Factory Asia: The Determinants of Multinational Activity in

the Context of Global Value Chains

Natalia Ramondo

UC-San Diego and NBER

May 27, 2016

Abstract

Traditionally, the literature has analyzed the determinants of horizontal activi-

ties of multinational firms—which entail serving the host market—vis-a-vis vertical

activities—which entail a form of production fragmentation and trade flows within—

and outside—the boundaries of the firm. For the most part, past analysis have been

restricted by data availability: When detailed data on affiliates’ activities were avail-

able, the coverage in terms of countries was small, and when data for a broad set of

countries were available, the detail provided on the affiliates’ activity was very low.

This paper brings new evidence on the determinants of the activity of affiliates

of multinational firms in Asia, in the context of global value chains. The analysis

relies on a unique firm-level data set provided by Dun & Bradstreet (D& B) with com-

prehensive coverage of the countries in the region. The key feature of the data that

makes the analysis possible is the information provided, at the firm level, on the firm’s

engagement in international trade activities; in this way, affiliates can be classified as

host-market oriented or exporters. The traditional country and industry determinants

of different types of Foreign Direct Investment (FDI)—comparative advantage, trade

costs, and economies of scale—can be tested in a very precise way for a large set

of countries and augmented by production fragmentation considerations (i.e., input-

output links) and measures of engagement of countries in the global value chain (e.g.,

measures of value-added trade and exports’ upstreamness).

1

1 Introduction

A major characteristic of the global economy is the importance and complexity of globalvalue chains (GVC), in which intermediate goods and services are traded across inter-nationally fragmented production processes. Multinational corporations (MNCs) are atthe center of such phenomenon, as the main organizers and coordinators of the GVC;cross-border trade of inputs and final goods takes place within the MNCs’ networks, con-tractual partners, and arm’s-length suppliers. According to UNCTAD (2013), GVC traderelated to MNCs account for around 80 percent of global trade. By various measures,countries in East and South-East Asia have participation in GVC well above the worldmean, as well as being a region with top MNC hosts and MNCs source economies, suchas China and Japan, respectively.

This paper brings new evidence on the determinants of the activity of affiliates ofmultinational firms in Asia, in the context of GVCs. The analysis relies on a unique firm-level data set provided by Dun & Bradstreet (D& B) with comprehensive coverage ofthe countries in the region. The key feature of the data that makes the analysis possi-ble is the information provided, at the firm level, on the firm’s engagement in interna-tional trade activities; in this way, affiliates can be classified as carrying host-market orexport-oriented activities. The lack of detailed information on trade activities of affili-ates was the main challenge faced by the literature so far. Having these data, the tradi-tional country and industry determinants of different types of Foreign Direct Investment(FDI)—comparative advantage, trade costs, and economies of scale—can be tested in avery precise way for a large set of countries and augmented by production fragmentationconsiderations (i.e., input-output links, as in Alfaro and Charlton, 2009) and measures ofengagement of countries in the global value chain (e.g., measures of value-added tradeand exports’ upstreamness).

Truly few papers in the literature can address the question of the determinants of affil-iates’ activity in such detail for so many countries. In general, the analysis has been donefor the United States, for which the data provided by the Bureau of Economic Analysis(BEA) are very detailed regarding the activity of U.S. affiliates abroad. A recent exampleof the usage of those data to address a question close to the one we address in this paper isBilir, Chor, and Manova (2014); they are interested in the effects of financial developmenton the activities of affiliates of U.S. MNCs—-horizontal vs export activities.

The importance of understanding the functioning of the GVC, and hence, the role ofits main actor, is undeniable for several reasons. First, the level of attachment and typeof engagement to the GVC seems to matter for development. UNCTAD (2013) reports

2

that countries with a higher growth in their participation in GVCs had a median growthrate of GDP per capita of 3.3 percent between 1990 and 2010, while the median countrythat did not increased its participation on GVCs grew at an average annual rate of lessthan one percent. Moreover, countries that not only increased their engagement in theGVC, but also did so with high value-added activities, grew the fastest.1 Second, theGVC is an important channel through which shocks get transmitted across countries. Asrecent papers have demonstrated, the increasing inter-dependence of economies throughsupplier linkages have created more synchronized business cycles. A natural disastershock, such as the Tohoku earthquake that hit Japan in 2011, created decreases in theproduction of affiliates of Japanese MNCs in the United States, as well as of other firmsin the U.S. economy related by networks to the Japanese affiliates, that went hand-in-hand with the decrease in imports from Japan (see Boehm, Flaaen, and Pandalai-Nayar,2015) . Finally, GVCs create different incentives along it for lobbying for trade policyas well as clearly differential effects of such policy within an industry. Two producersat different stages of the production process (e.g., input production and assembly) areindeed in conflict about which goods should be protected from imports; even two firmsin the same exact industry may end up with opposite interests in terms of trade policyonly because they made different choices regarding the location of the different stages ofproduction.2 Hence, the increasing—and type of—engagement of a country in the GVCmay re-shape the political process through which trade policy is made (see Blanchard,Bown, and Johnson, 2016).

Firm-level drivers as well as location and industry determinants of the activity ofMNCs linked to trade activities are key for policymakers in understanding the factorsinfluencing the participation of countries in the GVC and consequently, the attraction ofMNCs.

Traditionally, the activity of multinational affiliates has been divided into two typesof activities: Market-seeking activities (horizontal FDI) and low-cost-seeking activities(vertical FDI). Not only these two activities are non-exclusive, but the way MNCs useaffiliated and unaffiliated parties is also increasingly complex: It is not anymore aboutthe decision of where to produce/integrate an input used for production of a final goodat the headquarter; it is about how to integrate and where to locate a multi-dimensional

1GVC participation is measured by UNCTAD as the foreign content of a country exports plus the contentof exports consumed in foreign countries, while the domestic value added share of exports is the measureused for type of attachment to the GVC. In the following sections, we calculate some of these facts with ourdata.

2This is the case of Nike and New Balance, athletic footwear producers: While Nike produces and as-sembly all the footwear abroad, New Balance assembly—and sometime, fully manufactures—its shoes inthe United States.

3

GVC with final goods directed to markets all around the world. As transportation, ad-ministrative, and communication costs decrease, it is more and more feasible for the MNCto separate different stages of the production process across space, as well as from finalconsumers. As a result, vertical FDI should be understood more broadly as productionfragmentation not necessarily occurring between two parties of the same corporation, butalso between unrelated parties (think Apple-Foxconn). We will hence replace the term"vertical FDI" for the broader term "GVC FDI".

To be clear: Two elements of our data set allow us to carrying a very detailed analysisof GVC FDI. First, as mentioned above, having data on the engagement of each individualaffiliate on international trade allow us to analyze the motives of a firm for engagingin international trade activities, both imports and exports, vs pure horizontal activities.And second, the analysis of the presence and strength of fragmentation of productionacross borders is made possible by the availability of very detailed data on the industryof operation of parent firms and affiliates. Finally, it is worth mentioning that the datainclude the same detailed information for domestic firms; we will use those data in theanalysis.

2 Literature Review

Broadly speaking, traditionally, there is two explanations for the location choices of themultinational firm, which give raised to the traditional motives for FDI, horizontal andvertical, is large. Markusen (1984) focuses on the public non-rival nature of knowledge(an ownership advantage) within the firm. Hence, the MNC relied on exploiting a partic-ular form of economies of scale by replicating the same production activity across produc-tion locations; the blueprint, once developed, can be combined with immobile factors inmultiple locations by a single firm. This type of multinational activity, because it involvesreplicating the same activity in multiple countries, it is referred to as Òhorizontal FDI".

The proximity-concentration tradeoff theory arises from adding trade to the tradi-tional horizontal framework (Horstman and Markusen, 1992; Brainard, 1997). It explainsthe choice of overseas production over exports as motivated by proximity to consumers(either final consumers or other good producers downstream), or specialized suppliers,at the expense of scale: the choice is between lower variable costs from avoiding trade vshigher fixed costs from operating a new plant. The prediction of this theory is that FDIwill prevail over trade the higher transport costs and trade barriers, and the larger the

4

host market.3

Horizontal FDI theories assume similarity across countries in terms of factor price dif-ferences. In contrast, the theories that predict vertical expansion abroad (Helpman, 1984,1985) rely on differences in factor proportions across countries and on the assumptionthat headquarter and production activities have different factor intensities, in the spirit ofHeckscher-Ohlin models.

Carr, Markusen, and Maskus (2001) try to see how responsive affiliate sales to hori-zontal and vertical motives for FDI. They analyze the sensitivity of U.S. affiliate sales inthe host market to a set of country variables which the theory predicts should affect moreone type of FDI than the other. Their results suggest that affiliate sales are larger in coun-tries which are more similar to the U.S. in terms of size, decrease with the cost of investingin the host country, increase with the cost of exporting to the host country, and decreasewith the cost of importing back goods from the host country.

Recent developments have incorporate other considerations to the traditional motivesof FDI. In particular, Helpman, Melitz, and Yeaple (2004) have developed a model offirms that are heterogeneous in productivity and are faced to the proximity-concentrationtradeoff. The most productive firms in an economy will decide to open affiliates abroad,in line with the data, and industries with higher firm heterogeneity will show relativelymore firms deciding to become MNCs over exporters.

In the vertical FDI side, the game-changing work by Antras (2003) introduced contract-theory considerations into the sourcing decisions of firms. In this way, considerationsabout the institutional environment were incorporated. More precisely, firms in indus-tries with more relation-specific investments, in the words of Nunn (2007), are morelikely to keep the different stages of the production process within the boundaries of thefirm. The interaction between industries with different contract-intensive investmentsand countries with different institutional strengths (e.g., degree of contract enforcement)will give raised to predictions about the location of "vertical" affiliates.4

The work on vertical FDI points out directly already to the complexity of the globalvalue chain: Firms may choose to integrate some stages of production but not others,building a complex network of supplier within and outside the firm in different countries.Indeed, understanding the limitations to trade flows becomes then key to understand thesourcing and location decisions of MNCs.

3More precisely, the hypothesis predicts that we should observe more horizontal FDI the lower the ratioof scale economies at the plant relative to the corporate level. Economies of scale at the corporate level arisebecause there is a good of public nature (i.e. non-rival) within the corporation, such as knowledge.

4Helpman, Grossman and Szeidl (2006) introduce firm heterogeneity into a framework with three coun-tries in which firms choose to do vertical FDI in the South and horizontal FDI in the North.

5

In fact, another strand of the literature has been fully devoted to analyze the determi-nants of intra-firm trade, vis-a-vis arm-length trade. The analysis of the determinants ofintra-firm trade were carried as a direct test of Antras (2003)’s theory. Among others, onecan find papers by Hanson, Mataloni and Slaughter (2001,2005), Yeaple (2006), and Nunnand Trefler (2008, 2014), on the activity of U.S. multinationals. While the first two papersuse data from the Bureau of Economic Analysis (BEA) on the activity of U.S. affiliates andparents in terms of their sales and purchases, the third paper uses custom data from theU.S. Bureau of Census on related- and unrelated-party trade.

Latest developments have address with more sophisticated data the complex locationand sourcing decision strategies of MNCs with respect to the GVC. There are two strandsof literature in this regard: one devoted to construct sound statistics on value-added tradeas opposed to gross flows; and another one devoted to understand the activity of MNCsby combining data on their activity with information on the input and output links acrossindustries.

Alfaro and Charlton (2009) introduce vertical linkages by integrating input-outputrelationships into the analysis of affiliate location. They show that there is a tendency forparents to open "vertically linked" affiliates; that is, in an input-output sense, affiliates arevery close, or similar, to their parents. They conclude that vertical FDI is more prevalentthan previously thought. They cannot, however, provide direct evidence on the tradelinks between parents and affiliates. In fact, Ramondo, Rappoport and Ruhl (2015) show"vertically linked" affiliates are not necessarily sending inputs to the parent; input-outputlinks may be a poor proxy for intra-firm trade linkages. Their findings, however, point outto the complexity of the GVC; the MNC intertwine in-house and third party suppliers inall the stages of the GVC. The value of the results in Alfaro and Charlton (2009), confirmedby Ramondo et al. (2015) is, indeed, to show that there is a lot of production fragmentationbetween parents and affiliates within the MNC.

A more recent paper by Alfaro, Antras, Chor, and Conconi (2015) tries to tackle di-rectly the complexity of the GVC and how the MNC chooses to outsource or integratedits different stages. Using data on affiliates’ and parents’ industries together with theinput-output matrix, they tried to circumvent the lack of data on affiliates trade flows tomake inferences about the determinants of such decisions. By looking at all the industrycodes of the MNC, and comparing them with the ones needed to produce a final prod-uct, as dictated by the input-output matrix, they infer the inputs that the MNC may havechosen to outsource to third parties. They link these decisions of outsourcing vs in-houseproduction to the contractual environment of countries, specificity of investment of sec-tors, and elasticities of demands of the final good, in the spirit of the seminal work by

6

Antras (2003) and Antras and Chor (2013).

At the same time, an incipient literature devoted to build statistics on value-addedtrade aroused. The concern was that gross exports did not give an accurate picture ofwhich location was truly "producing" value. Given the increasing fragmentation of pro-duction, trade in inputs increased exponentially and with it, the back-and-forth and dou-ble counting of flows. An early attempt was Hummels, Ishii, and Yi (2001) who tried tomeasure the degree of vertical specialization by calculating the import content of exportsof a country. More recent developments started bringing into the picture world input-output tables. Johnson and Noguera (2012), Johnson (2014), Koopmans, Wang, and Wei(2014), Wang, Wei, and Zhu (2014) construct different measures of value-added trade,which we explain in detail below. Antras, Chor, Fally, and Hillberry (2015), and Antrasand Chor (2013) develop measures of the upstreamness of exports, both for a country andfor an industry (we will use these measures in the empirical analysis below).

