The rise of vertical specialization trade

Journal of International Economics 86 (2012) 133–140

Contents lists available at SciVerse ScienceDirect

Journal of International Economics journal homepage: www.elsevier.com/locate/jie

The rise of vertical specialization trade ☆ Benjamin Bridgman ⁎ U.S. Department of Commerce, Bureau of Economic Analysis, Washington, DC 20230, United States

a r t i c l e

i n f o

Article history: Received 21 June 2010 Received in revised form 23 August 2011 Accepted 23 August 2011 Available online 28 August 2011 JEL classification: F1 Keywords: Trade costs Vertical specialization Manufacturing trade

a b s t r a c t Manufacturing and vertical specialization (VS) trade, trade in goods that incorporate imported inputs, have grown rapidly since the 1960s. I argue that declining trade costs are an important explanation for these facts. I present a three stage vertical specialization trade model, with raw materials, manufactured parts and final goods sectors. In the simulated model, falling trade costs explain much of the observed growth in overall and VS trade. Manufacturing trade grows twice as fast as overall trade. Raw materials trade was more important in the 1960s when trade costs were high, since their production is more strongly linked to endowments than manufacturing. Therefore, materials will be traded even when trade costs are high. Trade costs have fallen more for manufactured goods over the last 40 years, leading to a rapid expansion of manufactured parts trade relative to materials. Published by Elsevier B.V.

1. Introduction Trade in manufactured goods has expanded rapidly in the last fifty years (Bergoeing et al., 2004). U.S. manufacturing export share of GDP grew by 140% between 1960 and 2006. The share of manufacturing output that is exported quadrupled during that period. This fact is puzzling given that manufacturing has not grown as a nominal share of output. Early on, when manufacturing was a large part of production, there was little trade in manufactured goods. Later, when manufacturing declined in importance, trade became dominated by these goods. At the same time, vertical specialization (VS) trade, trade in goods incorporating imported inputs, has expanded rapidly (Feenstra, 1998; Hummels et al., 1998, 2001). VS trade share of U.S. exports grew from 6% in 1972 to 12% in 1997 (Chen et al., 2005). VS trade growth is not due to a large increase in the share of intermediate goods trade. 1 Chen et al. (2005) find that share of trade accounted for by intermediate goods has been nearly constant since 1972.

☆ I thank two anonymous referees and seminar participants at the Federal Reserve Board, Federal Reserve Bank of Kansas City, 2009 International Industrial Organization Conference and the North American Summer Meetings of the Econometric Society. Brian Moyer kindly provided a data concordance. The views expressed in this paper are solely those of the author and not necessarily those of the U.S. Bureau of Economic Analysis or the U.S. Department of Commerce. ⁎ Tel.: + 1 202 606 9991; fax: + 1 202 606 5366. E-mail address: [email protected]. 1 Intermediate goods are those used as inputs to further production. In terms of input–output tables, they are goods that are shipped to production sectors rather than final demand. 0022-1996/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.jinteco.2011.08.016

I argue that the rise of manufacturing and VS trade are related: Both are driven by falling costs of trading manufactured parts. Prior to the Kennedy Round, U.S. trade was dominated by raw materials.2 Tariffs were high on manufactured goods, including parts. Materials faced high freight costs, since they have a low value to weight ratio. However, they were still imported because the ability to produce them is strongly linked to endowments. Materials cannot reliably be replaced domestically and were essential for production. Manufactured goods are easier to replace with domestic goods since they are less dependent on endowments. The Kennedy Round focussed on reducing manufacturing tariffs and was notable both for the size and coverage of these cuts. Since then, trade policy has gone from being biased against manufactured goods to being more neutral. Since manufactured goods are more responsive to trade barriers, manufacturing trade has grown faster than materials trade. The share of trade in intermediate goods has been roughly constant, but intermediate goods trade is now dominated by manufactured inputs. This paper presents a tractable general equilibrium model with Ricardian trade in intermediate goods. There are two countries with three layers of production: Raw materials are inputs to intermediate goods, which in turn are inputs to final consumption goods. All three types of goods may be traded, but incur an iceberg transportation cost and may face tariffs. I calibrate the model and run simulations using data on freight costs and tariffs. The simulated model predicts nearly all of the empirical growth in trade and the change in trade composition from 1967 to 2002.

