalized to 1 in 2000. jt The second step is to compute the weighted average of P<sup>e</sup> jt, with weights equal to the coe¢ cients from the Chinese IO table for 2002. The goal is to compute the average domestic prices facing processing ...rms in industry j. Speci...cally, for each industry j, the weighted

> 55http://www.econ.kuleuven.be/public/N07057/CHINA/appendix/

5
average of input prices is Pjt<sup>D=PJ</sup> k=1<sup>akj ePkt,whereakistheshareofindustrykgoodsin</sup> total material costs for production of a unit of industry j goods and J is the number of industries. Notice that Pjt<sup>DvariesacrosstimepurelyduetothevariationinPejt,sinceakjis</sup> ...xed throughout the sample.

# 1.1.6 Computing Industry-speci...c Imported Input Price Indices

To compute the imported input indices, we use the Tornqvist method to construct an industry-speci...c time-varying import price indices based on ...rm-level imports from the customs transaction data. For each industry j (15 of them), let Ijt be the set of common product (at the HS 8-digit level) in two consecutive years, t and t � 1: Denote product s’s import prices in year t and t�1 by p<sup>I</sup> st<sup>and pI</sup> s;t�1<sup>; and denote the share of product s’s imports</sup> in industry j<sup>0</sup> s total imports in year t and t � 1 by $sjt and $sj;t�1. Product-level import prices (by processing ...rms only) are computed as total import value divided by total quantity of import at the HS8 level, using customs transaction-level data. Then sector-speci...c rate of import price in‡ation in year t is de...ned as Using this weighted average of in‡ation rates, the sector-speci...c import price level is de...ned as with P<sup>e</sup> jt<sup>Inormalizedto1in2000.TableA6reportstheratiooftheimportedmaterialprice</sup> index to the domestic material price index across industry-years.

6
@ ( ) ( ) ° ; ; ; <u>a</u> ) (( ) 7 ( ) 7) <u>*</u> ( ) ~~ole~~ ) ~~&) j)~~
~~— al) @) ©~~ e <!-- Start of picture text --> gy<br>a -x<br><!-- End of picture text --> x ~~—___~~ x <!-- Start of picture text --> ( oS oe<br># ay<br>x lu lu<br><!-- End of picture text --> x
Solving it gives the following ratio of imported material cost to total material cost: We can then express ...rm i’s DV AR in period t, based on (4), as According to (36), the determinants of a ...rm’s DV AR can be analyzed as follows:

1. Cross-sectional distribution of the DV AR within an industry-year

Given input prices and elasticities, the cross sectional distribution of DV AR within an industry-year depends on the distribution of ...rm’s price-cost margin, �i, given that DV AR is an a¢ ne transformation of �i. Thus, within an industry-year, a ...rm with a higher �i will have a higher DV AR. Factors that a¤ect the price-cost margin will therefore a¤ect ...rm DV AR:

- Perfect Competition

If the industry is perfectly competitive, �it = 0; 8i; t; the cross-sectional distribution of DV AR degenerates to the following constant that does not vary across ...rms:

- Monopolistic Competition with CES preferences

Under monopolistic competition with CES preferences, �it = �; 8i; since markup is constant across all ...rms, the cross-sectional distribution of DV AR degenerates to the following 9
constant that also does not vary across ...rms within the same industry: Note that the cross-sectional distribution of DV AR does not depend on the distribution of ...rm productivity under CES preferences, as long as markup is constant across ...rms. Empirically, if we observe varying DV AR across ...rms within the same industry-year, it indicates that the CES preference assumption is not supported and that the industry is likely not perfectly competitive.

2. Time-series movement of DV AR within ...rms Eq. (36) shows that the time-series movement of DV AR is determined by the price of imported inputs to domestic inputs, PPt<sup>D</sup> <u>t</u><sup>I,whichiscommonacross...rmswithinthesame</sup> industry-year. Factors that a¤ect PPt<sup>D</sup> <u>t</u><sup>I</sup> will a¤ect a ...rm’s DV AR over time. It is worth emphasizing that factors that do not a¤ect PPt<sup>D</sup> <u>t</u><sup>I</sup> directly, such as the ...rm’s wages (w) or productivity (�i), do not directly a¤ect the time-series movement of DV AR within ...rms.<sup>57</sup>

# References

- [1] Ahn, J., A. Khandelwal, and S.J. Wei (2011) “The Role of Intermediaries in Facilitating Trade,”Journal of International Economics, vol 84, 73–85.

- [2] Cebeci, Tolga, Fernandes, Ana, Freund, Caroline. and Martha Pierola (2012). “Exporter Dynamics Database,”World Bank Policy Research Working Paper 6229.