Finally, it is worth mentioning that a very new literature has developed quantitativemodels of multinational activity that contemplate complex strategies for these firms. Byapplying statistical techniques introduced by Eaton and Kortum (2002), Ramondo andRodriguez-Clare (2013), Arkolakis, Ramondo, Rodriguez-Clare, and Yeaple (2015), Tintel-not (2015), and Alviarez (2015) build models in which firms not only replicate productionabroad to serve the host market, but also ship goods back home and third countries, aswell as receive goods from the parent company. The extension of Helpman, Melitz, andYeaple (2004) done by Irrarazabal, Moxnes, and Opromolla (2013) incorporates intra-firmtrade flows into the proximity-concentration tradeoff.

3 Theoretical Framework

The shape of the GVC is crucially determined by location decisions, and mode of entryinto markets, made by the MNC. Classical theories of the MNC are therefore useful tounderstand these choices. Typically, the motives for Foreign Direct Investment (FDI) havebeen divided in horizontal and vertical.

Horizontal FDI refers to the replication of the entire production process in anothercountry in order to saving on transportation costs and avoiding tariffs; the driver of suchFDI is to gain access to foreign consumers and the obvious alternative to reach such goalare exports. The theories of horizontal FDI have been based on the so-called proximity-concentration tradeoff by virtue of which firms tradeoff higher trade costs (incurred byexports) against higher fixed costs (incurred by opening an MNC foreign affiliate).

7

Vertical FDI refers to delegating part of the production process to an affiliate abroad,belonging to the same corporation; that is, the affiliate produces an input (upstream)for the parent (downstream). The driver of such FDI is typically factor price differencesacross countries. The MNC may also decide to outsource part of the production processto an independent foreign contractor; considerations about the contractual environmentof the industry and country are then important factors in shaping the in-house vs out-sourcing decision.5 Indeed, the accelerated rise of IT technologies have influenced thetraditional motives for FDI, deepening the fragmentation of the production chain, includ-ing its final step—the contact with consumers (see Fort, 2014).

Location and sourcing decisions of the MNC are taken with less and less constraintsfor at least three main motives: Countries eased restrictions to let foreign firms in, com-munication technologies became very sophisticated and widely spread, and trade costs,both shipping costs and trade barriers, decreased dramatically. One can think of the prob-lem of the GVC-MNC as one were the firm has to choose to minimize the unit cost of pro-duction of each stage of the production process, subject to some fixed costs of production.The choice set of costs contemplates any combination of country and supplier (within oroutside the firm), for each step of the GVC. Unit costs are conformed of labor wages at theproduction site, trade costs to/from the production site, and firm and aggregate produc-tivity in each location (aggregate productivity captures factors such as the institutionalenvironment of the country). In turn, fixed costs of setting up and maintaining activitiesare different depending on the country and type of supplier.

Assume there are two countries, Home (H) and Foreign (F). The production of a finalgood is composed of two stages: A first upstream stage (U) and a subsequent downstreamstage (D). For example, production entails firms the production of specialized inputs andthen the final assembly stage. To produce one unit of the final good aU units of inputsare needed at from the U-stage. Additionally, assembly requires aL units of labor per unitof the final good. Assume that the cost of a unit of input is CH and CF at Home andin Foreign, respectively, while wages are WH and WF in both countries. Additionally,shipping goods across locations is costly: for one unit of the intermediate or final goodto arrive at the other location, t ≥ 1 units must be shipped. Suppose that the size of theglobal market for the final good is 1 with a share of m for Home and 1 −m for Foreign.Fixed costs of setting up operations are denoted by F . We will abstract from issues relatedto the boundaries of the firm.

Figure 1 illustrates the choices presented to the firm in terms of production locations.

5The literature has used the term offshoring to refer to the international fragmentation of productioneither through vertical FDI or foreign outsourcing.

8

If both stages of production are kept at Home, the foreign market has to be served throughexports; if the production process is fully replicated abroad to serve that market, this ishorizontal FDI; if the production process is spliced between both location, this is GVCFDI; and if the whole production process is re-located abroad to serve both markets, thisis "factory-less" FDI.6 We compute the unit cost of serving each location. Concentrating

Figure 1: GVC Location Possibilities for the MNC

       

HOME   FOREIGN  

U  

D  

D  U  

D  

U  

GVC  FDI  

U   U  D   D  

horizontal  FDI  

Exports  

Factory-­‐less  FDI  

production at Home entails that the unit cost for the final good at Home is

peH = aUCH + aLWH + F, (1)

while in Foreign,peF = (aUCH + aLWH)t+ F. (2)

Replicating production in both locations entails the following unit costs for the final goodat Home and in Foreign, respectively,

phorH = aUCH + aLWH + F/m, (3)

andphorF = aUCF + aLWF + F/(1−m). (4)

6We assume that some HQ and coordination activities remain at Home.

9

Splitting production would entail unit costs of delivering the final good to Home of

pgvcH = (aUCHt+ aLWF )t+ 2F, (5)

and for ForeignpgvcF = aUCHt+ aLWF + 2F. (6)

If we assume that WF < WH and CH < CF , the option of producing inputs abroad andassembly at Home is never the most cost-effective.

Finally, concentrating production abroad entails unit costs given by

pfgfH = (aUCF + aLWF )t+ F, (7)

andpfgfF = aUCF + aLWF + F. (8)

Even without adding economies of scale, the problem delivers interesting results. First, ifthere is no differences in inputs costs and wages across countries, GVC-FDI will never bethe most profitable option. Hence, differences in factor prices across countries are neededin order to have GVC-FDI. Second, if trade were frictionless, we would never observehorizontal FDI as firms would always choose to produce each stage of production in thecheapest location; the contrary is true if trade costs were prohibitively high. Hence, thelower the trade cost, the most likely to observe GVC-FDI across countries.

Finally, adding economies of scale allows to derive implications related to the size ofthe markets and, by interpreting the fixed costs as entry costs, also implications relatedto the institutional environment of the hosting country. If the firm concentrates produc-tion in one location, it pays such cost only once, while if it splits production between—orreplicate it in—the two locations the fixed costs has to be paid twice. Centralizing pro-duction at Home or in Foreign entails to add to the unit cost of serving either Homeor Foreign the term F ; replicating production entails adding F/m and F/(1 − m) to theunit cost of serving Home and Foreign, respectively; and splitting production betweenthe two locations entails adding 2F to the unit costs of serving each market. Supposethat there is no differences in input costs and wages across countries. Again, GVC-FDIis never a profitable option. If additionally, markets are of similar size, considerations ofthe proximity-concentration tradeoff between exports and horizontal FDI are the ones inplay: how big is the fixed costs vs the trade costs; if on the contrary markets are of verydifferent size (e.g., in the extreme Home is m→ 1), horizontal FDI will never be an optionand only exports would be observe. Low trade costs and fixed costs together with high

10

differences in factor prices make GVI FDI more likely.

4 Empirical Analysis

We use two types of data for the analysis. First, the data provided by D&B give a com-prehensive picture of the activity of firms both directed to their host market and to exportmarkets, for MNC and non-MNCs in Asia, as well as detailed data on the industry of op-erations of parents and affiliates. Second, we need a measure of the degree of productionfragmentation and engagement of a country in the GVC, for a given industry. the vari-ables related to the fragmentation of production across countries in the aggregate will beborrowed from the literature on value-added trade.

4.1 Data

Data on the Activity of MNCs. We use firm-level data data from WorldBase, sold byDun & Bradstreet (D&B). These data provide a rich resource for very detailed measuresof MNC activity. This data set has been used in several academic papers (Alfaro andCharlton, 2009; Fajgelbaum, Grossman, and Helpman, 2015; and Alfaro et al., 2015, morerecently), and policy reports and books, such as "Synchronized factories — Latin Americaand the Caribbean in the Era of Global Value Chains" by the IADB (Juan S. Blyde, Editor).7

The unit of observation in WorldBase is the establishment, not the firm. As explainedin Alfaro and Charlton (2009), "establishments, like firms, have their own addresses, busi-ness names, and managers, but might be partly or wholly owned by other firms." We usea cross-section of establishments for the year 2015. For each establishment, we have thefollowing information:

• Industry code: four-digit SIC code of the primary industry in which the establish-ment operates;

• Country of operation of the establishment;

• Sales and/or employment; and

• International trade dummies: three variables indicating if the establishment onlyexports, only imports, or both exports and imports.

7See Alfaro and Charlton (2009) for an analysis of the coverage of Worldbase.

11

We restrict the sample to establishments reporting to a "global ultimate headquarter"(GUH)—e.g., there is no branches in our data; for some observations, the GUH and sub-sidiary is the same entity (i.e., these are most of the non-MNC establishments). Foreignestablishments are those that report a GUH located in a different country. Additionally,when an establishment identified as GUH is operating in the "holding companies" sector,whenever is available, we replace this primary industry code for the secondary industrycode. Finally, we are able to link establishments belonging to the same GUH.

We further restrict the sample to establishments in the mining, manufacturing, andbusiness services sectors, belonging to GUH in any sector. Our coverage in terms ofcountries is the following. We focus on establishments operating in the following Asiancountries: Afghanistan, Armenia, Australia, Azerbaijan, Bangladesh, Brunei, Cambodia,China, Georgia, Hong Kong, India, Indonesia, Japan, Kazakhstan, Korea, Kyrgyzstan,Malaysia, Nepal, New Zealand, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan,Thailand, Uzbekistan, and Viet Nam. On the origin side, we focus on GUH from thefollowing countries: Australia, Austria, Belgium, Brazil, Canada, Switzerland, Denmark,Spain, Finland, France, United Kingdom, Germany, Hong Kong, India, Indonesia, Ire-land, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Malaysia, Netherlands, Norway,New Zealand, Portugal, Russia Federation, Singapore, Sweden, Thailand, Turkey, Tai-wan, United States, and South Africa. For countries that are both in the destination andorigin list, we have observations for domestic establishments. Additionally, the data al-low for an analysis of outward FDI for some countries in Asia, particularly, China andIndia.

We are left with 230,130 observations. Table 1 presents a summary of the fractionof plants that are foreign and/or exporters, across sectors. The shares are in line withobservations in the literature: the fraction of exporters is almost 20 percent overall, butincreases to more than 60 percent among foreign plants and a lower 15 percent for do-mestic plants. As expected the highest percentages are found in manufacturing for whichoverall exporters represent a 24 percent of total plants. The number of foreign plants isalso sensible: They represent 10 percent of the total plants in our sample. These descrip-tive statistics pool all data together: Table 2 presents descriptive statistics by country.Notice that the large countries in the region (panel A) have both domestic and foreignplants. Thanks to this sub-sample of countries, we are able to carry the analysis includ-ing domestic exporters and, as shown below, make some quality checks on the data; forcountries in panel B, we only have foreign plants (i.e., plants with parents in a differentcountry). Some of those countries, which are the poorest in Asia, have very few datapoints; nonetheless, we will keep then in the analysis. It is reassuring that for China the

12

fraction of foreign plants that export is around 82 percent, a number very similar to theone reported by Defever and Riano (2015). Additionally, Japan has the lowest penetrationof foreign firms, as expected, and Viet Nam, one of the highest share of export-orientedforeign plants.

Data on value-added trade. Now, we turn to the description of the data on value-added trade. GVCs are associated with sequential production in which countries importintermediate goods, add value, and export those goods, final or intermediate, to anothercountry. The more fragmented the production chain is, the more trade in intermediategoods we should observe, as well as a little bit of value added in different locations.Studies have been done for very specific goods such iPad and iPhone (Dedrick, Kraemer,and Linden, 2008). Being only one good, the authors were able to calculate how mucheach participating country adds to the total final value of these goods. Such calculationsreveal that China, for instance, which has a high export flow in terms of gross value,only contributes to less than 4% in terms of value added to the total value of an iPad.This happens because the iPad is produced through a long sequential production chainthat involves many locations: China receives many of the parts to assemble the iPad fromother countries. Capturing this phenomenon more generally is not easy and requires verydetailed data.

The crudest measure of GVC participation is simply trade in intermediate goods, asa share of total trade, from country a to country b, in industry h. This measure captureshow much intermediate vs final good trade the country has under the presumption thatmore trade in intermediate goods reflects more fragmentation of production, and hence,a higher participation in GVCs.

An early attempt to measure "vertical specialization" (VS) was done by Hummels etal. (2001): they measured the use of imported inputs in the production of goods thatwill later be exported. Their measure of VS was, hence, the ratio of exports to imports(in inputs), in a country-industry; the lower this measure, the more VS across countries.Recent developments, introduced as a measure of engagement in GVC the foreign valueadded in a country’s exports (VAX): how much value added from other countries is em-bodied in a country’s exports . Papers such as Johnson and Noguera (2012) and Koop-man et al. (2014) have developed several of these measures using data on bilateral tradeflows together with input-output tables for different countries. VAX seeks to measure theforeign content (imports) in goods that are then exported abroad; in this sense, it is anupstream-oriented (or forward-linkage oriented) measure. A country that, on average, isparticipating in later stages of the production chain, will show up with a high VAX, as a

13

share of exports.8

In a more recent paper, Wang, Wei, and Zhu (2014) propose as a measure of GVCat the bilateral country-sector level the ratio of domestic value added that is exportedand stays abroad.9 The higher the share of valued-added that is exported and not re-exported, the lower the degree of vertical specialization. However, Wang et al. (2014)explain that VAX may not captured important features of the international fragmentationof production. Two countries, in a given industry, may have the same VAX ratio butfor very different reasons. For example, US and Chinese total electronics exports mayhave an identical VAX ratio, say 0.5. In the Chinese case, this is because half of Chinesegross exports reflect foreign value added (e.g., value added from Japan, Korea, and theUnited States). In contrast, for US exports, half of the gross exports are US value added inintermediate goods that are used by other countries to produce goods that are re-exportedto the United States. In this example, only half of the US value added that is initiallyexported is ultimately absorbed abroad; the US VAX ratio is 0.5 even if it does not use anyforeign value added in the production of its electronics exports. In this example, Chinaand the United States occupy very different positions in the GVC but the two countries’VAX ratios would not reveal this important difference.