2

The composition of intermediate goods trade is documented in detail below.

134

B. Bridgman / Journal of International Economics 86 (2012) 133–140

Manufacturing trade grows much faster than overall trade growth. While overall share of goods output that is traded more than doubles between 1967 and 2002 in the baseline simulation, manufacturing trade share triples. VS trade also grows rapidly, more than doubling from 1972 to 1997. Beginning with the Kennedy Round, manufactured goods tariffs fell more than non-manufactured goods tariffs. Lower trade costs on manufactured parts led to VS trade growth. While VS trade grows rapidly, intermediate goods' share of trade does not increase. Intermediate goods trade shifts from being dominated by raw materials to manufactured parts. Raw materials production tends to depend on local geographical conditions in a way that manufacturing does not. Therefore, raw materials will be traded even when trade costs are high. Combined with the fact that trade costs for raw materials fell less, most trade expansion is due to manufactured parts. Examining the impact of tariffs and transportation costs separately, falling tariffs have a stronger effect on the growth of both manufacturing and VS trade. Specifically, falling tariffs on manufactured parts lead to their offshoring while falling freight costs alone do not. Other papers have studied the rise of manufacturing trade. Bergoeing and Kehoe (2003) find that a monopolistic competition model of trade cannot explain increasing manufacturing trade. Dalton (2009) examines the impact of Just-in-Time (JIT) inventories on the expansion of manufactured goods trade. His model is able to generate a level shift in manufacturing trade in the early 1980s when JIT is adopted, but does not generate the empirical pattern of trade expansion over the period considered in this paper. The paper contributes to the historical measurement of the structure of trade protection. Examples include Anderson (1972) and Irwin (2007). It presents estimates of trade costs of goods by final and intermediate uses. Supplementary tables used in the calculation of the input–output (IO) tables provide estimates of trade costs by IO commodity. These supplementary tables can be combined with the IO tables to generate estimates of the structure of protection. U.S. foreign trade statistics do not provide detailed data on freight costs before 1974, so historical data are very thin (Hummels, 2007). There is a large literature investigating postwar trade growth, including Rose (1991), Krugman (1995), Baier and Bergstrand (2001), Bergoeing and Kehoe (2003) and Alessandria and Choi (2010). Models incorporating VS trade, such as Yi (2003) and Bridgman (2008) have been successful at resolving the puzzle that tariffs have not fallen enough to generate the observed trade growth given estimates of the Armington elasticity (Armington, 1969), the aggregate elasticity of substitution between domestic and foreign goods. However, they have not emphasized the structure of trade expansion. While Bergoeing et al. (2004) speculate that a VS model could generate that change in composition, they do not pursue the issue. A number of papers have examined the importance of intermediates trade for a number of issues including development (Jones, 2008; Goldberg et al., 2008; Estevadeordal and Taylor, 2008), firm

Fig. 2. Materials share of intermediate goods imports.

productivity (Amiti and Konings, 2007), trade elasticities (Ramanaryanan, 2006), business cycle co-movement (Kose and Yi, 2001), and the border effect in gravity equations (Yi, 2010). Grossman and RossiHansberg (2008a, 2008b) examine the growth of trade in intermediate services. A number of papers have used input–output tables to examine the factor content of trade, including Trefler and Zhu (2000) and Reimer (2006). Theoretical models of vertical specialization trade include Dixit and Grossman (1982) and Sanyal (1983). Unlike these papers, I examine the change in the composition of intermediates trade. 2. Intermediate goods trade and trade costs facts This section documents the change in the composition of intermediates trade and the structure of trade costs for goods by use. 2.1. Composition of intermediate goods trade Intermediate goods trade has shifted from being dominated by raw materials to manufactured parts. Fig. 1 shows the nominal share of materials (agricultural and mining products) of U.S. intermediate goods imports. 3 Imports are dominated by such raw materials early in the period. After the 1950s, the composition of imports began to shift significantly. Materials fell from over half of imported intermediate goods to less than a quarter in the 1990s. These data likely underestimate the real decline in the importance of materials in intermediate goods trade. The data are reported in current dollars so they are vulnerable to swings in commodity prices, especially oil. The run-up in materials share in the 2000s is driven by oil prices: non-fuel materials share shows a slight decline during this period. (Data constraints do not allow removing fuels from the full time series.) The spike in 1982 is also likely driven by high oil prices. The decline in the importance of raw materials is not restricted to the United States. Fig. 2 shows similar data for three major economies. 4 These data are reported in constant prices, so are not vulnerable to variations in commodity prices. (No such data exist for the United States.) All three show a decline in the importance of materials imports. 2.2. The structure of protection I now turn to the structure of protection from tariffs and transportation costs for intermediate and final goods. I use input–output

Fig. 1. Materials share of U.S. intermediate goods imports, 1925–2005.