> 57Domestic wages can still indirectly a¤ect ...rm DV AR through a¤ecting the price of domestic materials. In the regression analysis below, controlling for the relative price of materials, we should expect no impact from wages on ...rm DV AR.

10
- [3] Koopman, Robert, Zhi Wang, and Shang-Jin Wei (2012). “Estimating Domestic Content in Exports When Processing Trade Is Pervasive,” Journal of Development Economics, 99:1, pp.178-89.

- [4] Manova, Kalina and Zhihong Yu (2013). “Firms and Credit Constraints along the Global Value Chain: Processing Trade in China,”NBER Working Paper 18561.

11
Figure A1: Share of China’s Processing Exports, 2000-2007 <!-- Start of picture text --> .56<br>.53<br>.55<br>.55<br>.55<br>.55<br>.55 .55<br>2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>Total Exports Processing Exports<br>Share of Processing Exports<br>1200<br>1000<br>800<br>Billion USD 600<br>400<br>200<br><!-- End of picture text --> 12
Figure A2: Shares of Processing Exports in China’s Top 10 Export Destinations (2000 & 2007) <!-- Start of picture text --> US HK JP KR DE NL GB SG TW IT<br>2000 2007<br>.8<br>.6<br>.4<br>Share of Processing in Exports .2<br>0<br><!-- End of picture text -->
Figure A3: Shares of Processing Exports by Industry Sector (2007) <!-- Start of picture text --> wood & articles (44-46)<br>stone & plaster (68-70)<br>chemical products (28-38)<br>base metals (72-83)<br>beverages & spirit (16-24)<br>textiles (50-63)<br>raw hides & skins (41-43)<br>misc manu. (94-96)<br>footwear & headgear (64-67)<br>vehicles & aircrafts (86-89)<br>precious metals (71)<br>pulp of wood (47-49)<br>plastics & rubber (39-40)<br>machinery/ mechanical/ elec equip (84-85)<br>optical, photographic (90-92)<br>0 .2 .4 .6 .8<br>Share of Processing in Exports<br><!-- End of picture text --> Figure A4: DVAR of Processing Exports - Di¤erent Filtered Samples (2000-2007) <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>Filter: Exp >= Imp & Material >= Imp Filter + DVAR < 25% DVAR_Ord<br>Filter + DVAR < Med DVAR_Ord<br>.6<br>.55<br>.5<br>.45<br><!-- End of picture text --> 14
Figure A5: Export Share of the Two Types Processing (2000-2007) <!-- Start of picture text --> 2000 2002 2004 2006<br>year<br>Import and Assembly Pure Assembly<br>.8<br>.6<br>exp_shr<br>.4<br>.2<br><!-- End of picture text --> Figure A6: DVAR of Processing Exports (Multi-industry Firms, 2000-2007) <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.6<br>.55<br>DVAR<br>.5<br>.45<br>.4<br><!-- End of picture text --> 15
Figure A7: DVAR of Aggregate Exports (Single-industry Firms, 2000-2007) <!-- Start of picture text --> 2000 2001 2002 2003 2004 2005 2006 2007<br>Year<br>Measured DVAR (Aggregate Exp) 95 c.i. (upper bound)<br>95 c.i. (lower bound)<br>.75<br>.7<br>DVAR<br>.65<br>.6<br><!-- End of picture text -->
Table A1: Top 10 Destinations of China’s Processing Exports

||2000||2007||
|---|---|---|---|---|
|Rank||USD (Bil)||USD (Bil)|
|1|United States|35.17|United States|152.51|
|2|Hong Kong SAR, China|31.02|Hong Kong SAR, China|150.00|
|3|Japan|23.17|Japan|60.25|
|4|Germany|5.62|Netherlands|29.08|
|5|Korea, Republic of|5.34|Germany|29.00|
|6|Netherlands|3.90|Korea, Republic of|26.70|
|7|United Kingdom|3.90|Singapore|19.04|
|8|Singapore|3.62|United Kingdom|17.41|
|9|Taiwan, China|2.92|Taiwan, China|13.22|
|10|France|2.10|France|11.81|

Source: China’s Customs Trade Data.