Wang et al. (2014) decompose the gross value of exports in several terms: Domesticvalue added that is ultimately absorbed abroad (DVA) —an inverse measure of VS; for-eign value added used in the production for exports (FVA); returned domestic value, orthe portion of domestic value added that is initially exported and returned home embed-ded in imports (RDV); and pure double counted terms due to the back and forth inter-mediate goods trade (PDC). We use their measures of DVA and RDV, as a share of grossexports, which are available for 35 industries, including services industries, and 46 coun-tries. We use their measures in our analysis in two ways: First, we use them as dependentvariables to briefly analyze the country and industry determinants of the GVC; and sec-ond, we use them as explanatory variables in the empirical analysis of the determinantsof the activities of MNCs.

Descriptive statistics are presented in table 3. The interesting feature to remark here isthat both China and India have increased by 50 and 100 percent, respectively, in the last

8It is straightforward that the problem with the VAX measure is that countries specialized in goods (e.g.primary products) which are at the early stages of the supply chains, and hence, provide inputs to othercountries downstream, will show up as having very low foreign value-added in their exports. But this isnot necessarily an indication of the lack of a complex value chain. Other measures has been developedthat are downstream-oriented (or backward-linkage oriented). In particular, in Koopmans et al. (2014), theauthors calculate a measure of indirect value added. This measure shows the extent to which the exports (inintermediate inputs) of a country are used in exports of downstream sectors in other countries . Concretely,the variable indicates the percentage of a country’s exports used as inputs in the exports of other countries.

9This measure is always between 0 and 1.

14

15 years, the share of return domestic value (rdv) of their gross exports.

4.2 Other controls

Country-level variables used as controls in the analysis can be (broadly) divided in threegroups: comparative advantages, institutions, and integration factors. The first group ofcontrols includes variables such as difference in real GDP per capita between source andhost country, capital to labor ratio differences, share of skilled labor in the population,market size. The second group of controls includes indices of institutional quality (e.g.,rule of law, level of corruption) and financial development (e.g., private credit over GDP),as used by Rajan and Zingales (1998) and Manova (2013). Integration factors includeproxies for trade costs, such as distance and common "gravity" variables, regional tradeagreements, and tariffs. The degree of upstreamness of exports, for all sectors, in a givencountry, is also considered.

Industry variables are also included in some parts of the analysis and interacted withthe country variables. R&D intensity of the industry, as well as other variables such asskill and capital intensities—which indicate the "factor intensity" of the industry—are in-cluded. These variable are taken from the United States. Additionally, a variable thatindicates the position of the industry in the input-ouput chain (e.g. level of upstream-ness), or connectivity of two industries (e.g., direct requirement coefficients), is consid-ered. Finally, a variable that indicates the degree of investment-specificity that the indus-try requires (Nunn, 2007) as well as an index of the level of "routine tasks" of the industry,developed by Costinot, Oldenski and Rauch (2011) are added.

Table [TBA] has the complete list of these variables as well as their sources and cover-age years.

5 Descriptive Analysis

5.1 GVC Trade in the Aggregate

We start by showing some of the broad patterns observed in the data. We also illustratethrough a couple of examples how the measures of value-added trade work. Figure 2shows the evolution of exports in intermediate goods, as a share of total exports, andVAX ratios, an average across all the countries in our sample and ADB Asian members,respectively, and individual members.

15

Both the evolution of the VAX ratio and the intermediate trade shares hint at the samepattern: Vertical specialization, or fragmentation of production, across borders has dra-matically increased in the last twenty years. The same is true for ADB members in Asia,even though their average VAX ratio is 10 percentage points higher than the average inour sample. This trend, however, masks a high degree of heterogeneity across countries.Panels 2b and 2d show the same variables capturing vertical specialization, and com-pares two Asian countries, China and India, with two Latin-American countries, Mexicoand Brazil. Mexico presents the lowest VAX ratio of the group—while Brazil presentsthe highest—all throughout the period, but both are fairly stable. In contrast, China andIndia present drastic decreases in the VAX ratio indicating their increasing participationin GVCs. Looking at intermediate goods’ trade indicates that their share in total exportsonly has an upward trend for China, but not clear trend for the remaining three countries.

Next, in the spirit of Wang et al. (2014), we use the more detailed measures of value-added trade proposed in their paper (domestic and foreign value added of exports—DVA and FVA, respectively, and return domestic value—RDV). We start by documentingaggregate relations. Table 3 summarizes those measures. Additionally, this table showsaverages for some of the variables used later to capture trade costs.

The degree of export upstreamness of a country exports is positive related to the DVAshare; that is, if a country tends to produce earlier stages of the GVC, the more the domes-tic value-added of exports. Figure 3a shows the results. Figure 3b shows the relationshipbetween DVA shares and the rule of law of a country. The better the rule of law index,the smaller the share of domestic value-added in gross exports and the higher the foreignvalue-aded content of exports. Having a better institutional environment stimulates moreparticipation in the GVC.

Finally, to illustrate in more detail the information conveyed by these measures ofGVC, we take China in the "Electrical and optical equipment" sector. The DVA sharewhen from 73 percent in 2000, to 79 percent in 2014, while the FVA embedded in Chineseexports of electrical goods and optical equipment went slightly down from 20 to 17 per-cent in the same period. The share of returned value added (RDV) when sup from 0.8 to1.3 percent. The fact that China increased its domestic value-added for exports togetherwith a decrease in foreign valued-added and increase in returned value-added, suggeststhat China may have been changing its position in the GVC, from a low-cost location ofproduction of intermediate goods to a final destination market. With respect to partnerscountries, even though both the DVA and FVA decrease with Taiwan, the RVA increasedfrom 4.6 percent in 2000 to 12.5 percent in 2015. With richer countries such as the UnitedStates and Japan, all measures increased.

16

We can also look what happened in the same sector in the United States, Japan, andTaiwan as exporters when we consider China as the importer country. While with theUnited States the FVA shares decreased almost by half, from 8 to 5 percent, it almost dou-bled with Japan, from 8 to 13 percent. The FVA withTaiwan, in the meantime, decreasedfrom 31 to 24 percent. With United States and Japan the DVA share decreased while withTaiwan increased, while all the RVA shares decreased. This is pointing out to a deeperrole of China in the GVC in this particular sector. In the same period, China’s tariffs de-creased from an average of 18 percent in 2000 to 8 percent in 2014, with products withzero tariffs and products that had tariffs as high as more than 100 percent reaching 25percent. Indeed, this has to be an important factor in deepening the degree of integrationof China in the GVC.

5.2 GVC-FDI in the Worldbase Data.

Next, we document that: 1) Exporters are larger than domestic firms; 2) Foreign estab-lishments are larger than domestic establishments; and 3) foreign affiliates that export arelarger than affiliates that do not export. It is an important quality check of the Worldbasedata that these standard facts in the literature hold here. Fact 1 has been documented ex-tensively, starting by the early work of Bernanr and Jensen (199X), for many countries forwhich firm-level data are available; it is also la raison d’etre for the Melitz model of trade.Fact 2 has been documented with firm-level data by Helpman, Melitz, and Yeaple (2004),and subsequently for other countries. Finally, fact 3 is less spread since it is difficult to finddata for the activities of MNCs at the firm-level, even for one country. Researchers usingthe data for U.S. MNCs from the Bureau of Economic Analysis (BEA) have been able todocument this fact (see our own research in Ramondo, Rappoport, and Ruhl, 2015).

One problematic feature of the data is that 91,419, out of 230,130, have a missing valuefor sales. There is a set of 15,805 establishments that have non-missing for both sales andemployment. We use those observations to impute sales from the employment informa-tion to establishments with employment data, but missing sales; this is a set of 20,786establishments. We are left with 159,497 observations.10 The same procedure can be ap-plied to impute employment values.

Results are shown in figure 4. It is clear that the three facts from the literature hold inour data: Exporter are larger than domestic plants; foreign firms are larger than domesticplants; and foreign plants that export are larger than foreign plants that only serve the

10I estimate the following equation using OLS: log salesaci = β log empaci + Dci + eaci, for each affiliatea, in country c and industry i. I cluster standard errors at the parent level. The coefficient is β = 0.85 (s.e.0.11), and the R-squared is 0.56.

17

host market.

Next, let’s analyze the relation between the export activity of foreign affiliates andthe engagement of the host country in the GVC. Figure 5 shows that the domestic value-added (DVA) content of exports, for the country where foreign plants are located is neg-atively correlated with the fraction of foreign plants that export. That is, when a countryis part of a more fragmented GVC, manifested in a lower DVA, it also attracts a largerfraction of export-oriented affiliates of MNCs. Moreover, figure 6a shows that the moreintense is the GVC-FDI activity of affiliates in a host country c from a given source countryn, the higher the level of export upstreamness of nwith respect to c. Conversely, countrieswith a small share of GVC-FDI are the ones whose exports are in more upstream sectorsthan the source countries of foreign affiliates; MNCs do not locate in those countries theirexporter affiliates. This is again suggestive of affiliates of MNCs that export being part ofa complex GVC. Moreover, distance seems to matter for GVC FDI, suggesting that MNCschoose to locate exporters closer to home.11

Ww turn to the activities of Chinese plants abroad, their engagement in GVC-FDI, andthe relation between the presence of Chinese affiliates in those host countries and the com-position of Chinese gross exports to those same markets. Results are in table 4. Countriesin which Chinese affiliates are export-oriented, Chinese exports to those countries have ahigher share of RDV (to China) as well as a higher foreign value added component. Thisis far of being conclusive as the data for Chinese plants abroad is incomplete, but it issuggestive of Chinese MNCs coordinating GVC activities in other Asian countries.

Is there a relationship between engagement to the GVC, both through FDI and trade,and a country’s growth? Some evidence is provided by the diagram in figure 7. Thereis 26 countries in the region which are divided in four bins. For countries in each bin,the median annual (average) growth rate of real GDP per capita, between 2000 and 2010,is computed. In the left panel, bins are constructed as follow: Low/High FDI refers tocountries with sales of foreign plants, as a share of total sales, below/above the mediashare across countries; low/high GVC-FDI refers to countries with a fraction of foreignplants that exports below/above the media share across countries. In the right panel,the intensity of GVC-FDI (columns) is replace by the intensity of GVC-trade: Low/highGVC-trade refers to countries with a DVA share above/below the median share acrosscountries. Even thought observations are not many, the results are suggestive: countrieswhich high engagement in GVC-FDI and GVC-trade together with high presence of largeforeign plants grew more. Attracting large foreign plants seems to be really importantfor higher growth: The median country in the row "High FDI" grew substantially faster,

11This is indeed true for any type of FDI.

18

on average, than countries with a low level of attraction of foreign plants. The mediancountry that seems to have grown the least is the one with low presence of foreign plantsthat are mainly non-exporters.12

Is different attachment to the GVC linked to a decrease in tariffs? The distinction isclear between the group of countries with high attachment to the GVC and the rest: Forthe median country in the group with (High FDI, High GVC-FDI), the average annualdecrease in tariffs in the last fifteen year was above 12 percent, while for the remaininggroups the median country experienced an average annual decrease in tariffs of sevenpercent. Viet Nam, which is the country in the right panel with (High FDI, High GVC-Trade) experienced one of the highest average (annual) decrease in tariffs of all the coun-tries in Asia, 22 percent vs less than five percent for the median country in Asia.

6 Econometric Analysis

6.1 The Determinants of GVC FDI

We start by investigating a standard relationship found in the trade and MNC litera-ture: the gravity equation. This equation states that the flow of firms (or goods) betweencountry-pairs should be inversely proportional to bilateral resistance factors, such as ge-ographical distance. Following the state-of-the-art in estimating gravity equations, wesubsumed destination and origin country factors in two sets of country fixed effects. Theregressions shown in table 5 are meant to establish the "gravity stylized fact" for Asiancountries, using direct measures of bilateral affiliates activity, such as sales, employment,and number of foreign plants. A similar analysis using variables directly related to theactivity of affiliates of MNCs, not blanche of payment flows (or stocks), is presented inRamondo (2014) and Ramondo, Rodriguez-Clare, and Tintelnot (2015); the main differ-ence with their work is that, thanks to the access to Worldbase, we are able to have amuch more detailed sample of country-pairs involving countries in Asia. Besides stan-dard gravity variables (e.g., distance, sharing language, belonging to the same regional

12For the left panel, the countries in each bin are: (High FDI, High GVC-FDI) = Vietnam, Philippines, andKazakhstan; (Low FDI, High GVC-FDI) = Korea, China, Taiwan, Thailand, Indonesia, Georgia, Malaysia,Hong Kong, Singapore, and India; (High FDI, Low GVC-FDI) = Cambodia, Uzbekistan, Armenia, Azerbai-jan, Pakistan, Kyrgyzstan, Brunei, Sri Lanka; (Low FDI, Low GVC-FDI) = Australia, Bangladesh, Japan, andNew Zealand. For the right panel, the countries in each bin are: (High FDI, High GVC-Trade) = Vietnam;(Low FDI, High GVC-Trade) = Korea, China, Taiwan, Thailand; (High FDI, Low GVC-Trade) = Cambo-dia, Nepal, Uzbekistan, Philippines, Armenia, Azerbaijan, Pakistan, Kyrgyzstan, Kazakhstan, Brunei, SriLanka; (Low FDI, Low GVC-Trade) = Indonesia, Georgia, Malaysia, Australia, Bangladesh, Japan, HongKong, Singapore, New Zealand, India.

19

trade agreement, RTA), we add a variable that is related to GVC-trade: the bilateral do-mestic value-added embedded in gross exports from host country to the origin countryof the foreign affiliates (see columns 2,4, and 6, in table 5).

The effects of distance are negative and the coefficient close to one is as found in theliterature. However, the effect of distance drops and lose significant when we controlfor DVA shares, in some specifications. Sharing a language has a positive effect on thebilateral activity of affiliates of MNCs, as well as belonging to the same RTA. Differencesin levels of development between the origin and destination countries significantly detersMNC activity. The variable DVA share is in general positively associated to the activityof foreign plants in a host country: When DVA shares are larger it means that the hostcountry adds more value domestically; this indicates less fragmentation of production,but also a richer network of domestic firms, which may be an attraction factor for foreignplants.