3 Up to 1955, estimates are share of natural resource goods in non-final manufactured imports using data from Vanek (1963). From 1967 on, the estimates are the share of imported intermediate goods used by goods producing (agriculture, mining and manufacturing) industries that originate from materials (agriculture and mining) industries using IO tables. Sources and full details of the estimates are given in Appendix A. 4 In the following Figure, the estimates are the share of imported intermediate goods used by goods producing industries that originate from materials industries using OECD constant currency input–output tables. See Appendix A for details.

B. Bridgman / Journal of International Economics 86 (2012) 133–140

3. Model

Table 1 Weighted U.S. import costs. Variable All imports Tariff Freight Interm. (mfg.) Tariff Freight Interm. (non-mfg.) Tariff Freight Final Tariff Freight

1967

1972

1992

1997

2002

7.1 7.4

5.9 5.3

2.6 4.0

2.2 3.3

0.7 3.4

7.1 7.3

5.8 4.9

2.7 4.1

2.0 4.4

0.8 3.9

4.1 10.8

3.1 9.9

0.4 10.9

0.9 7.1

0.1 3.5

8.6 5.8

6.4 4.7

2.6 3.4

2.4 2.8

2.1 2.3

tables to split goods by use and estimate their trade costs. The tariff and transportation margins on imports are calculated as a supplementary table in the compilation of the input–output tables, since the margins need to be allocated to their producing industries: Wholesale trade for tariffs and transportation services for transportation. This table is not reported for all benchmark years, but they are for 1967 (pre-Kennedy Round) and 1972 (post-Kennedy Round). They can also be calculated for 1992, 1997 and 2002. These margins are matched to the IO tables. 5 I assume that imported commodities are used at the same rate for intermediate and final production as aggregate supply of that commodity. This assumption is equivalent to assuming that the imported share of a commodity is the same for both final and intermediate goods. 6 The trade weighted import cost is given by: ∑i τi yiImp sUse i ∑i yiImp sUse i

135

ð2:1Þ

where τi is the tariff rate, yiImp is imports and siUse is the share of the domestic supply of commodity i that for that use (intermediate or final). Freight costs fi are weighted in a similar fashion. As can be seen from Table 1, tariffs prior to the Kennedy Round protected manufacturers and allowed raw materials to enter at relatively low tariffs. (The Kennedy Round was agreed to in 1967 and implemented over the next five years, so the 1967 to 1972 comparison gives an indication of its effects.) This tariff structure was a long standing feature of trade policy (Irwin, 2007). Since then, trade policy has become more neutral with all goods facing similar, low tariffs. The discriminatory tariff rates are to a large degree undone by higher freight costs for non-manufactured goods. Most raw materials are bulky and of low value. This finding is consistent with those of Yeats (1977). As found in Hummels (2007), freight rates have not fallen as rapidly as tariffs. There are significant differences across types of goods. Freight costs for manufactured goods have fallen by much more than for raw materials. Manufactured goods freight costs fell in half while raw materials show no downward trend. This finding is consistent with the containerization revolution reducing the cost of non-bulk items (Levinson, 2006). The overall protection profile (tariffs plus freight) has gone from somewhat protecting manufacturing and final goods producers to protecting raw materials producers. The tariffs on all goods have declined nearly to zero. Freight for manufacturing has fallen significantly, especially for final goods. Freight costs remain relatively high for materials. 7

The model features two countries with representative households. Production occurs in three stages. Each country produces a raw material unique to that country. These materials are inputs to a continuum of intermediate goods that are common to both countries. The intermediate goods are inputs to country specific final consumption goods. All three types of goods may be traded, but incur an iceberg transportation cost and may face tariffs. 3.1. Households There are two countries each with a representative household. Households have preferences over a consumption good represented by:
 h
ρ
ρ i1 i i i i i i ρ U C 1 ; C 2 ¼ ϕ1 C1 þ ϕ2 C2