17
||% of customs<br>30.96<br>18.00<br>16.67<br>19.53<br>12.09<br>13.11<br>23.97<br>27.83<br>22.76<br>11.99<br>18.37<br>22.09<br>31.62<br>23.16<br>25.87<br>21.95|Survey.|
|---|---|---|
|porters|vations<br>...ltered<br>257<br>410<br>1190<br>678<br>77<br>337<br>4806<br>1329<br>226<br>219<br>786<br>4986<br>405<br>810<br>1391<br>17907|facturing<br>nalysis.|
|Numbers of Ex|Firm-year Obser<br>% of customs<br>42.89<br>40.39<br>37.20<br>35.77<br>26.53<br>46.85<br>37.99<br>45.18<br>40.38<br>24.42<br>40.32<br>41.73<br>48.95<br>34.62<br>36.35<br>39.36|ics (NBS) Manu<br>luded from the a|
|ent Subsamples by|Number of <br>merged w/ NBS<br>356<br>920<br>2656<br>1242<br>169<br>1204<br>7619<br>2158<br>401<br>446<br>1725<br>9420<br>627<br>1211<br>1954<br>32108|l Bureau of Statist<br>sectors and are exc|
|on of Di¤er|customs<br>830<br>2278<br>7139<br>3472<br>637<br>2570<br>20054<br>4776<br>993<br>1826<br>4278<br>22574<br>1281<br>3498<br>5376<br>81582|nd Nationa<br>ufacturing|
|Table A2: Representati|es & spirit (16-24)<br>l products (28-38)<br> & rubber (39-40)<br>es & skins (41-43)<br> articles (44-46)<br>wood (47-49)<br>(50-63)<br>r & headgear, etc. (64-67)<br>laster, cement, etc. (68-70)<br> metals (71)<br>tals (72-83)<br>ry, mech, elect eqmt (84-85)<br> & aircraft (86-89)<br> photographic, etc. (90-92)<br>nufacturing (94-96)|ina’s Customs Trade Data a<br> 2, 3, 5, and 19 are non-man<br>oled across 2000-2007.|
||Industry<br>04:beverag<br>06:chemica<br>07:plastics <br>08:raw hid<br>09:wood & <br>10:pulp of <br>11:textiles <br>12:footwea<br>13:stone, p<br>14:precious<br>15:base me<br>16:machine<br>17:vehicles <br>18:optical, <br>20:misc ma<br>Total|Source: Ch<br>Sections 1, <br>Sample po|

18
|ort Values|...ltered<br>% of customs<br>822<br>56.78<br>1308<br>29.72<br>6331<br>44.72<br>1843<br>27.77<br>217<br>30.17<br>1130<br>40.93<br>20168<br>47.71<br>10567<br>58.31<br>706<br>44.82<br>1616<br>12.15<br>4166<br>33.16<br>102399<br>45.81<br>17525<br>69.45<br>4155<br>41.38<br>6690<br>49.50<br>179641<br>46.03|acturing Survey.<br>nalysis.|
|---|---|---|
|amples By Exp|(million usd)<br>% of customs<br>72.02<br>58.71<br>67.36<br>63.25<br>60.48<br>69.66<br>70.04<br>73.57<br>71.92<br>73.97<br>51.25<br>67.66<br>78.40<br>80.06<br>66.97<br>68.72|s (NBS) Manuf<br>ded from the a|
|rent Subs|Sales<br>merged<br>1042<br>2584<br>9535<br>4199<br>434<br>1923<br>29606<br>13333<br>1133<br>9838<br>6439<br>151238<br>19782<br>8039<br>9050<br>268173|f Statistic<br> are exclu|
|Table A3: Representation of Di¤e|Industry<br>customs (mil usd)<br>04:beverages & spirit (16-24)<br>1447<br>06:chemical products (28-38)<br>4401<br>07:plastics & rubber (39-40)<br>14156<br>08:raw hides & skins (41-43)<br>6639<br>09:wood & articles (44-46)<br>718<br>10:pulp of wood (47-49)<br>2760<br>11:textiles (50-63)<br>42272<br>12:footwear & headgear, etc. (64-67)<br>18123<br>13:stone, plaster, cement, etc. (68-70)<br>1575<br>14:precious metals (71)<br>13299<br>15:base metals (72-83)<br>12562<br>16:machinery, mech, elect eqmt (84-85)<br>223527<br>17:vehicles & aircraft (86-89)<br>25232<br>18:optical, photographic, etc. (90-92)<br>10041<br>20:misc manufacturing (94-96)<br>13514<br>Total<br>390268|Source: China’s Customs Trade Data and National Bureau o<br>Sections 1, 2, 3, 5, and 19 are non-manufacturing sectors and<br>Sample pooled across 2000-2007.|