We now turn to the analysis of the determinants of GVC FDI. In the first set of re-gressions we use as our dependent variable a dummy variable that indicates whether theestablishment exports (1) or not (0). This dummy is constructed by merging the variablesindicating the presence of both exports and imports, or only exports, at the establishmentlevel. As shown in the descriptive section, most establishments have both sides of inter-national trade flows. This regression is the main regression of the paper: It preciselycaptures at the affiliate level the presence of pure host-market activities vs export ac-tivities, and it can be seen as the main contribution of the paper, informing, as it couldnot be done before, the literature on horizontal vs vertical FDI.

Controls in this regression include variables at the affiliate level (sales), country level,and country-pair level. Alternately, industry-level variables are included and country(or country-pair) level factors are subsumed in fixed effects. Among the country-levelvariables of interest, we include the level of upstreamness of exports, and the domesticvalue share of exports. This latter variable is also used at the country-pair level, similarlyto the variable measuring returned domestic value of exports. When relevant, countryvariables are interacted with a dummy variable that indicates whether the establishmentbelongs to a parent in a different country. Note that there is room for a rich array of fixedeffects as we not only have country of origin and destination of establishments, but alsothe industries for both the establishment and its parent, as well as an indicator of whetherdifferent establishments belong to the same parent. Results are shown in tables 6 and ??.

Consistently, plants that export are larger: around five percent in terms of sales. Theestimates suggest that we should find exporters in smaller markets in terms of GDP, butthe effect is considerable smaller for foreign plants — i.e., the coefficient on log rgdp

20

vs log rgdp × D(foreign>0). Richer markets see also more exporters, but again the ef-fect is less pronounce if we concentrate on foreign plants. The capital-labor ratio of thecountry also matters for finding exporters there: Higher ratios are associated with lessexporters which, in Asia, suggest that exporters seek labor abundant countries. The sameis true for MNCs to a lesser extend. Countries with more educated population do havemore exporter plants, but for this variable the effect is null or reverse for foreign exporterplants. The quality of institutions, captured by the rule of law index, is an important factorin creating plants oriented to serving the domestic market: The weaker the institutionsin a country, the more plants are export oriented; the association is weaker for foreignplants.13. The availability of private credit in a country seems to matter for domestic ex-porters, but not for foreign exporters—-which again, makes sense given that MNCs donot rely on host-country credit. The variable denoting trade costs ("entry costs") is an im-portant factor to observe GVC FDI in a country: the higher the entry costs, the lower thefraction of export-oriented foreign plants. Again, this results points out to the fact thatplants of foreign MNCs care about the flexibility on, e.g., importing intermediate inputs.

Finally, turning to the factors related to GVC trade, the estimates suggest that coun-tries which exports are in more upstream sectors have fewer exporters; the effect is evenstronger for foreign plants. When the level of export upstreamness goes from the 50th pcto the 95th pc (this is like giving Japan the levels of export upstreamness of Australia),the probability of observing a foreign plant that exports decreases by 31 percent (from amean of 45 percent to 32 percent). The VAX ratio, of DVA share, is associated negativelywith the presence of exporters, and in a lesser magnitude when we restrict the attentionto foreign plants. Countries with a higher share of return value added in the gross valueof their exports have much more domestic and foreign exporters. These are indications ofengagement of MNCs in the GVC.

The next set of regressions adds information on the input-output (I-O) links betweenthe industry of operation of a plant and the one of its parent, in the spirit of Alfaro andCharlton (2009). The novelty is that, in some of the specifications, we can go a step furtherand associate production fragmentation within the MNC with the activities of the affiliate(i.e., exports vs pure-domestic activities). For now, we restrict the analysis to manufactur-ing plants belonging to parents also operating in manufacturing. We include plants withparents in the same and different country, but we exclude (domestic) plants that are theirown parent.

First, we establish the Alfaro and Charlton (2009)’s fact for countries in Asia. That is,13This result makes sense since export-oriented foreign plants are often "shield" from domestic institu-

tions by locating in special economic zones.

21

we establish that we observe more MNC activity in industry pairs with stronger input-output (I-O) links. We measure these links by considering the direct requirement coef-ficient between the industry of the affiliate (upstream) and the industry of the parent(downstream); we denote it by drap. We also consider the direct requirement coefficientbetween the industries of the affiliate and parent but when the affiliate is downstream andthe parent upstream; we denote if by drpa. We say that there is strong I-O link betweenparent and affiliate when the direct requirement coefficient is bigger than zero.

We aggregate the firm-level data in the following way to construct the dependentvariable: affiliates operating in country ca and industry ka belonging to parents in countrycp and industry kp. We use three measures of affiliates’ activity in a 4-tuple: number ofaffiliates; sales of affiliates; and affiliates’ employment.

Figure 8a, taken from Ramondo et al. (2015), summarizes the characteristics of thedirect requirements coefficients, taken from 2002 U.S. the input-output table. The x-axis isthe industry code of the using (downstream) industry, and the y-axis is the industry codeof the producing (upstream) industry. The bubble’s size is proportional to the size of thedirect requirements coefficient of the industry pair. It is clear from the figure that mostindustries require inputs from similar industries: The entries in the direct requirementstable tend to be largest on or near the diagonal. Figures 8b and 8c plots the distributionof industry pairs for the parent-affiliates pairs in the data, for all plants and for plantswith foreign parents, respectively. The bubble’s size is proportional to the number ofparent-affiliate observations in that industry pair. Combining these figures suggests thatparents own affiliates in similar industries, and these industries are important producersof intermediate inputs for each other. More than 95 percent of manufacturing affiliatesoperate in industries with a strong I-O link with the industry of the parent; only fivepercent of plants present zero direct requirement coefficients with the parent (againstalmost 50 percent in the IO matrix). The distribution is skewed: While the mean of drap is0.20, the median is 0.06—similarly for dpa; less than a third of observations are above themean.

Table 9 presents the formal results. Similar to the results in Alfaro and Charlton (2009)and Ramondo et al. (2015), MNC activity—and also activity of multi-plant firms—is as-sociated with production chains between parents and affiliates. An increase in drap from0.06 (the median) to 0.3 (the 75pc) is associated with an increase in sales of affiliates ina given 4-tupla of 18 percent; the effect is halve in half when only plants with foreignparents are considered.

Which are the countries and industries where we observe plants with stronger I-Olinks with their parents? Not surprisingly, we find the car industry at the top of the list,

22

particularly, involving Chinese plants of Japanese, Korean, and German parents, as wellas the and semiconductor and electronic industry with Chinese plants from Japan, Tai-wan, and Korea, and the plastic industry with Chinese plants of Japanese MNCs. Table7 presents the most common country-industry pairs for which I-O links are strong (andthe industry of the affiliate is different from the one of the parent.14 Regarding the en-gagement of country pairs in GVC FDI, table 8 lists the pair of countries with the highestnumber of affiliate plants with parents in a different country that engage in both im-ports and exports activities. Most Chinese plants belonging to Japanese, Hong Kong, andAmerican MNCs are intensively engaged in international trade activities.

In the second set of results, we analyzes which type of plants presents I-O links withtheir parents: large vs small, exporters vs non-exporters, foreign vs domestic.

First, columns 1 and 2 of table 10 establish that stronger I-O links with the parent,either upstream or downstream, are associated with larger affiliate plants; the effect is notdifferent for foreign plants. Going from a value for drap in the 50pc to the one in the 75pc(from 0.06 to 0.3) is associates with affiliates which sale are 3.4 percent larger. The effectis not large.

Second: Are these production chains between parent and affiliate plants associatedwith affiliates that export and import? Columns 3-8 present the results. There is a strongpositive association between the number of plants with both non-zero upstream anddownstream links with the parent and affiliate plants that both export and import. Thenumber of plants that are upstream of the parent in the production chain is significantlyand positively associated with the exporter status of the plant — similarly, when the im-porter status of the plant is considered and production-chain relations in which the affili-ate is downstream of the parent. The coefficients when only foreign plants are consideredare lower, but always strongly significant. Let’s interpret more carefully the coefficients.Take column 3: The median bin has one plant engaged in international trade, while the90% pc bin has two plants and the 95% pc has 4 plants (one of the bins with the mostplants is the one involving Chinese plants of Japanese parents in the auto parts industry,with 150 plants). Moving from the median bin to the 95% pc bin implies an increase of3.6 plants with strong I-O links with their parent —which is equivalent to moving fromthe median bin to more than the 90% pc bin in terms of the distribution of the number ofplants with strong links with their parents (the bin with the largest number of plants withstrong I-O links with their parent involves, precisely, the auto parts industry and Chineseplants of Japanese MNCs, with 169 plants). Magnitudes are similar for the remaining

14The industry classification of 4-digit NAICS is coarse; many affiliate-parent pairs belong to the same4-digit industry which, in itself, indicates strong I-O links since most industries use the inputs they producewithin the same industry.

23

columns. These results are suggestive of the engagement of a plant in international tradeactivities being related to the GVC.

6.2 The determinants of GVC Trade.

We start by analyzing gravity equations for measures of GVC trade. We focus on the shareof trade in intermediate goods and the share of domestic value added.

Table 11 shows results for the flow of intermediate goods, from country e to i, as shareof total trade from e to i, in industry k, an average over the period 2000-2005. Table 12 usesas dependent variable the ratio of DVA with respect to gross exports, also at the bilateralsector level, for the years 2000, 2005, 2011, and 2014. We consider the whole sample ofcountries and the sub-sample of ADB Asian members, as well as all, manufacturing, andmining sectors.

As expected, geographical distance has a negative effect on trade of intermediategoods, irrespective of the sector. Sharing a language seems to have a positive effect but itis only significant for the sample of ADB Asian members. Sharing a colonial past detersthe share of trade in intermediate goods, specially in Asia, as well as belonging to thesame RTA. Turning to the bilateral factors affecting the domestic value-added share ofexports, distance and being part of the same RTA play a positive role: the further awaythe country-pair (or sharing the RTA), the more the domestic content of exports. Sharinga language works in the opposite direction. If the exporter is at a higher level of devel-opment than the importer the domestic value added share of exports is lower than if theopposite is true. Finally, if the exporter is relatively more capital abundant than the im-porter the DVA share is higher than if the exporter were relatively more labor abundantthan the importer.

Table 13 considers country factors that may affect the domestic value-added share ofexporter.

Richer exporter countries, measured by real GDP per capita, have in general higherDVA shares. The presence of more private credit—which cannot be evaluated amongADB Asian members because of collinearity—also seems to stimulate engagement in theGVC as the share of DVA in exports is lower. The effect is particularly strong amongADB Asian members, except for manufacturing for which the relationship is reversed andricher countries are associated with lower DVA shares. The market size of the exporterimpacts positively, as expected, the share of domestic value added of exports (except,again, for countries in Asia, for the manufacturing sector). Smaller markets are naturally

24

the ones which are more open and hence more engaged in the GVC, once we control forother factors.

The capital intensity of the exporter affects positively the share of DVA in manufactur-ing, but negatively in mining: Countries with higher capital-labor ratios add more valueto exports domestically if the good is a manufacturing good — that is, relatively laborabundant countries are the ones with lower DVA shares and hence more engaged in theGVC. The exporter level of human capital, measured as the average years of schoolingof the population in a country, reinforces the engagement of a country in the GVC. Fi-nally, the degree of upstreamness of exports (overall) is positively correlated with addingmore value domestically; that is, countries that on average are specialized in more up-stream sectors, present less engagement in the GVC. The effects of the country capital-labor ratios, human capital endowments and the level of upstreamness of exports is inline what we observed in table 6: Exports in more upstream sectors were associated withless exporters, and countries with less human capital and relatively scarce labor had lessexporters. As our sample on GVC FDI is for Asia, the results on the level of developmentof a country and market size, and the country’s engagement on the GVC, respectively areconsistent for GVC-FDI and GVC-trade: More developed and smaller countries are moreengaged in the GVC (e.g. the negative coefficient on rgdpl and rgdp in column 6 of table13).

7 GVC-FDI: The case of U.S. MNCs.

The country for which an analysis similar to the one above is possible, at a very detailedlevel, is the United States, thanks to the data on the activity of U.S. MNCs provided bythe Bureau of Economic Analysis (BEA).

Ramondo, Rappoport, and Ruhl (2015) construct indices of horizontal, vertical andexport-platform activity, using the BEA firm-level confidential data on affiliates of U.S.MNCs. These indices are extracted from affiliate-level regressions as fixed effects at thecountry-industry (4-digit NAICS) level for each type of affiliate activity; that is, the indicessimply indicate the average sales share in each activity type for a given industry-countrypair. The procedure is as follows. We first define horizontal activities as the share ofaffiliate sales directed to the local market of operation, vertical activities as the share ofsales to other related parties, and export-platform activities as the share of sales directedto unrelated parties outside the host market, using the confidential data on the destination

25

of the sales of affiliates at the BEA. Then, we run the following OLS specification:

D(shareMaic > 0) = Fic + eaic,M = V ert,Hor, Exp (9)

where D(shareMaic > 0) is a dummy variable that equals one if the share of sales of typeM in total sales for affiliate a operating in country c and (primary) industry i is positive;and equals zero otherwise. In this way, we have a country-industry data set of 1, 529

observations for each type of affiliate activity, at the extensive margin.15 Notice that byfocusing on fixed effects, these indices isolate completely country-industry motives todo a type of activity; all firm-specific attributes are left out. Hence, these indices can becorrelated with industry and country characteristics. The year used is 2004, for which theBEA data are of high quality.