ð3:1Þ

where Cji denotes consumption good j ∈ {1, 2} for country i ∈ {1, 2}. The associated prices are Pc,i j. ϕji is a home bias parameter, where ϕji = ϕ if j = i and ϕji = 1 − ϕ if j ≠ i. Each country is endowed with labor N i. The wage is W i. 3.2. Raw materials sector i Each country can use labor Nm to produce a raw material good Mji i with a price Pm, j. Each country can only produce the good with its name: j = i. Output is given by Ymi = A im Nmi.

3.3. Manufactured parts sector There is a continuum of manufactured parts x i(z) with a price Px,i j(z) for z ∈ [0, 1]. Each country is endowed with technologies that combine materials inputs Mji, j ∈ {1, 2} and labor Nxi(z) to produce parts. Total output of part z is given by:

i Yx ðzÞ

¼

0 ! 1 11−α
α
σ σ i i A Nx ðzÞ @ ∑ Mj ðzÞ :

i Ax ðzÞ

j

ð3:2Þ

1 The productivity parameters are given by A1 ðzÞ ¼ and ð1 þ zÞθ 1 A2 ðzÞ ¼ , a variant of the mirror image technology in Bridgman ð2−zÞθ (2008) which is based on Dornbusch et al. (1977) and Eaton and Kortum (2002). The parameter θ governs the relative comparative advantage of the two countries. 3.4. Consumption goods sector Manufactured parts can be assembled into consumption goods using labor Nci. As with material goods, each country can only produce the good with its name: j= i. The total output is given by the technology:
α  1
 1−αc c i i i i ∫ ln x ðzÞ dz Yc; j ¼ A c Nc

ð3:3Þ

0

for i = 1, 2 and j = i. The associated price is Pc,i j. 5

Appendix A provides detail on data sources and calculations. This assumption is widely used in the literature. For example, the OECD uses it to split the IO tables into domestic and foreign sources. 7 The significant decline in non-manufacturing intermediate freight costs in 2002 is largely due to the run up in oil prices. Excluding oil products raises the freight rate to 5.7%. Bridgman (2010) shows that freight rates for oil are negatively related to oil prices, since rates are charged by volume. 6

3.5. Transportation sector The countries may trade the goods they produce with each other by incurring an iceberg transportation cost specific to that good: fk for k ∈ {m, x, c}.

136

B. Bridgman / Journal of International Economics 86 (2012) 133–140

3.6. Government

4.2. Solution

The countries each have a government that can impose an ad valorem (net of transport fees) tariff τki on traded goods k ∈ {m, x, c}. The government gives the domestic representative household transfers T i and maintains budget balance.

The two countries are mirror images in manufactured parts production. There is a symmetric equilibrium with a closed form solution when the parameters are the same in the two countries. Specifically, if the parameters N i, τki, Aki for k ∈ {m, x, c} and are constant across the two countries, there exists an equilibrium where C11 = C22, 1 2 1 2 1 2 1 2 C12 = C21, Pm, 1 = Pm, 2, W = W , Pc, 2 = Pc, 1 and Pc, 1 = Pc, 2. Prices and quantities in the parts and materials sectors across the countries mirror each other: Px1(z) = Px2(1 − z), etc. In the rest of the paper, I examine this symmetric equilibrium. I denote the common parameters and quantities (for example, N i and W i) by omitting the i superscript (for example, τ 1 = τ 2 = τ) and 1 normalize price of country one's material good to one (Pm, 1 = 1). 1 1 This implies that the wage W ¼ . Define ¯z i as the cutoff industry Am in country i such that manufactured parts z N ¯z 1 and z b ¯z 2 will be imported. Given the functional forms,

4. Equilibrium 4.1. Definition Households sell labor and purchase goods. They choose C1i and C2i to maximize U(C1i , C2i ) subject to the budget constraint i

i

i

i

i

∑ Pc; j Cj ¼ W N þ T :

ð4:1Þ

j

Materials firms buy labor and sell materials. They face competitive markets and solve:

1

¯z 1 ¼ 1− ¯z 2 ¼ i

i

i

i

i

MaxPm;i Am Nm −W Nm :