19
Table A4: Median of Materials to Sales Ratio by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.785|0.774|0.779|0.724|0.833|0.784|0.797|0.774|
|06:chemical products (28-38)|0.813|0.824|0.777|0.790|0.814|0.771|0.787|0.772|
|07:plastics & rubber (39-40)|0.806|0.791|0.791|0.799|0.830|0.806|0.798|0.798|
|08:raw hides & skins (41-43)|0.806|0.810|0.788|0.766|0.772|0.792|0.763|0.741|
|09:wood & articles (44-46)|0.801|0.788|0.769|0.741|0.776|0.801|0.796|0.815|
|10:pulp of wood (47-49)|0.800|0.796|0.778|0.785|0.818|0.799|0.769|0.771|
|11:textiles (50-63)|0.791|0.782|0.770|0.771|0.769|0.758|0.753|0.736|
|12:footwear & headgear, etc. (64-67)|0.795|0.778|0.754|0.770|0.763|0.745|0.749|0.720|
|13:stone, plaster, cement, etc. (68-70)|0.795|0.768|0.735|0.777|0.750|0.777|0.739|0.753|
|14:precious metals (71)|0.780|0.754|0.739|0.749|0.744|0.711|0.724|0.762|
|15:base metals (72-83)|0.826|0.817|0.797|0.782|0.812|0.791|0.787|0.810|
|16:machinery, mech, elect & eqmt (84-85)|0.800|0.803|0.773|0.773|0.804|0.796|0.780|0.780|
|17:vehicles & aircraft (86-89)|0.811|0.829|0.800|0.776|0.811|0.787|0.809|0.788|
|18:optical, photographic, etc. (90-92)|0.806|0.785|0.750|0.759|0.773|0.753|0.753|0.727|
|20:misc manufacturing (94-96)|0.796|0.776|0.757|0.764|0.783|0.755|0.758|0.761|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey.

20
Table A5: Upstream Variety Counts

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|01:live animals (1-5)|287.7|288.2|292.1|289.9|291.2|293.5|291.5|293.4|
|02:vegetables (6-14)|333.4|335.0|340.4|339.2|340.9|344.1|342.3|342.8|
|03:animal or vegetable oil (15)|294.2|294.5|297.9|295.6|296.4|299.2|297.3|298.0|
|04:beverages & spirit (16-24)|307.3|308.4|313.3|311.7|313.2|316.2|314.3|315.3|
|05:mineral products (25-27)|253.5|256.0|258.9|261.2|262.6|265.2|266.5|265.4|
|06:chemical products (28-38)|304.5|307.4|312.4|313.5|315.5|318.6|319.8|316.9|
|07:plastics & rubber (39-40)|263.6|263.6|268.4|268.1|270.9|273.2|273.6|272.1|
|08:raw hides & skins (41-43)|308.1|309.1|312.8|310.8|312.1|314.5|314.1|312.2|
|09:wood & articles (44-46)|186.2|188.2|192.3|192.0|194.1|195.2|193.6|193.2|
|10:pulp of wood (47-49)|202.6|205.3|207.3|209.4|209.6|213.3|210.8|209.8|
|11:textiles (50-63)|445.7|447.2|452.0|449.6|452.3|454.4|453.1|451.8|
|12:footwear & headgear, etc. (64-67)|374.5|374.6|378.6|376.5|379.5|381.1|380.4|378.6|
|13:stone, plaster, cement, etc. (68-70)|282.2|284.2|288.9|289.9|292.3|294.6|295.5|293.6|
|14:precious metals (71)|310.3|313.5|319.3|320.1|323.8|326.3|326.9|324.4|
|15:base metals (72-83)|348.7|352.7|359.5|361.0|366.4|369.0|370.4|367.4|
|16:machinery, mech, elect eqmt (84-85)|447.6|450.9|456.3|457.6|461.6|463.6|464.3|462.9|
|17:vehicles & aircraft (86-89)|296.4|297.3|302.6|304.7|308.1|309.4|310.9|311.0|
|18:optical, photographic, etc. (90-92)|421.6|424.6|430.7|430.9|435.7|437.6|438.3|435.8|
|20:misc manufacturing (94-96)|326.8|328.5|333.5|333.0|336.6|338.4|338.5|336.4|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey. Each variety is de...ned as a HS-6 digit product.