Tables 14 and 15 summarize the indices derived from 9, by industry and region, whenthe dependent variable is the share of the affiliate’s activity-type in total sales. Lookingat table 14. "Electrical equipment" and "Textiles and apparel" are the industries in whichintra-firm trade is most important (panel 1)—vertical activities. Affiliates operating in"Textiles and apparel" have, on average, the highest share of exports to unaffiliated par-ties (panel 2)—export-platform activities, and, on the other extreme, affiliates in "Food,tobacco, and beverage" industry have the largest share of sales to local unaffiliated par-ties (i.e., horizontal activities).

The geographical patterns of the vertical and horizontal activities of U.S. foreign affil-iates are reported in table 15. Not surprisingly, U.S. affiliates in neighboring countries aremost likely to engage in vertical activities. These activities are the least representative ofthe mean affiliate in Africa. European affiliates, on average, direct the most of their salesto unaffiliated parties in other countriesÑEuropean export platforms. Finally, affiliates inLatin America (excluding Mexico) are most likely to be local horizontal (panel 3). It isworth noting that U.S. affiliates in Asia are more export-oriented, both with related andunrelated parties, than U.S. affiliates in Latin American (excluding Mexico) and Africa.

Figure 9 shows for Asian countries the ranking in terms of vertical and export-platformactivities of U.S. affiliates, both at the extensive and intensive margin. For instance, figure9a shows that, on average (across industries), Sri Lanka is the country with the lowestshare of U.S. affiliates doing vertical activities (i.e. exporting back goods to their parents),but it is the location with the largest share of U.S. affiliates with positive exports to third-parties. Korea presents the opposite case: The amount of U.S. affiliates doing verticalactivities is above the average, and also they do so at a high intensity (as shown in figure

15We also calculated indices at the intensive margin by using as the dependent variable in 9 the log of thesales share in each type.

26

9c.

Finally, the results of a more formal analysis is presented in table 16, which showscorrelations between the indices of U.S. MNC activity and some country-pair, country,and industry-country characteristics.

8 Discussion

9 Conclusions

27

Figure 2: VAX and trade in intermediates

(a) vax ratio, all.6

.7.8

.9va

x ra

tio

1995 1997 1999 2001 2003 2005 2007 2009year

all ADB Asian members

(b) vax ratio, selected countries

.6.7

.8.9

vax

ratio

1995 1997 1999 2001 2003 2005 2007 2009year

China Mexico India Brazil

(c) interm’ trade shares, all

.4.5

.6.7

.8Tr

ade

in in

term

edia

tes',

as

a sh

are

of to

tal e

xpor

ts

1995 1997 1999 2001 2003 2005 2007 2009year

all ADB Asian members

(d) interm’ trade shares, selected countries

.4.5

.6.7

.8Tr

ade

in in

term

edia

tes',

as

a sh

are

of to

tal e

xpor

ts

1995 1997 1999 2001 2003 2005 2007 2009year

China Mexico India Brazil

28

Figure 3: Share of domestic value-added in gross exports and exporter characteristics.

(a) export upstreamness-1

-.8-.6

-.4-.2

0lo

g of

dva

as

a sh

are

of g

ross

exp

orts

1.5 2 2.5 3country's exports upstreamness

(b) rule of law

-1-.8

-.6-.4

-.20

log

of d

va a

s a

shar

e of

gro

ss e

xpor

ts

-1 0 1 2country's rule of law

Notes: The OLS coefficient for figure 3a is 0.13*** and for figure 3b is -0.034**.

29

Figure 4: Exporters and foreign affiliates in WorldBase

(a) exporters vs non-exporters plants, sales0

.05

.1.1

5.2

.25

kern

el d

ensi

ty

5 10 15 20 25x

exporter non-exporter

(b) exporters vs non-exporters plants, employment

0.1

.2.3

.4ke

rnel

den

sity

-5 0 5 10 15x

exporter non-exporter

(c) foreign vs domestic plants, sales

0.0

5.1

.15

.2.2

5ke

rnel

den

sity

5 10 15 20 25x

foreign domestic

(d) foreign vs domestic plants, employment

0.1

.2.3

.4ke

rnel

den

sity

-5 0 5 10 15x

foreign domestic

(e) exporter vs non-exporter MNC plants, sales

0.1

.2.3

kern

el d

ensi

ty

5 10 15 20 25x

exporter non-exporter

(f) exporter vs non-exporter MNC plants, employ-ment

0.1

.2.3

kern

el d

ensi

ty

-5 0 5 10 15x

exporter non-exporter

30

Figure 5: GVC trade and GVC FDI in the aggregate

0.2

.4.6

.81

fract

ion

of fo

reig

n af

filia

tes

that

exp

ort,

coun

try-p

air l

evel

.5 .6 .7 .8 .9 1DVA share, country-pair level

Notes: The y-axis variable i is the number of affiliates in country c belonging to parents in n that export, as a share oftotal affiliates in country c belonging to parents in n. The x-axis variable is the domestic value added (DVA) of exportsfrom c to n, as a share of gross exports from c to n. In all cases origin and destination countries are different (c 6= n).The OLS coefficient for a fitted line is -1.23*** (s.e. 0.18)

31

Figure 6: Share of GVC-FDI and country-pair characteristics.

(a) differences in export upstreamness-3

-2-1

0lo

g of

frac

tion

of fo

reig

n af

filia

tes

that

exp

ort,

by c

ount

ry-p

air

-.4 -.2 0 .2 .4log of ratio of export upstreamness of host, relative to source, country

(b) bilateral distance

-3-2

-10

log

of fr

actio

n of

fore

ign

affil

iate

s th

at e

xpor

t, by

cou

ntry

-pai

r

6 7 8 9 10log of bilateral distance

Notes: The OLS coefficient for figure 6a is -0.895*** (s.e. 0.17) and for figure 6b is -0.10** (s.e. 0.04).

32

Figure 7: Engagement in the GVC and growth

High  FDI

Low  FDI 0.032  (10) 0.033  (4)

High  FDI

0.013  (4) 0.036  (10)

0.049  (9) 0.061  (3) 0.049  (11) 0.061  (1)

Low  GVC-­‐Trade High  GVC-­‐TradeLow  GVC-­‐FDI High  GVC-­‐FDI

Low  FDI

Notes: Each square shows median growth annual average rate of real GDP per capita, for 2000-2010, for the groupof countries with different level of engagement to the GVC. Low/High FDI refers to countries with sales of foreignplants, as a share of total sales, below/above the median share across countries. Low/High GVC-FDI refers tocountries with a fraction of foreign plants that export below/above the median share across countries. Low/highGVC-trade refers to countries with DVA shares above/below the median across countries. Number of observationsin each group are in parenthesis.

For the left panel, the countries in each bin are: (High FDI, High GVC-FDI) = Vietnam, Philippines, and Kazakhstan;(Low FDI, High GVC-FDI) = Korea, China, Taiwan, Thailand, Indonesia, Georgia, Malaysia, Hong Kong, Singapore,and India; (High FDI, Low GVC-FDI) = Cambodia, Uzbekistan, Armenia, Azerbaijan, Pakistan, Kyrgyzstan, Brunei,Sri Lanka; (Low FDI, Low GVC-FDI) = Australia, Bangladesh, Japan, and New Zealand. For the right panel, thecountries in each bin are: (High FDI, High GVC-Trade) = Vietnam; (Low FDI, High GVC-Trade) = Korea, China,Taiwan, Thailand; (High FDI, Low GVC-Trade) = Cambodia, Nepal, Uzbekistan, Philippines, Armenia, Azerbaijan,Pakistan, Kyrgyzstan, Kazakhstan, Brunei, Sri Lanka; (Low FDI, Low GVC-Trade) = Indonesia, Georgia, Malaysia,Australia, Bangladesh, Japan, Hong Kong, Singapore, New Zealand, India.

33

Figure 8: Production Fragmentation: Direct requirements coefficients and parent-affiliateactivity.

(a) I-O industry pairs, U.S.31

0032

0033

0034

00

pro

du

cin

g in

du

stry

3100 3200 3300 3400

using industry

(b) parent-affiliate pairs, all plants

3100

3200

3300

3400

Indu

stry

of p

aren

t

3100 3200 3300 3400

Industry of affiliate

(c) parent-affiliate pairs, foreign plants

3100

3200

3300

3400

Indu

stry

of p

aren

t

3100 3200 3300 3400

Industry of affiliate

Notes: Figure 8a: Direct requirements coefficients for industry pairs in 2002, from the U.S. input-output matrix; bub-bles are proportional to the size of the direct requirements coefficient. Figure 8b: Frequency of the industries ofparent-affiliate pairs, all plants; bubbles are proportional to the number of parent-affiliate pairs in a given industrypair. Figure 8c: Frequency of the industries of parent-affiliate pairs, plants with parents in a different country; bubblesare proportional to the number of parent-affiliate pairs in a given industry pair. The direct requirements coefficientis the value of goods needed from the producing (upstream) industry in order to produce one dollar of output in theusing (downstream) industry. Manufacturing industries only.

34

Figure 9: Activities of U.S. Affiliates in Asia: Vertical vs Export-Platform

(a) vertical, extensive margin

-­‐0.8  

-­‐0.6  

-­‐0.4  

-­‐0.2  

0  

0.2  

0.4  

0.6  

0.8  

THA   HKG   KOR   IND   IDN   CHN   JPN   PHL   SGP   MYS   PAK   AUS   TWN   NZL   LKA  

(b) exp.platform, extensive margin

-­‐0.8  

-­‐0.6  

-­‐0.4  

-­‐0.2  

0  

0.2  

0.4  

0.6  

0.8  

LKA   NZL   SGP   HKG   IND   CHN   JPN   MYS   PHL   PAK   AUS   THA   IDN   TWN   KOR  

(c) vertical, intensive margin

-­‐4  

-­‐3  

-­‐2  

-­‐1  

0  

1  

2  

IDN   MYS   SGP   KOR   CHN   NZL   PHL   TWN   LKA   THA   AUS   PAK   HKG   JPN   IND  

(d) exp.platform, intensive margin

-­‐4  

-­‐3  

-­‐2  

-­‐1  

0  

1  

2  

NZL   SGP   JPN   HKG   THA   AUS   TWN   CHN   KOR   IND   PAK   IDN   PHL   MYS  

Notes: "Vertical" refers to sales to related parties, "exp.platform" refers to sales to unrelated parties outside the hostmarket. Averages across 4-digit industries, for each country. The average for all the sample is normalized to zero.

35

Table 1: Summary statistics: Worldbase.

All sectors Mining Manufacturing Services Other sectors

Fraction of exporters, all plants 0.19 0.12 0.24 0.07 0.14among foreign plants 0.63 0.38 0.74 0.28 0.24among domestic plants 0.15 0.10 0.19 0.05 0.12

Fraction of foreign plants 0.09 0.07 0.10 0.07 0.20

Total number of plants 230,130 5,958 161,418 61,280 1,474

36

Table 2: Summary statistics, by country: Worldbase.

Foreign plants, Exporters as share of:as share of all plants all plants foreign plants

Panel A

Australia 0.13 0.136 0.225China 0.23 0.477 0.815Hong Kong 0.41 0.481 0.480India 0.02 0.162 0.469Indonesia 0.50 0.444 0.521Japan 0.01 0.034 0.202Korea 0.14 0.447 0.438Malaysia 0.63 0.670 0.711New Zealand 0.11 0.178 0.000Singapore 0.41 0.423 0.518Taiwan 0.22 0.812 0.766Thailand 0.31 0.522 0.723

Panel B

Afghanistan 0.000Armenia 0.000Azerbaijan 0.000Bangladesh 0.100Brunei 0.167Cambodia 0.407Georgia 0.417Kazakhstan 1.000Kyrgyzstan 0.000Nepal 0.000Pakistan 0.000Philippines 0.661Sri Lanka 0.357Uzbekistan 0.286Viet Nam 0.757

Note: Panel A lists countries for which we have both observations on domestic and foreign plants, whilepanel B lists countries for which we only have observations on foreign plants. Some countries in panelB have very few observations: Afghanistan 1; Armenia 4; Azerbaijan 8; Kazakhstan 1; Kyrgyzstan 3; andNepal 4.

37

Table 3: Summary statistics: GVC trade.

All ADB members ADB Asian Members Non-members China India

dva2000 0.771 0.801 0.804 0.754 0.860 0.8602015 0.782 0.814 0.810 0.768 0.851 0.876

fva2000 0.166 0.140 0.142 0.181 0.102 0.1032015 0.164 0.088 0.143 0.177 0.109 0.095

rdv2000 0.0043 0.0076 0.0031 0.0015 0.0050 0.00132015 0.0037 0.0128 0.0030 0.0018 0.0073 0.0027

Note: domestic value added (DVA), foreign value added (FVA), and return domestic value (RDV) of ex-ports, are as shares of gross exports, an average across sectors and country-pairs.

Table 4: China: GVC-FDI and GVC-Trade.

Host country Number of Chinese Share of Chinese plants RDV DVA FVAplants abroad that export (share of Chinese gross exports)

Australia 60 0.170 0.007 0.865 0.086Hong Kong 61 0.490 na na naIndonesia 5 0.200 0.012 0.813 0.122India 8 0.125 0.008 0.774 0.165Japan 15 0.067 0.010 0.838 0.090Cambodia 2 0.500 na na naKorea 2 0.500 0.038 0.647 0.216Malaysia 4 0.500 na na naPakistan 1 0.000 na na naPhilippines 4 0.750 na na naSingapore 31 0.290 na na naThailand 2 1.000 0.017 0.550 0.311Taiwan 2 1.000 0.078 0.534 0.252Viet Nam 17 0.765 0.023 0.804 0.145

214 0.454 0.024 0.728 0.173

Note: The last row shows in the first column the total number of Chinese plants abroad present in oursample from WorldBase, while the remaining columns show averages for each variable.

38

Table 5: Determinants of bilateral FDI: gravity. OLS.