ð4:2Þ

2ð1 þ τx þ fx Þθ −1 1 θ

ð1 þ τx þ fx Þ þ 1

:

ð4:8Þ

Parts exports are given by: Manufactured parts firms face competitive markets and solve: 0 ! 1 11−α
α
σ σ i i i i i i i i A MaxPx;i ðzÞAx ðzÞ Nx ðzÞ @ ∑ Mj ðzÞ −W Nx ðzÞ− ∑ Pj Mj ðzÞ: j

h i 1 ðAm N þ T Þ 1 þ fx þ ð1 þ τx þ fx Þ1−ρ z¯ 2 h i : 1 ð1 þ τx þ fx Þ 1 þ τx þ fx þ ð1 þ τx þ fx Þ1−ρ

j

ð4:3Þ

0

−i

i

i

ð4:5Þ

−i where Pm, i is the price of the materials in the other country. Parts and consumption goods exporters solve a similar problem. Feasibility for each consumption good requires that for j = 1, 2:

j

j

i

j

f c C −j þ ∑ C j ¼ Y c

Am N þ T 
1  1−ρ ϕ Pc 1 þ τc þ fc þ ð1 þ τc þ fc Þ 1−ϕ

ð4:10Þ

ð4:4Þ

Transportation firms buy domestic goods and sell exports. Materials exporters face competitive markets and solve: −i

2

C2 ¼ C1 ¼


α

 1−α c c i i i 1 i i i 1 i i ∫ ln x ðzÞ dz −W Nc −∫ P ðzÞx ðzÞ dz: Max Pc;i A c Nc

MaxPm;i Mi −Pm;i M i ð1 þ fm Þ

Consumption goods exports are given by: 1

For j = i, consumption goods firms solve:

0

ð4:9Þ

where Pc,1 1 = Pc,2 2 = Pc. Tariffs in the United States are collected on the FOB value of goods (the value before transport costs are added). Therefore,   1 1−σ 1 τ m Am N 1 þ τm þ fm T¼    σ  ð1−αÞð ¯z 1 þ ð1 þ fx Þ ¯z 2 Þ 1−σ 1 1þ 1 þ τ m þ fm Am Nτc þ NAm τx ð1− ¯z 1 Þ þ 
 1 : 1−ρ ϕ 1 þ τc þ fc þ ð1 þ τc þ fc Þ 1−ϕ

ð4:11Þ

ð4:6Þ

i¼1;2

5. Results j where − j is the other country. The term fcjC − j is the amount of consumption used to pay the iceberg cost to ship the good. There is a corresponding feasibility constraint for parts that are exported and materials production. Labor feasibility requires that labor sums to the total population.

i

i

i

1

i

N ¼ Nc þ Nm þ ∫ Nx ðzÞdz: 0

ð4:7Þ

The definition of equilibrium is standard. Definition 1. Given tariffs, an equilibrium is consumption, parts and materials goods allocations and prices in each period such that: 1. Households solve their problem, 2. Materials, parts, consumption goods and transportation firms solve their problem, 3. The government balances its budget, 4. The allocation is feasible.

This section calibrates the model and presents the results of the simulations. In the calibration, I follow the convention of Yi (2003) and interpret the two countries as the United States and other industrialized countries (the EC plus Japan). I will generally use U.S. data to select parameters since these data are easier to obtain. In particular, I only have structure of protection data for the United States. In addition, aggregating some of the data concepts, such as vertical specialization trade, is difficult across multiple countries. The industrial structures of these countries are very similar and production parameters do not appear to vary significantly across even countries with very different industrial structures. (For example, see Gollin (2002).) Therefore, using U.S. parameters should be a reasonable proxy. The model abstracts from the service sector since vertical specialization over the period I examine is dominated by goods trade. Therefore, I use goods GDP as the data concept that matches the model's GDP, with agricultural and mining sectors matching the model's