21
Table A6: Price Index of Imported Materials/ Price Index of Domestic Materials

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|1|0.980|0.975|1.075|1.067|1.092|1.187|1.220|
|06:chemical products (28-38)|1|0.981|1.028|1.145|1.219|1.385|1.564|1.657|
|07:plastics & rubber (39-40)|1|0.997|1.053|1.139|1.183|1.288|1.418|1.526|
|08:raw hides & skins (41-43)|1|1.000|0.997|1.098|1.125|1.192|1.279|1.355|
|09:wood & articles (44-46)|1|0.960|0.991|1.077|1.112|1.162|1.233|1.262|
|10:pulp of wood (47-49)|1|0.998|1.024|1.116|1.168|1.241|1.332|1.486|
|11:textiles (50-63)|1|0.995|1.004|1.087|1.108|1.153|1.228|1.253|
|12:footwear & headgear, etc. (64-67)|1|0.994|1.019|1.101|1.150|1.234|1.328|1.396|
|13:stone, plaster, cement, etc. (68-70)|1|0.996|1.007|1.095|1.197|1.356|1.510|1.659|
|14:precious metals (71)|1|0.985|0.960|1.048|1.094|1.208|1.316|1.403|
|15:base metals (72-83)|1|0.978|0.991|1.043|1.112|1.256|1.403|1.488|
|16:machinery, mech, elect eqmt (84-85)|1|1.021|1.115|1.237|1.305|1.431|1.572|1.890|
|17:vehicles & aircraft (86-89)|1|1.044|1.053|1.136|1.245|1.390|1.547|1.890|
|18:optical, photographic, etc. (90-92)|1|1.015|1.120|1.299|1.416|1.541|1.672|2.022|
|20:misc manufacturing (94-96)|1|0.992|1.009|1.105|1.175|1.286|1.413|1.563|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey. Both prices are normalized to 1 for year 2000.

Table A7: Percentage of Foreign Content in Domestic Materials

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.727|0.795|0.960|1.176|1.560|2.032|2.029|2.084|
|06:chemical products (28-38)|0.670|0.744|0.921|1.151|1.595|2.134|2.183|2.318|
|07:plastics & rubber (39-40)|0.386|0.433|0.544|0.691|0.975|1.312|1.374|1.466|
|08:raw hides & skins (41-43)|0.718|0.788|0.972|1.210|1.652|2.169|2.210|2.291|
|09:wood & articles (44-46)|1.110|1.209|1.465|1.826|2.518|3.353|3.352|3.493|
|10:pulp of wood (47-49)|0.892|1.012|1.286|1.680|2.389|3.211|3.374|3.549|
|11:textiles (50-63)|1.058|1.163|1.443|1.800|2.436|3.226|3.288|3.426|
|12:footwear & headgear, etc. (64-67)|0.927|1.027|1.290|1.631|2.263|3.023|3.133|3.293|
|13:stone, plaster, cement, etc. (68-70)|1.204|1.338|1.662|2.094|2.944|3.967|4.103|4.381|
|14:precious metals (71)|0.918|1.024|1.276|1.607|2.249|3.053|3.188|3.450|
|15:base metals (72-83)|1.146|1.282|1.602|2.026|2.857|3.907|4.122|4.511|
|16:machinery, mech, elect eqmt (84-85)|1.089|1.230|1.544|1.974|2.737|3.689|3.939|4.375|
|17:vehicles & aircraft (86-89)|1.414|1.586|1.981|2.528|3.564|4.855|5.134|5.657|
|18:optical, photographic, etc. (90-92)|0.730|0.820|1.028|1.311|1.829|2.466|2.617|2.877|
|20:misc manufacturing (94-96)|1.015|1.129|1.412|1.787|2.502|3.366|3.513|3.759|

Source: From Koopman, Wang, and Wei (2012) and authors’imputation based on the growth rate of the number of ordinary importers 22
Table A8: 25th-percentile of Ordinary Exporters’DVAR by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.909|0.928|0.897|0.884|0.876|0.922|0.911|0.931|
|06:chemical products (28-38)|0.880|0.906|0.895|0.942|0.880|0.914|0.904|0.915|
|07:plastics & rubber (39-40)|0.811|0.862|0.853|0.838|0.795|0.845|0.849|0.848|
|08:raw hides & skins (41-43)|0.792|0.846|0.876|0.894|0.870|0.792|0.803|0.777|
|09:wood & articles (44-46)|0.820|0.848|0.855|0.878|0.859|0.898|0.870|0.901|
|10:pulp of wood (47-49)|0.804|0.850|0.826|0.873|0.775|0.946|0.893|0.895|
|11:textiles (50-63)|0.802|0.852|0.855|0.873|0.858|0.890|0.893|0.891|
|12:footwear & headgear, etc. (64-67)|0.756|0.789|0.792|0.855|0.804|0.870|0.823|0.888|
|13:stone, plaster, cement, etc. (68-70)|0.942|0.889|0.912|0.907|0.861|0.876|0.877|0.892|
|14:precious metals (71)|1.000|1.000|1.000|1.000|1.000|1.000|1.000|1.000|
|15:base metals (72-83)|0.851|0.861|0.896|0.916|0.876|0.917|0.926|0.953|
|16:machinery, mech, elect eqmt (84-85)|0.830|0.833|0.841|0.893|0.836|0.900|0.910|0.915|
|17:vehicles & aircraft (86-89)|0.944|0.971|0.978|0.967|0.943|0.980|0.982|0.989|
|18:optical, photographic, etc. (90-92)|0.808|0.867|0.843|0.882|0.897|0.901|0.915|0.915|
|20:misc manufacturing (94-96)|0.730|0.804|0.892|0.901|0.899|0.912|0.932|0.923|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey.