Dep. variable Log of aff sales Log of aff employment Log of number aff(1) (2) (3) (4) (5) (6)

log dist -1.023** -0.594 -0.874** -0.664 -0.730** -0.739**(0.321) (0.464) (0.275) (0.451) (0.149) (0.266)

D(share language) 0.849* 0.982 0.674* 0.837 0.426** 0.366(0.335) (0.599) (0.314) (0.573) (0.162) (0.298)

D(share colonial past) 0.089 -0.299 0.400 -0.104 0.495* -0.068(0.436) (0.488) (0.434) (0.437) (0.210) (0.285)

D(rta) 1.512** 1.684+ 0.907* 0.964 0.864** 1.047*(0.441) (0.854) (0.419) (0.796) (0.207) (0.453)

log rgdpld/rgdplo -8.836** -10.487** -7.643** -9.608** -6.472** -6.760**(1.202) (3.401) (1.165) (3.282) (0.726) (1.662)

logKLd/KLo 1.219 11.151** 0.025 9.892** -0.594 7.329**(1.570) (3.554) (1.581) (3.438) (0.922) (1.750)

dva share 6.563* 4.348 2.760+(3.048) (2.902) (1.520)

Observations 409 205 409 205 438 210R-squared 0.708 0.740 0.695 0.738 0.814 0.847

Notes: The dependent variable is a measure of the activity of affiliate of multinational firms (sales, em-ployment, and number of affiliates) from source country i in destination country n. All specifications withsource and destination fixed effects. Robust standard errors in parentheses. Levels of significance are de-noted by ** p<0.01, * p<0.05, + p<0.1.

39

Table 6: Determinants of GVC FDI: country variables. OLS.

Dep var D(exports>0)

(1) (2) (3) (4) (5)

log of affiliate sales 0.044** 0.039** 0.044** 0.044** 0.044**(0.001) (0.001) (0.001) (0.001) (0.001)

log rgdp -0.108** -0.010 -0.067** -0.088** -0.070**(0.013) (0.072) (0.015) (0.013) (0.025)

× D(foreign>0) 0.072** -0.104 0.060** 0.022* -0.030(0.013) (0.073) (0.014) (0.010) (0.023)

log rgdpl 1.015** 2.399** 1.292** 1.342** 0.429**(0.078) (0.738) (0.146) (0.088) (0.107)

× D(foreign>0) -0.163+ -2.326** -0.494** -0.462** 0.129(0.097) (0.746) (0.162) (0.108) (0.131)

log K/L -0.688** -2.332** -1.225** -1.360** -0.893**(0.087) (0.736) (0.141) (0.079) (0.114)

× D(foreign>0) 0.011 2.053** 0.393* 0.403** -0.060(0.101) (0.738) (0.160) (0.101) (0.121)

log yr. sch. -0.008 0.115** 0.031* 0.047** 0.165**(0.008) (0.041) (0.013) (0.008) (0.021)

× D(foreign>0) 0.000 -0.152** -0.042** -0.016* -0.058**(0.008) (0.041) (0.014) (0.008) (0.022)

rule of law -2.167** -1.868**(0.215) (0.405)

× D(foreign>0) 0.787** 2.385**(0.225) (0.424)

priv. credit 0.322*(0.143)

× D(foreign>0) 0.029(0.139)

entry costs 0.006(0.012)

× D(foreign>0) -0.005**(0.001)

Export upstreamness -0.343**(0.061)

× D(foreign>0) -0.101+(0.058)

dva share -1.667**(0.208)

× D(foreign>0) 0.566**(0.219)

rdv share 15.950**(3.112)

× D(foreign>0) 13.124**(3.288)

Observations 159,390 131,454 157,323 159,390 154,608R-squared 0.480 0.425 0.465 0.480 0.479

Notes: The dependent variable is a dummy variable equal to one if the plant reports export activity. Thedummy D(foreign > 0) is one if the plant belongs to a parent in a different country from the one ofoperations. Country-level variables are for the country the plant is located. Entry costs are the costs ofbusiness start-up procedures, as % of GNI per capita. The variable "priv.credit" refers to private credit asshare of GDP. DVA and RDV are calculated as a share of gross exports. All specifications with parent-affiliate industry fixed effects and origin country fixed effects. Robust standard errors, clustered at theparent level, in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, + p<0.1.40

Table 7: Production fragmentation: Most common country-industry pairs.

Country Country Industry of plant Industry of parentof plant of parent

China Japan Resin, Sybthetic Rubber, and Artificial Synthetic Fibers Plastic ProductsChina Japan Resin, Sybthetic Rubber, and Artificial Synthetic Fibers Basic ChemicalChina Japan Semiconductors and Other Electronic Component Plastic ProductsChina Japan Electrical Equipment Semiconductors and Other Electronic ComponentChina Japan Semiconductors and Other Electronic Component Navigational, Measuring, Electromedical, and Control InstrumentsChina Japan Motor Vehicle parts Engine, Turbine, and Power Transmission equipmentChina Germany Motor Vehicle parts Plastic ProductsChina Japan Motor Vehicle parts Cutlery and HandtoolChina Japan Motor Vehicle parts Rubber ProductsChina Japan Semiconductors and Other Electronic Component Computer and peripheral EquipmentChina Japan Other electrical equipment and Component Semiconductors and Other Electronic ComponentChina Japan Motor Vehicle parts Plastic ProductsChina Japan Motor Vehicle parts Semiconductors and Other Electronic ComponentChina Japan Semiconductors and Other Electronic Component Other Electrical Equipment and ComponentsChina Taiwan Communications Equipment Semiconductors and Other Electronic ComponentChina Taiwan Computer and peripheral equipment Semiconductors and Other Electronic ComponentChina Korea Semiconductors and Other Electronic Component Communications EquipmentChina Japan Semiconductors and Other Electronic Component Motor Vehicle partsChina Japan Semiconductors and Other Electronic Component Communications EquipmentChina Japan Metal Working Machinery Plastic ProductsChina Japan Other Fabricated Metal Products Motor Vehicle partsChina Germany Electrical Equipment Navigational, Measuring, Electromedical, and Control InstrumentsChina Taiwan Semiconductors and Other Electronic Component Computer and peripheral EquipmentChina United States Basic Chemical Other General Purpose MachineryChina Japan Semiconductors and Other Electronic Component Electrical Equipment

Notes: Industry pairs in manufacturing only, at four-digit NAICS.

41

Table 8: GVC FDI: Most common country pairs.

Country of plant Country of parent Number of plants w/ imports & exports

India United States 69Korea Japan 69China Netherlands 82Australia United States 91Singapore United States 102China Sweden 116China Italy 117China United Kingdom 120Viet Nam Korea 125China Switzerland 131Singapore Japan 164Philippines Japan 171Malaysia Japan 175China France 177Taiwan Japan 212Indonesia Japan 214Thailand Japan 258Viet Nam Japan 306China Singapore 337China Korea 358China Taiwan 401China Germany 625China United States 646China Hong Kong 1314China Japan 2260

42

Table 9: Production fragmentation and the boundaries of the firm. OLS.

Dep. variable Log num.aff Log sales.aff Log emp.aff Log num.aff Log sales.aff Log emp.aff(1) (2) (3) (4) (5) (6)

drap 0.624** 0.813** 0.804** 0.304** 0.399+ 0.389+(0.113) (0.211) (0.173) (0.095) (0.228) (0.200)

drpa 0.427** 0.975** 0.759** 0.181* 0.541** 0.365*(0.101) (0.202) (0.161) (0.077) (0.200) (0.165)

Observations 7,837 6,999 6,999 4,127 3,682 3,682R-squared 0.219 0.157 0.254 0.110 0.077 0.158

sample all plants all plants all plants foreign plants foreign plants foreign plants

Notes: The dependent variable is a measure of the activity of affiliates operating in industry k and country n belonging to firms in industryl country i. Only affiliate-parent pairs in the manufacturing sector are included. Columns 1-3 includes all plants, while columns 4-6 onlyplants with parents in a different country. Only plants that are not parent companies are included. All regressions with origin country fixedeffects, destination country fixed effects, and parent industry fixed effects. Standard errors, clustered at the parent-affiliate industry level,in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, + p<0.1.

43

Table 10: Production fragmentation and GVC FDI. OLS.

Dep variable log aff sales Number of affiliatesdrap > 0 drap > 0 drpa > 0 drap > 0 drap > 0 drpa > 0drpa > 0 drpa > 0

(1) (2) (3) (4) (5) (6) (7) (8)

drap 0.153*(0.077)

× D(foreign>0) -0.071(0.107)

drpa 0.170*(0.076)

× D(foreign>0) 0.129(0.117)

D(exports>0 & imports>0) 1.202** 0.752**(0.180) (0.184)

D(exports>0) 1.172** 0.743**(0.167) (0.185)

D(imports>0) 1.107** 0.704**(0.144) (0.168)

Observations 19,225 19,225 18,158 18,158 18,158 8,740 8,740 8,740R-squared 0.132 0.132 0.375 0.417 0.433 0.665 0.671 0.683

origin & destination fe no no yes yes yes yes yes yesorigin-destination fe yes yes no no no no no noclusters by industry of p p p-a p-a p-a p-a p-a p-asample all plants all plants all plants all plants all plants foreign plants foreign plants foreign plants

Notes: The dependent variable in columns 1 and 2 is at the firm level, while in columns 3-8 the dependent variable is the number of plants operating in industryka and country ca belonging to parents in industry kp and country cp with different types of I-O links with the parent plant. The dummy D(foreign>0) is onefor plants with parents in a different country. The variable Num(imports>0 & exports >0) denotes the number of plants in the bin ka × ca × kp × cp with bothimports and exports. Columns 1-5 include all plants, while columns 6-8 includes only plants with parents in a different country. Only affiliate-parent pairs inthe manufacturing sector and only plants that are not parent companies are included. All specifications with affiliate industry and parent industry fixed effects.Standard errors in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, + p<0.1.

44

Table 11: Determinants of GVC trade, trade in intermediates: gravity. OLS.

Dep var log of intermediates’ trade flows, as a share of total trade(1) (2) (3) (4) (5) (6)

log dist -0.008** -0.017** -0.035** -0.006 -0.008 -0.031+(0.002) (0.003) (0.005) (0.010) (0.011) (0.017)

D(share language) 0.008 0.004 0.009 0.026* 0.019+ -0.013(0.006) (0.006) (0.014) (0.011) (0.010) (0.021)

D(share colonial past) -0.010+ -0.007 0.007 -0.052** -0.039* 0.037(0.006) (0.007) (0.014) (0.019) (0.018) (0.028)

D(rta) 0.007 0.014+ 0.009 -0.227** -0.291** 0.111(0.007) (0.007) (0.014) (0.079) (0.088) (0.211)

log rgdpld/rgdplo 0.002 0.005 -0.006 -0.005 -0.004 -0.008(0.004) (0.005) (0.008) (0.006) (0.008) (0.017)

logKLd/KLo -0.007 -0.006 0.003 -0.005 -0.005 0.004(0.005) (0.005) (0.009) (0.006) (0.008) (0.017)

Observations 40,360 17,621 1,251 6,840 3,422 241R-squared 0.524 0.627 0.489 0.566 0.619 0.710

sample ind all manuf mining all manuf miningsample ctry all all all ADB ADB ADB

Notes: Observations are at the bilateral country (exporter) sector level, an average over 2000-2005.The dependent variable refers to trade in intermediates from j to n, as a share of total trade from j ton, in industry i. The sample of ADB countries refers to Asian members only. All specifications withexporter, importer, and industry fixed effects. Robust standard errors, clustered by importer-exporter,in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, + p<0.1.

45

Table 12: Determinants of GVC trade, domestic value-added: gravity. OLS.

Dep variable DVA as share of gross exports

(1) (2) (3) (4) (5) (6)

log dist 0.005** 0.010** 0.005** 0.004+ 0.005* 0.009*(0.001) (0.002) (0.001) (0.002) (0.002) (0.004)

D(share language) -0.005* -0.009* -0.005* -0.002 -0.004 -0.005(0.002) (0.004) (0.002) (0.002) (0.003) (0.005)

D(share colonial past) 0.002 0.005 0.003 -0.003 -0.001 -0.002(0.002) (0.004) (0.002) (0.003) (0.003) (0.003)

D(rta) 0.001 0.004* 0.003* 0.008* 0.014** 0.017**(0.001) (0.002) (0.001) (0.003) (0.003) (0.005)

log rgdple/rgdpli -0.049** -0.118** -0.043* -0.037 -0.025 -0.148**(0.018) (0.028) (0.018) (0.030) (0.025) (0.056)

logKLe/KLi 0.047** 0.114** 0.040* 0.034 0.021 0.141**(0.017) (0.027) (0.017) (0.029) (0.024) (0.054)

Observations 88,616 2,594 38,980 18,988 9,277 576R-squared 0.784 0.922 0.754 0.738 0.755 0.893sample industries all mining mfg all mining mfgsample countries all all all ADB ADB ADB

Notes: All specifications with exporter, importer, and industry fixed effects. Robust standard errors,clustered by importer-exporter, in parentheses. Levels of significance are denoted by ** p<0.01, *p<0.05, + p<0.1.

46

Table 13: Determinants of GVC trade: country characteristics. OLS.

Dep variable DVA, as a share of gross exports(1) (2) (3) (4) (5) (6)

rule of law -0.021** -0.048** -0.019** -0.029** -0.056** 0.124**(0.004) (0.005) (0.005) (0.001) (0.001) (0.002)

log rgdpl 0.023** 0.135** 0.019* 0.199** 0.290** -0.551**(0.008) (0.012) (0.009) (0.006) (0.005) (0.015)

log rgdp 0.031** 0.013** 0.038** 0.073** 0.104** -0.025**(0.001) (0.002) (0.001) (0.000) (0.000) (0.001)

log KL 0.014* -0.099** 0.027** -0.151** -0.217** 0.297**(0.006) (0.009) (0.007) (0.003) (0.003) (0.009)

yr.sch -0.013** -0.021** -0.015** -0.015** -0.016** 0.043**(0.001) (0.001) (0.001) (0.001) (0.000) (0.002)

exp upstreamness 0.052** 0.099** 0.081** 0.120** 0.171** 0.295**(0.005) (0.008) (0.007) (0.002) (0.002) (0.002)

priv. credit -0.011* 0.020** -0.014*(0.005) (0.006) (0.007)

Observations 183,068 5,362 79,597 33,560 15,963 953R-squared 0.670 0.421 0.577 0.706 0.708 0.783sample industries all mining mfg all mining mfgsample countries all all all ADB ADB ADB

Notes: Observations are at the bilateral country-sector level, for different years. All specifications with importer-year,and industry-year fixed effects. Robust standard errors, clustered by importer-exporter, in parentheses. Levels ofsignificance are denoted by ** p<0.01, * p<0.05, + p<0.1.