B. Bridgman / Journal of International Economics 86 (2012) 133–140

materials sector and manufacturing matching the manufactured parts and final goods sectors. The model also abstracts from capital. The industrial countries are at similar levels of development so differences in capital are unlikely to be a significant factor in the impact of falling trade costs on trade growth. Since there is no capital, labor is the only source of value added. In the calibration, the model's labor stands in for all empirical value added. 5.1. Calibration The model's parameters are selected as follows. Waugh (2010) examines data for a number of countries (including the United States) and finds that the value added share of manufacturing gross output is 0.33. I set the share of intermediate goods in consumption goods production αc equal to 0.33. In the model, the only intermediate inputs into manufactured parts production are raw materials. In I–O tables, the largest source of an industry's intermediate shipments is typically itself. To match the value added share of parts manufacturing α, we need to remove the non-raw materials portion of intermediates. Let produc¯ tion function of parts in the I–O table be x ¼ nα¯ mβ¯ x1−¯α −β . I will set α ¯ α¼ α ¯ þβ ¯. P P Based on Waugh (2010), I set α ¼ 0:33. To calculate β, I calculate the share of intermediate shipments to manufacturing originating from materials industries (agriculture and mining). Using the 1967 U.S. direct transactions I–O table, this share is 21%. Therefore, I calculate that ¯β ¼ 0:21⁎ð1− α ¯ Þ ¼ 0:21⁎ð1−0:33Þ ¼ 0:14. Therefore, α ¼ 0:33 ¼ 0:7. 0:33 þ 0:14 There is little information on materials elasticity parameter σ. Rotemberg and Rotemberg (1999) survey the business cycle literature and find a range of elasticities from 0.1 to 0.7, which implies a value of σ between −0.4 and −9. I use the value of −1 suggested by Jones (2008), which implies an elasticity midway between Cobb– Douglas and Leontief. Below, I examine the robustness of the results to variations in this parameter. The consumption good productivity parameter Ac is normalized to 1. The materials productivity parameter Am is set to 1.075 to match the non-manufacturing share of goods GDP in 1967 (14.0%). The Armington parameter ρ is set to match the long run trade elasticity of 6.4 estimated in Ruhl (2005). While the model does not include the fixed costs featured in Ruhl (2005), his estimate is designed to capture the elasticity with respect to permanent changes in trade costs that this paper examines. I discuss the robustness of the results to changes in ρ below. The comparative advantage in parts parameter θ and home bias parameter ϕ are selected by grid search to match the level of VS trade in 1972 (6% of exports) and share of manufacturing output that is exported in 1967 (9%) respectively given the other parameters. Model VS trade is measured as the sum of the three sources of VS trade: Materials imports that are exported in parts (ð1− ¯z 2 ÞP1m;2 M12 ), imported parts in exported final goods (ð1− ¯z 2 ÞP21 C21 ) and imported materials in ! m;2 2 P M domestic parts used in exported final goods P11 C21 1 1 1 1 ¯z 2 . Note Px ð0Þx ð0Þ that this definition does not include goods that are exported and reimported. While this is an important source of VS trade (see Johnson and Noguera (2008)), it is omitted from the data sources I use. The value of the comparative advantage parameter θ is set to 0.24. This parameter is not far from that used in the heterogeneous trade literature. Eaton and Kortum (2002) suggest a range of 0.08 to 0.28 as reasonable for this parameter for traded goods, close to the range of 0.1 to 0.25 in Alvarez and Lucas (2007). Waugh (2010) uses a value of 0.18 for all traded goods for all countries. These models do not map exactly (they only have one layer of traded production,

137

Table 2 Baseline parameters. Variable Value

ρ 0.85

θ 0.24

α 0.7

αc 0.3

σ −1

Am 1.075

Ac 1

ϕ 0.54

among other differences), but it suggests that the calibration is not strongly different from the literature. I discuss the robustness of the model to changes in θ below. Tariffs and freight rates are taken from Table 1. I use nonmanufactured intermediate goods for raw materials, manufactured intermediate goods for parts and manufactured final goods for final production. Since these are trade weighted measures, they suffer from some well-known limitations. High trade cost goods are likely to be traded less than low trade cost goods. A particular issue with this measure in this context is that there has been significant trade growth along the extensive margin: trade in new goods (Kehoe and Ruhl, 2003). Therefore, there are a significant number of goods whose trade costs are not measured in the early years. Bridgman (2010) shows that for freight, lower trade costs induce lower value goods to be traded which mask changes in trade costs. A measure of the size of trade weighting bias is the Mercantilist Trade Resistance Index (MTRI) proposed by Anderson and Neary (2003), which is the estimated uniform tariff equivalent that generates the observed level of trade. I scale up trade costs by 1.69, the ratio of MTRI that Kee et al. (2005) estimates to trade-weighted tariffs for the United States in 2002. 8 These estimates only cover tariffs. I am not aware of any MTRI estimates for transport costs. Anderson and van Wincoop (2004) note that transport costs are similar to tariffs in magnitude and variability, so a tariff based estimate is likely to be a reasonable proxy for bias in transport cost measures. The baseline parameters are summarized in Table 2.