Table A9: DVAR by Industry and Year

|Industry Sector||||Ye|ar||||
|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|
|04:beverages & spirit (16-24)|0.650|0.685|0.699|0.694|0.725|0.680|0.732|0.750|
|06:chemical products (28-38)|0.386|0.463|0.500|0.481|0.384|0.452|0.564|0.443|
|07:plastics & rubber (39-40)|0.418|0.458|0.364|0.403|0.357|0.507|0.417|0.443|
|08:raw hides & skins (41-43)|0.426|0.343|0.410|0.418|0.504|0.525|0.531|0.573|
|09:wood & articles (44-46)|0.438|0.604|0.445|0.289|0.552|0.594|0.347|0.390|
|10:pulp of wood (47-49)|0.304|0.401|0.395|0.393|0.452|0.547|0.562|0.515|
|11:textiles (50-63)|0.495|0.464|0.525|0.546|0.558|0.599|0.620|0.561|
|12:footwear & headgear, etc. (64-67)|0.590|0.571|0.613|0.663|0.628|0.657|0.686|0.693|
|13:stone, plaster, cement, etc. (68-70)|0.550|0.517|0.538|0.617|0.587|0.504|0.530|0.554|
|14:precious metals (71)|0.248|0.262|0.094|0.306|0.531|0.291|0.504|0.528|
|15:base metals (72-83)|0.525|0.468|0.545|0.477|0.556|0.356|0.426|0.491|
|16:machinery, mech, elect eqmt (84-85)|0.402|0.428|0.467|0.436|0.489|0.540|0.479|0.529|
|17:vehicles & aircraft (86-89)|0.501|0.657|0.507|0.628|0.554|0.617|0.721|0.767|
|18:optical, photographic, etc. (90-92)|0.469|0.530|0.509|0.529|0.463|0.574|0.641|0.558|
|20:misc manufacturing (94-96)|0.617|0.572|0.599|0.606|0.620|0.584|0.663|0.650|

Source: China’s Customs Trade Data and National Bureau of Statistics Manufacturing Survey DVAR is computed using single-industry ...rm sample and Filter 2 stated in Table 2.

23
|(8)<br>ln(expt�1)|-0.151***<br>(0.028)|15304|.0753|the relation between the<br> Bootstrapped standard||
|---|---|---|---|---|---|
|(7)<br>ln(salest�1)|-0.0461*<br>(0.026)|15299|.0944|(1)-(4) examine <br> of exiting ...rms.||
|(6)<br>DV ARt�1|0.0374***<br>(0.005)<br>E¤ects|15304|.0828|data. Columns <br> characteristics||
|(5)<br>statet�1|0.0035**<br>(0.002)<br>ear Fixed|15274|.0148|nd customs <br> examine the||
|(4)|0.0617<br>(0.038)<br>-0.0073***<br>(0.002)<br>Industry-Y|15274|.0711|erged NBS a<br>s (5) and (8)||
|(3)<br>tt|0.0686*<br>(0.039)<br>-0.0039<br>(0.003)|15271|.0702|Data set: m<br>s. Column|0.01.|
|(2)<br>Exi|0.0606*<br>(0.035)<br>0.108***<br>(0.014)<br>-0.0088***<br>(0.002)|15274|.075|ays included. <br>bability of exit|p<0.05; *** p<|
|(1)|0.0680*<br>(0.039)<br>0.101***<br>(0.015)<br>-0.0035*<br>(0.002)|15271|.0737|ects are alw<br> and the pro|p<0.10; **|
|Var|t�1<br>ARt�1<br>alest�1)<br>xpt�1)<br>t<br>rols|||ar ...xed e¤<br>acteristics|ntheses. *|
||te<br> <br>s<br>e<br>it<br>t|||ye<br>ar|e|
|Dep|sta<br>DV<br>ln(<br>ln(<br>Ex<br>Con|N|R2|Note: Industry-<br>(lagged) ...rm ch|errors are in par|