47

Table 14: Activities of foreign affiliate of U.S. MNCs, average by industry.

Number Mean Sd p25 p50 p75

Panel 1: Intensity of Vertical Activities

All 1296 0.000 1.758 −0.793 0.283 1.258Food, beverage, tobacco 184 −0.150 2.045 −1.116 0.262 1.312Textile and apparel 39 0.353 1.393 −0.591 0.528 1.510Chemicals 249 −0.345 1.681 −1.217 −0.102 0.849Glass and Stone 42 −0.113 1.477 −1.064 0.252 0.733Metal 39 −0.113 2.204 −0.742 0.565 1.359Metal products 88 −0.105 1.861 −1.031 0.278 1.257Machinery 141 0.323 1.629 −0.300 0.543 1.500Electronics 114 0.175 1.658 −0.739 0.456 1.429Electrical equip. 75 0.416 1.558 −0.407 0.771 1.731Transportation equip. 82 0.196 1.795 −0.326 0.525 1.418Other 243 0.022 1.689 −0.757 0.389 1.261

Panel 2: Intensity of Export Platform Activities

All 1053 0.000 1.821 −0.925 0.338 1.337Food, beverage, tobacco 148 −0.732 2.037 −1.678 −0.520 0.886Textile and apparel 30 0.883 1.114 0.356 1.175 1.735Chemicals 201 −0.519 2.002 −1.783 −0.271 0.998Glass and Stone 37 0.275 1.697 −0.944 0.609 1.469Metal 37 0.654 1.535 0.026 0.977 1.794Metal products 65 0.468 1.426 −0.162 0.621 1.525Machinery 126 0.287 1.712 −0.230 0.676 1.447Electronics 88 0.677 1.413 −0.025 0.857 1.770Electrical equip. 59 0.231 1.848 −0.896 0.775 1.585Transportation equip. 75 0.356 1.491 −0.603 0.579 1.512Other 187 −0.079 1.746 −0.907 0.080 1.208

Panel 3: Intensity of Local Horizontal Activities

All 1432 0.000 1.231 −0.279 0.483 0.774Food, beverage, tobacco 262 0.339 1.183 0.389 0.756 0.817Textile and apparel 47 0.063 0.991 −0.353 0.338 0.811Chemicals 265 0.200 1.021 0.101 0.624 0.785Glass and Stone 45 0.182 0.929 0.047 0.486 0.706Metal 50 0.006 0.918 −0.498 0.264 0.815Metal products 95 −0.156 1.266 −0.697 0.270 0.704Machinery 137 −0.485 1.347 −0.963 −0.059 0.491Electronics 119 −0.346 1.525 −0.728 0.174 0.694Electrical equip. 71 −0.288 1.485 −0.625 0.161 0.623Transportation equip. 82 −0.388 1.446 −0.821 −0.028 0.571Other 259 0.083 1.076 −0.035 0.490 0.745

Notes: "Vertical" refers to sales to related parties, "export platform" refers to sales to unrelated parties out-side the host market, and "local horizontal" refers to sales directed to the host market.The indexes are basedon the OLS regression of the corresponding dependent variable on country-industry fixed effects. The num-ber of observations and R-squared of the regressions are, respectively: 3,769 and 0.45 for panel 1; 2,655 and0.55 for panel 2; and 4,251 and 0.47 for panel 3. The mean of each index is normalized to zero.48

Table 15: Activities of foreign affiliate of U.S. MNCs, average by region.

Number Mean Sd p25 p50 p75

Panel 1: Intensity of Vertical Activities

All 1296 0.000 1.758 −0.793 0.283 1.258OECD 766 0.007 1.723 −0.755 0.289 1.206non-OECD 530 −0.010 1.809 −1.040 0.281 1.347North America 114 0.261 1.349 −0.346 0.350 1.238Latin American not Mexico 160 −0.229 1.816 −1.470 −0.013 1.175Europe 628 0.117 1.745 −0.530 0.430 1.323Africa 39 −0.274 2.182 −1.408 0.008 2.012Middle East 19 0.172 2.090 −2.104 1.118 2.192Asia 336 −0.177 1.784 −1.238 0.155 1.088

Panel 2: Intensity of Export Platform Activities

All 1053 0.000 1.821 −0.925 0.338 1.337OECD 641 0.122 1.807 −0.762 0.531 1.438non-OECD 412 −0.191 1.830 −1.204 0.083 1.156North America 92 −0.322 1.614 −1.281 −0.130 0.940Latin American not Mexico 115 −0.793 1.931 −1.565 −0.533 0.504Europe 556 0.429 1.650 −0.212 0.816 1.602Africa 32 −0.197 1.411 −1.033 −0.262 0.536Middle East 8 0.384 1.641 −0.904 0.643 1.715Asia 250 −0.458 2.002 −1.588 −0.110 1.139

Panel 3: Intensity of Local Horizontal Activities

All 1432 0.000 1.231 −0.279 0.483 0.774OECD 837 −0.092 1.239 −0.464 0.340 0.737non-OECD 595 0.129 1.211 0.036 0.600 0.797North America 116 −0.005 1.233 −0.174 0.403 0.685Latin American not Mexico 181 0.430 0.827 0.430 0.706 0.809Europe 691 −0.249 1.356 −0.703 0.163 0.713Africa 54 0.052 1.682 0.431 0.750 0.811Middle East 21 0.137 1.426 0.456 0.776 0.816Asia 369 0.241 0.924 0.142 0.609 0.792

Notes: "Vertical" refers to sales to related parties, "export platform" refers to sales to unrelated parties out-side the host market, and "local horizontal" refers to sales directed to the host market.The indexes are basedon the OLS regression of the corresponding dependent variable on country-industry fixed effects. The num-ber of observations and R-squared of the regressions are, respectively: 3,769 and 0.45 for panel 1; 2,655 and0.55 for panel 2; and 4,251 and 0.47 for panel 3. The mean of each index is normalized to zero.

49

Table 16: Determinants of activities of affiliates of U.S. MNC. OLS.

Dep variables D(vert) D(vert) D(exp_plat) D(exp_plat) D(vert) D(vert) D(exp_plat) D(exp_plat)(1) (2) (3) (4) (5) (6) (7) (8)

log dist -0.034+ -0.679 0.016 -0.995 -0.037+ -0.797 0.015 -1.091(0.020) (0.796) (0.038) (0.810) (0.020) (0.746) (0.037) (0.741)

D(share language) 0.036 -0.043 0.165** 0.222* 0.035 -0.026 0.164** 0.234**(0.026) (0.063) (0.038) (0.081) (0.026) (0.058) (0.037) (0.073)

D(rta) -0.021 -0.177 -0.100* -0.303+ -0.027 -0.192 -0.103* -0.320+(0.031) (0.149) (0.042) (0.167) (0.030) (0.142) (0.042) (0.152)

log rgdp 0.006 -0.096* 0.005 -0.123* 0.003 -0.100* 0.005 -0.127*(0.008) (0.043) (0.014) (0.048) (0.008) (0.041) (0.015) (0.044)

log rgdpl -0.026 -0.191** 0.066 -0.148* -0.024 -0.202** 0.065 -0.157*(0.023) (0.055) (0.045) (0.054) (0.023) (0.057) (0.045) (0.054)

log KY 0.084 0.257+ 0.324** 0.342** 0.106* 0.262+ 0.328** 0.354**(0.053) (0.126) (0.093) (0.114) (0.053) (0.128) (0.094) (0.107)

log yr.sch 0.004 -0.097 -0.197* -0.763+ 0.210 -0.427 -0.254+ -1.072*(0.046) (0.370) (0.094) (0.394) (0.136) (0.392) (0.134) (0.439)

rule of law 0.009 0.050+ -0.007 0.116** 0.009 0.057* -0.007 0.121**(0.008) (0.027) (0.023) (0.026) (0.009) (0.027) (0.023) (0.024)

exp. upstreamness 0.000 0.254 0.128** 0.254 -0.002 0.286 0.127** 0.280(0.025) (0.289) (0.042) (0.253) (0.025) (0.274) (0.042) (0.234)

KY × log ky -0.097+ 0.014 -0.032 -0.036(0.053) (0.044) (0.049) (0.037)

yr.sch × log sl -0.736+ 0.885+ 0.194 0.850(0.406) (0.437) (0.374) (0.813)

Observations 5,155 1,323 5,155 1,323 5,155 1,323 5,155 1,323R-squared 0.731 0.807 0.715 0.788 0.734 0.809 0.715 0.790sample all ADB all ADB all ADB all ADB

Notes: The variable D(X) denotes the share of affiliates with positive flows of activity X. "Vert" refers to sales to related parties, and"exp. plat" refers to sales to unrelated parties outside the host market. All specifications with industry fixed effects. Robust standarderrors, clustered by country, in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, + p<0.1.

50

References

[1] Alviarez, Vanessa. 2015. "Multinational Production and Comparative Advantage".Manuscript, UBC.

[2] Alfaro, Laura, Pol Antras, Davin Chor, and Paola Conconi. 2015. “InternalizingGlobal Value Chains: A Firm-Level Analysis, ” mimeo.

[3] Alfaro, Laura and Andrew Charlton. 2009. “Intra-industry Foreign Direct Invest-ment. ” American Economic Review 104(2): 459–494.

[4] Antras, Pol, and Davin Chor. 2013. "Organizing the Global Value Chain," Economet-rica 81 (6): 2127Ð2204.

[5] Antras, P., Chor, D., Fally, T., Hillberry, R., 360 2012. "Measuring Upstreamness ofProduction and Trade.” American Economic Review Papers and Proceedings, 102 (3):412—416.

[6] Antras, Pol. 2003. "Firms, Contracts, and Trade Structure." Quarterly Journal of Eco-nomics 118(4): 1375-1418.

[7] Antras, P., and Helpman, E., 2004. "Global Sourcing.” Journal of Political Economy112(3), 552—580.

[8] Arkolakis, C., N. Ramondo, A. Rodriguez-Clare, and S. R. Yeaple (2014). ÒInnova-tion and Production in the Global Economy.Ó Mimeo Penn State University.

[9] Bilir, Kamrar, Davin Chor, and Kalina Manova. 2014. "Host-Country Financial De-velopment and Multinational Activity." NBER Working Paper 20046.

[10] Blanchard, Emily, C. Bow, and R. Johnson. 2016, "Global Supply Chains and TradePolicy,", mimeo, Dartmouth College.

[11] Brainard, S. Lael. 1997. "An Empirical Assessment of the Proximity-ConcentrationTradeoff Between Multinational Sales and Trade." text itAmerican Economic Review86: 520-544.

[12] Carr, D.L., Markusen, J.R., Maskus, K.E. 2001. “Estimating the Knowledge-CapitalModel of the Multinational Enterprise.” American Economic Review 91, 693–708.

[13] Costinot, A., Oldensky, L., Rauch, J., 2011. “Adaptation and the Boundary of Multi-national Firms.” Review of Economics and Statistics 93, 298–308.

[14] Defever, Fabrice, and Alejandro Riano. 2015. “Protectionism through Exporting:Subsidies with Export Share Requirements in China.”CEP Discussion Paper No 1182.

[15] Fajgelbaum, P., Grossman, G., Helpman, E., 2015. “A Linder Hypothesis for ForeignDirect Investment. ” Review of Economic Studies 82.

[16] Fort, Teresa. 2015. "Technology and Production Fragmentation: Domestic versus For-eign Sourcing", mimeo.

51

[17] Grossman, G.M., Helpman, E., Szeidl, A., 2006. "Optimal Integration Strategies forthe Multinational Firm. ” Journal of International Economics 70, 216—238.

[18] Hanson, Gordon, Raymond Mataloni, and Matthew Slaughter. 2001. “ExpansionStrategies of U.S. Multinational Firms.” In Brookings Trade Forum, ed. in Dani Ro-drik and Susan Collins (Brookings Institution) pp. 245Ð282

[19] Hanson, Gordon, Raymond Mataloni, and Matthew Slaughter. 2005. "Vertical Pro-duction Networks in Multinational Firms,” Review of Economics and Statistics 87, 664–678.

[20] Helpman, Elhanan. 1984. "A Simple Theory of Trade with Multinational Corpora-tions." Journal of Political Economy 92: 451-471.

[21] Helpman, Elhanan (1985). "Multinational Corporations and Trade Structure." Reviewof Economic Studies 52: 443-457.

[22] Helpman, E., M. Melitz, and S.R. Yeaple. 2004. “Export versus FDI with Heteroge-nous Firms,” American Economic Review 94(1): 300–316.

[23] Horstmann, I., and J. Markusen. 1992. "Endogenous Market Structures in Interna-tional Trade (natura facit saltum)." Journal of International Economics 32(1-2): 109-129.

[24] Irrarazabal, A., A. Moxnes, and L. Opramolla. 2013. "The Margins of MultinationalProduction and the Role of Intra-Firm Trade." Journal of Political Economy 121(1): 74–126

[25] Johnson, R., and G. Noguera. 2012. “Accounting for Intermediates: Production Shar-ing and Trade in Value Added,” Journal of International Economics 86 (2).

[26] Johnson, Robert. 2014. “Five Facts about Value-Added Exports and Implications forMacroeconomics and Trade Research,” Journal of Economic Perspective 28(2): 119-142.

[27] Inter-American Development Bank. 2015. “Synchronized Factories—Latin Americaand the Caribbean in the Era of Global Value Chains, ” Juan S. Blyde Editor

[28] Koopman, Robert, Zhi Wang, and Shang-Jin Wei. 2014. “Tracing Value-Added andDouble Counting in Gross Exports” American Economic Review 104(2): 459–494.