24
Table A11: Import and Assembly versus Pure Assembly

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Dep. Var.|DVA|R|Imp/Ma|terial|ln(Exp|Variety)|ln(Imp|variety)|
|Sample:|IA|PA|IA|PA|IA|PA|IA|PA|
|Year Dummies:|||||||||
|2001|0.0298***|0.0237|-0.0232***|0.00386|-0.123***|-0.0524|-0.0366*|-0.0264|
||(0.007)|(0.032)|(0.007)|(0.033)|(0.019)|(0.101)|(0.022)|(0.088)|
|2002|0.0494***|0.0422|-0.0295***|0.0359|-0.114***|-0.0604|0.0601**|0.0335|
||(0.008)|(0.034)|(0.007)|(0.035)|(0.020)|(0.106)|(0.024)|(0.093)|
|2003|0.0682***|0.0618*|-0.0700***|0.00539|-0.224***|-0.0959|0.101***|0.119|
||(0.007)|(0.034)|(0.007)|(0.038)|(0.021)|(0.107)|(0.023)|(0.093)|
|2004|0.0706***|0.0486|-0.0917***|0.0271|-0.286***|-0.133|0.118***|0.217**|
||(0.008)|(0.032)|(0.008)|(0.044)|(0.022)|(0.106)|(0.024)|(0.096)|
|2005|0.0980***|0.100***|-0.118***|-0.0290|-0.349***|-0.221**|0.203***|0.228**|
||(0.008)|(0.034)|(0.009)|(0.047)|(0.024)|(0.107)|(0.025)|(0.105)|
|2006|0.140***|0.132***|-0.161***|-0.0467|-0.202***|-0.136|0.283***|0.285***|
||(0.008)|(0.038)|(0.010)|(0.045)|(0.025)|(0.106)|(0.029)|(0.102)|
|�wL<br>PY<br>�<br>it|-0.0044|0.0009|0.0270|0.251*|-0.0343|-0.231|-0.0417|0.0059|
||(0.016)|(0.065)|(0.052)|(0.136)|(0.055)|(0.226)|(0.037)|(0.241)|
|�<br>P <sup>D</sup>M<sup>D</sup>+P <sup>I</sup>M<sup>I</sup><br>PY<br>�<br>it|-0.0247***|0.0073|||0.0143|-0.0867|0.0097|-0.123|
||(0.009)|(0.058)|||(0.025)|(0.164)|(0.026)|(0.224)|
|ln(K=L)it|||-0.0037|-0.0071|||||
||||(0.005)|(0.011)|||||
|N|13062|1744|13040|1733|13062|1744|13062|1744|
|R<sup>2</sup>|.0686|.0459|.0867|.0579|.0647|.0208|.0419|.0372|

Note: Firm and year ...xed e¤ects are always included. Data set: merged NBS-customs data. IA and PA stand for import and assembly and pure assembly, respectively. Columns (1) and (2) use ...rm DVAR as the dependent variable; columns (3) and (4) use ...rm imports-to-materials ratio as the dependent variable; columns (5) and (6) use log of the ...rm’s export varieity as the dependent variable; columns (7) and (8) use log of the ...rm’s export varieity as the dependent variable. Bootstrapped standard errors are in parentheses. * p<0.10; ** p<0.05; *** p<0.01.

25
Table A.12: Products that used to be imported by processing exporters but not exported by ordinary exporters in 2000

|Rank|HS6 (96)|Description|Imp00|Exp07|% Exp07 by FIE|
|---|---|---|---|---|---|
|1|740200|Unre...ned copper; copper anodes|94775.05|1.785|1.5|
|2|530121|Broken and scutched|69219.71|73.338|0.0|
|3|740311|Re...ned copper - Cathododes|52945.12|115.669|0.0|
|4|510130|Carbonised|47167.51|4099.934|19.2|
|5|291733|Aromatic polycarboxylic acids|22195.56|71.764|63.5|
|6|740321|Copper alloys - Copper-zinc base alloys|13405.72|21.957|5.0|
|7|710610|Powder|10303.45|6269.82|47.3|
|8|291412|Acyclic ketones without oxygen function|9354.077|20100.525|13.8|
|9|740329|Other copper alloys|8589.997|250.009|1.2|
|10|410122|Other hides and skins of bovine animals|7923.013|409.437|91.7|
|11|30375|Other ...sh, excluding livers and roes|7108.482|403.583|18.1|
|12|470720|Other paper or paperboard|5220.848|57.024|0.0|
|13|750712|Tubes and pipes - of nickel|4757.735|1073.887|1.5|
|14|750511|Bars, rods and pro...les, of nickel|4255.77|87.14|0.0|
|15|721113|Not further worked than hot-rolled|3560.055|1737.362|0.0|
|16|400260|Isoprene rubber (IR)|3206.528|2492.855|0.6|
|17|870423|Other, with compression-ignition|2527.633|796856.69|8.4|
|18|481031|Kraft paper and paperboar|2410.466|2424.858|2.1|
|19|370120|Instant print ...lm|2332.919|351.927|0.0|
|20|370256|Other ...lm, for colour photography|2135.713|55.455|0.0|
|21|722530|Other, not further worked|2130.281|69535.009|10.3|
|22|40110|Of a fat content|2022.768|0.023|100.0|
|23|40410|Whey and modi...ed whey|1992.98|0.71|0.0|
|24|721020|Plated or coated with lead|1506.084|2511.163|0.9|
|25|540342|Other yarn, multiple or cabled|1413.818|80.048|7.3|
|26|530129|Flax, broken, scutched, hackled - other|1163.462|135.442|49.7|
|27|370510|For o¤set reproduction|1067.683|91.158|10.4|
|28|740312|Re...ned copper - Wire-bars|1028.783|0.455|100.0|
|29|370231|Other ...lm, without perforations|888.111|38.389|0.0|
|30|480240|Wallpaper base|772.938|6382.673|28.0|
|31|80221|Hazelnuts or ...lberts|617.869|5.9|0.0|
|32|50710|Ivory; ivory powder and waste|540.557|20.158|0.0|
|33|151329|Palm kernel or babassu oil|445.65|24.453|99.4|
|34|80211|Almonds - In shell|376.58|3.5|0.0|
|35|890392|Motorboats, other than outboard|360|607.729|0.0|
|36|841013|Hydraulic turbines and water wheels|300|2133.552|0.0|
||||t o|be continue|d to the next page|

26
|Rank|HS6 (96)|Description|Imp00|Exp07|% Exp07 by FIE|
|---|---|---|---|---|---|
|37|293211|Compounds containing unfused furan ring|298.517|3480.953|62.8|
|38|30541|Smoked ...sh, including ...lletsi|268.626|51.527|15.2|
|39|290121|Unsaturated - Ethylene|228.697|53980.444|62.4|
|40|720450|Remelting scrap ingots|213.786|0.15|0.0|
|41|320120|Wattle extract|186.009|4.052|61.1|
|42|330112|Essential oils of citrus fruit|182.584|216.775|14.5|
|43|180320|Wholly or partly defatted|132.859|3.155|100.0|
|44|220860|Vodka|70.474|110.711|83.5|
|45|382313|Industrial monocarboxylic fatty acids|60.583|58.399|0.0|
|46|151229|Cotton-seed oil and its fractions|51.215|1788.796|55.8|
|47|520625|Single yarn, of combed ...bres|50.501|721.513|1.0|
|48|470319|Unbleached - Non-coniferous|40.203|97.423|0.0|
|49|271129|In gaseous state - Other|39.653|14.256|18.4|
|50|722720|Of silico-manganese steel|37.912|48480.139|17.5|
|51|180310|Not defatted|37.019|1449.275|51.3|
|52|550520|Of arti...cial ...bres|33.626|195.591|7.0|
|53|150300|Lard stearin, lard oil, oleostearin|32.134|1.57|100.0|
|54|20319|Fresh or chilled - Other|28.441|25052.286|0.0|
|55|292213|Amino-alcohols, their ethers and esters|25.68|58.781|0.0|
|56|711510|Catalysts in the form of wire cloth|18.672|0.432|0.0|
|57|151000|Other oils and their fractions|14.377|0.035|0.0|
|58|151521|Maize (corn) oil and its fractions|11.338|20758.875|22.8|
|59|151110|Crude oil|9.91|0.137|0.0|
|60|262011|Containing mainly zinc|7.8|226.859|0.0|
|61|180400|Cocoa butter, fat and oil|6.861|27570.497|45.3|
|62|270730|Xylole|6.047|41.119|0.0|
|63|630631|Sails - Of synthetic ...bres|5|1073.53|0.0|
|64|722592|Otherwise plated or coated w/ zinc|1.681|1002.997|0.0|
|65|252230|Hydraulic lime|1.344|11.135|0.0|
|66|310229|Ammonium sulphate; double salts|0.992|155.239|0.0|
|67|854340|Electric fence energisers|0.54|441628.86|25.1|
|Total|||392,126|1,546,760|16.63|

Imp00 is the value of imports by processing exporters in 2000, in thousands USD. Exp07 is the value of exports by non-processing exporters in 2007, in thousands USD.

27