[29] Manova, Kalina 2013. “Credit Constraints, Heterogeneous Firms, and InternationalTrade,” Review of Economic Studies 80: 711—744.

[30] Markusen, James. 1984. "Multinationals, Multi-Plant Economies, and the Gains fromTrade." Journal of International Economics 16(3-4): 205-226.

[31] Nunn, Nathan. 2007. "Relationship-Specificity, Incomplete Contracts, and the Patternof Trade, ” Quarterly Journal of Economics 122(2): 569—600.

[32] Nunn, N., and D. Trefler. 2008. "The Boundaries of the Multinational Firm: An Em-pirical Analysis," in: Helpman, E., Marin, D., Verdier, T. (Eds.), The Organization ofFirms in a Global Economy. Harvard University Press, 55–83.

52

[33] Nunn, N., and D. Trefler. 2014. "Domestic Institutions as a Source of ComparativeAdvantage" Handbook of International Economics, Gita Gopinah, Elhanan Help-man, and Kenneth Rogoff, eds. Vol. 4, Chapter 5.

[34] Rajan, R., and L. Zingales. 1998. “Financial Dependence and Growth,” AmericanEconomic Review 88: 559—86.

[35] Ramondo, N., V. Rappoport, and K. Ruhl. 2016. "Intrafirm Trade and Vertical Frag-mentation in U.S. Multinational Corporations". Journal of International Economics,98(1): 51-59.

[36] Ramondo, N. 2014. "A Quantitative Approach to Multinational Production". Journalof International Economics, 93(1): 108—122.

[37] Ramondo, N., and A. Rodriguez-Clare. 2013. "Trade, Multinational Production, andthe Gains from Openness". Journal of Political Economy, 121(2): 273-322.

[38] Ramondo, N., A. Rodriguez-Clare, and F. Tintelnot. 2015. "Multinational Production:Data and Stylized Facts". American Economic Review Papers and Proceedings, 105(5):530—36.

[39] Tintelnot, Felix. 2012. "Global Production with Export Platforms." Mimeo Universityof Chicago.

[40] Wang, Zhi, Shnag-Jin Wei, and Kunfu Zhu. 2014. "Quantifying International Produc-tion Sharing at the Bilateral and Sector Level," NBER Working paper 19677.

[41] Yeaple, Stephen. 2006. "Offshoring, Foreign Direct Investment, and the Structure ofU.S. Trade.". Journal of European Economic Association 4, 602–611.

A Appendix: Determinants of U.S. intra-firm imports

We use data from the U.S. Census Bureau on related and unrelated party trade. These datahint directly to the magnitude of the activities that the MNC chooses to keep in-house vsthe activities that it decides to outsource to foreign third parties along the GVC.

The data are by country, 6-digit NAICS industry, for the period 2000-2011. The depen-dent variable of interest is the ratio of related-party to total imports to the United States, atthe country-industry-year level. Alternately, we also construct a Grubel-Lloyd index thattakes into account also the share of intra-firm exports from the United States; an index ofzero indicates that intra-firm trade happens mostly two ways, while one closer to one in-dicates that these trade happens mostly one way. While a low Grubel-Lloyd index can beinterpreted as the GVC kept within the firm, a higher magnitude for this variable wouldindicate that the MNC uses in-house as well as outside suppliers for the production of itsdifferent stages of production.

53

Table 17 shows results coming from estimating by OLS the following type of equation:

logZnit = β1Yni + β2Cn + β3Cn × Ii +Dit + unit. (10)

The variableZnit stands for measures of trade to the U.S. from related parties from countryn in year t, and industry i. In the tables that follow, Znit indicates, alternately: intra-firmimports as a share of total imports, ifm; and the Gruber-Lloyd index, gl, which also usesthe flows of related-party exports from the U.S. to country n and industry i. The variableYni captures controls that vary at the industry-country level, such as tariffs. The variablesCc and Ii denote, respectively, country and industry controls. As all the variables arecross-sectional, we add an industry-year fixed effect. We run two specifications: oneusing the whole sample and another using only observations for Asian ADB members.

Table 18 presents results from estimating an OLS specification similar to the one in 10but concentrating in industry, rather than country, variables,

logZnit = β1Yni + β2Ii +Dnt + unit. (11)

Country variables Cn are replaced by a set of industry variables Ii, such as capital in-tensity of the industry, and replace the industry-year fixed effects by country-year fixedeffects. Interactions between country and industry characteristics are removed for sim-plicity. This table is aimed at simply analyzing which industries characteristics are theones determining the presence of intra-firm trade.

Table 17 indicates that higher trade costs, in the form of higher tariffs or higher ship-ping costs, deter intra-firm trade into the United States, more so than arm-length trade.Trade from industries and countries with higher trade costs is also more likely to be "twoways"; that is, the Gruber-Lloyd (GL) index decreases toward zero with trade costs. In-come level of the trade partner significantly decreases the GL index when we restrict thesample to import from ADB Asian countries. This means that richer countries in thearea are the ones with lower GL indices so that there is a lot of back-and-forth intra-firmtrade—and presumably more engagement in the GVC. Market size has a positive im-pact on both measures of intra-firm trade: The larger the importer, the larger the share ofintra-firm imports as a share of total US imports and more asymmetry in those intra-firmflows. The degree of export upstreamness of a country seems to have opposite effectswhen we consider all countries and only ADB Asian countries: For this second group ofcountries, if the country specializes in early stages of the GVC, intra-firm imports to theUnited States are a larger share of total trade and the GL index is on average increasingtoward one. The institutional environment of the country, measured by the rule of law,

54

affects intra-firm flows depending on the industry characteristics regarding: contract in-tensity ("contract x ruleoflaw"), degree of intermediation ("intermediation x ruleoflaw"),degree of routiness ("routiness x ruleoflaw"), dependence on external capital ("ext.dep.x ruleoflaw"), and tangibility of assets ("tangible x ruleoflaw"). The importance of theavailability of private credit also varies according to some industry characteristics, suchas the dependence of external credit ("ext.dep. x private credit") and tangibility of assets("tangible x private credit"). As usual, to capture Heckscher-Ohlin-type forces regardingdifferent factor endowments of countries and different factor intensities of industries, forcapital and skill levels, the interactions "k.int x log KL" and "skill.int x yr.sch" are included.

Table 18 focuses explicitly on how industry characteristics affect the flows of intra-firm trade; country characteristics are subsumed in the country-year fixed effects. It isworth noting in this table that the R&D intensity of an industry, as well as the amount ofequipment capital (e.g. computers) per worker needed, and the degree of upstreamness(i.e. the position of the industry in the input-output matrix), positively affects the amountof activities the MNC chooses to perform within its boundaries. These variables alsopositively affect the GL index, stimulating more intra-firm trade in only one direction,more likely imports. The same is true for the skill intensity of the industry, measuredby the share of skilled to unskilled workers ("log sl"). The larger incidence of financialfrictions, measured by higher external financial dependence or lower asset tangibility,have a positive effect on intra-firm import shares. As expected, the measures of productcontractibility, "intermediation" and "contractibility", are negatively associated with theextent to which foreign inputs purchases are done within the MNC. The variable "inputsubstitutability" is meant to capture the degree of input substitutability in an industry: Asexpected, the higher the input substitutability, the lower the share of inputs sourced fromwithin the MNC, and the more asymmetric the flows of intra-firm trade.

55

Table 17: Determinants of U.S. intra-firm imports: country characteristics. OLS.

Dep Var share of related-party imports, in total U.S. imports Gruber-Lloyd index, related-party U.S. trade(1) (2) (3) (4)

log freight costs -0.152* -0.175** -0.132** -0.114+(0.060) (0.049) (0.042) (0.056)

log tariff -0.001 -0.000 -0.001** 0.000(0.000) (0.001) (0.000) (0.001)

log rgdpl 0.028 -0.024 0.053+ -0.110*(0.031) (0.053) (0.032) (0.048)

log rgdp 0.020* -0.007 0.060** 0.042**(0.010) (0.009) (0.008) (0.007)

log KL -0.019 -0.028 -0.039 0.062(0.028) (0.046) (0.028) (0.046)

ruleoflaw -0.011 0.038 0.044 0.236**(0.034) (0.049) (0.029) (0.049)

private.credit 0.001+ 0.002** 0.000 -0.001+(0.000) (0.000) (0.000) (0.000)

log yr.sch 0.018* -0.025 0.017+ -0.028*(0.009) (0.020) (0.010) (0.011)

employment.flexibility -0.064 -0.286* 0.081 -0.032(0.062) (0.106) (0.052) (0.066)

exp.upstr -0.055* 0.145** -0.062** 0.071*(0.024) (0.048) (0.022) (0.033)

k.int x log KL 0.001 0.005 -0.003 0.001(0.003) (0.003) (0.003) (0.004)

skill.int x log yr.sch 0.019 -0.073 0.036 -0.102*(0.023) (0.049) (0.026) (0.047)

contractibility x ruleoflaw 0.008 -0.002 -0.006 -0.066*(0.012) (0.018) (0.014) (0.030)

intermediation x ruleoflaw -0.043* -0.006 -0.077** -0.106+(0.021) (0.041) (0.025) (0.054)

routiness x ruleoflaw -0.066** 0.008 -0.025 0.058(0.018) (0.043) (0.018) (0.046)

routiness x yr.sch 0.134* -0.201+ 0.066 -0.122(0.053) (0.110) (0.052) (0.114)

external.dependence x private.credit -0.009 -0.007 -0.010 0.022(0.017) (0.026) (0.015) (0.022)

asset.tangibility x private.credit -0.183* -0.178+ 0.020 0.313+(0.092) (0.091) (0.106) (0.154)

external.dependence x ruleoflaw 0.015 -0.004 0.011 -0.004(0.010) (0.015) (0.009) (0.010)

asset.tangibiblity x ruleoflaw 0.100+ 0.124* -0.021 -0.180**(0.051) (0.049) (0.052) (0.060)

Observations 162,032 40,599 121,400 31,475R-squared 0.188 0.335 0.237 0.265sample all adb all adb

Notes: The variable "ifm" refers to the All specifications with industry-year fixed effects. Robust standard errors, clustered by country, in parentheses. Levelsof significance are denoted by ** p<0.01, * p<0.05, + p<0.1.

56

Table 18: Determinants of U.S. intra-firm imports: industry characteristics. OLS.

Dep Var share of related-party imports, in total U.S. imports Gruber-Lloyd index, related-party U.S. trade(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

log freight costs -0.845** -1.256** -0.857** -1.245** -0.581** -1.024** -0.421** -0.658** -0.423** -0.629** -0.383** -0.574**(0.134) (0.153) (0.128) (0.154) (0.132) (0.151) (0.120) (0.173) (0.109) (0.163) (0.133) (0.189)

log tariffs -0.002** -0.003* -0.001* -0.002* -0.002* -0.002+ -0.002** -0.002** -0.002** -0.002** -0.002** -0.003**(0.000) (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

import.demand elast -0.001* -0.000 -0.001 -0.000 -0.001* -0.000 -0.001** -0.002** -0.001* -0.001 -0.001* -0.001(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.001) (0.000) (0.001)

R&D int 0.010** 0.013** 0.009** 0.012** 0.004 0.007 0.006 0.008 0.006* 0.009* 0.007* 0.011*(0.004) (0.004) (0.003) (0.004) (0.003) (0.004) (0.003) (0.005) (0.003) (0.004) (0.003) (0.004)

log sl 0.016 0.036* 0.026 0.043* 0.014 0.026 0.012 0.013 0.020 0.016 0.028+ 0.025(0.016) (0.018) (0.017) (0.019) (0.016) (0.019) (0.014) (0.021) (0.012) (0.020) (0.014) (0.024)

log kl 0.020** 0.028** -0.000 -0.004(0.006) (0.006) (0.006) (0.009)

log buildl -0.014 -0.037* -0.006 -0.026 -0.047** -0.079** -0.050** -0.084**(0.015) (0.017) (0.015) (0.018) (0.013) (0.021) (0.013) (0.020)

log equipl 0.024* 0.057** 0.029* 0.056** 0.031** 0.055** 0.031* 0.055**(0.012) (0.015) (0.014) (0.017) (0.012) (0.018) (0.012) (0.019)

upstreamness 0.023** 0.009 0.021** 0.010 0.020** 0.025* 0.013+ 0.013(0.007) (0.009) (0.008) (0.009) (0.006) (0.010) (0.007) (0.011)

input substitutability -1.851 -3.935+ -1.545 -4.119* 2.897 4.224 2.820 4.397(2.204) (2.145) (2.106) (2.028) (2.026) (2.904) (2.077) (3.083)

log vadd-to-vship 0.030 -0.033 0.058 -0.035 0.043 0.137* 0.071+ 0.183*(0.048) (0.059) (0.048) (0.061) (0.041) (0.069) (0.042) (0.072)

external.dependence 0.006 0.027 -0.030** -0.047**(0.014) (0.020) (0.011) (0.017)

asset.tangibility -0.161* -0.050 -0.038 -0.082(0.069) (0.072) (0.070) (0.105)

routiness 0.072* 0.060 0.017 0.016(0.030) (0.037) (0.023) (0.041)

intermediation -0.201** -0.172** -0.156** -0.211*(0.058) (0.061) (0.059) (0.090)

contractability -0.062* -0.096** 0.029 0.062(0.030) (0.035) (0.026) (0.040)

Observations 227,829 49,007 225,738 48,571 225,691 48,568 185,585 39,405 184,546 39,187 184,546 39,187R-squared 0.180 0.224 0.184 0.230 0.190 0.238 0.262 0.147 0.271 0.162 0.274 0.168sample all adb all adb all adb all adb all adb all adbNotes: All specifications with country-year fixed effects. Robust standard errors, clustered by industry, in parentheses. Levels of significance are denoted by ** p<0.01, * p<0.05, +p<0.1.

57