5.2. Simulations This section presents the results of the calibrated model. In interpreting the results, I identify the raw materials sector as nonmanufacturing output and the manufactured parts and final goods sectors as manufacturing output. The model is able to match a number of trade growth facts. It generates both the empirical growth in trade and the change in composition. As can be seen from Fig. 3, the model does a good job of matching overall empirical trade growth. The share of goods production that is exported in the model grows 174% from 1967 to 2002, not far from the actual growth in export share of 135%. There are only predictions for a few years, so the model has less to say about the year to year time series. However, those few predictions are consistent with the data. The model generates a doubling of the trade share with a relatively modest fall in trade costs due to the rapid expansion of manufacturing trade. The share of manufacturing output that is exported in the model grows much faster than total trade, growing by 350% between 1967 and 2002. This growth is somewhat more than the 317% empirical growth in the share of manufacturing output. This growth is mostly due to increasing trade in manufactured parts. Of the three types of goods, manufactured parts grow the fastest. In 1967, there is no trade in parts. By the 1990s, this category is over half of manufacturing trade. This prediction is consistent with the finding that parts and component trade has grown more rapidly than manufacturing trade (Yeats, 2001). 8 Irwin (2007), using the closely related Trade Resistance Index, estimates that the ratio in 1960 was 1.74 which suggests the bias hasn't changed too much over the sample period.

138

B. Bridgman / Journal of International Economics 86 (2012) 133–140

Fig. 3. U.S. exports/value added, model and data 1967–2006.

Fig. 4. U.S. materials exports/value added, model and data 1967–2006.

VS trade also grows rapidly. Table 3 compares the model's predictions with the calculations from Chen et al. (2005). In the model, VS trade increases from 6% of exports in 1972 to 17% in 1997. The model is broadly consistent with the estimates for the United States in Chen et al. (2005), though it overpredicts the increase in VS trade. While VS trade grows rapidly, intermediate goods trade does not increase significantly. This prediction matches the data: Intermediate goods share of trade is roughly constant. The model predicts that 52.3% of exports are intermediates (materials and parts) in 1967 which is close to its prediction of 58.5% in 2002. Therefore, the rise of VS trade in the model is not driven by a relative increase in intermediates trade. The level is similar to estimates in Chen et al. (2005). They estimate intermediates were about half of trade (50.4% in 1972 and 51.9 in 1997), not too far from the model's predictions (45% in 1972 and 54% in 1997). As can be seen in Fig. 4, the model predicts very little growth in materials trade. This prediction matches the data. There is little permanent growth in materials trade, though commodity price swings lead to temporary spikes. The share of U.S. materials production that is exported increases by 10.6% from 1967 to 2002, very close to the model's prediction of 12.1%. One might be concerned that shifts in industrial structure drive the results. Table 3 reports the materials share of U.S. goods GDP. While there has been a shift away from goods production to services, within goods the share devoted to materials production has been essentially constant. The model also predicts no trend in materials share of goods GDP. Raw materials production tends to depend on local geographical conditions in a way that manufacturing does not. Mines can only be sited where ore exists naturally. A steel plant can be placed anywhere. Therefore, raw materials will be traded even when trade costs are high. Combined with the fact that trade costs for raw materials fell less, most of the new trade in goods is due to manufactured parts. This feature of the model is consistent with empirical finding that goods lower down the supply chain have lower price–trade elasticities (Balassa and Kreinin, 1967). It is not the case that geography does not matter for manufacturing, but it is less tied to geographic endowments relative to raw materials. Even industries that use inputs that are closely tied to

natural endowments are often placed far from the sources of those inputs. For example, the center of cane sugar refining in the United States was New York City. New Orleans, a major port close both to domestic and imported raw sugar sources, was a minor producer (Glaeser, 2005). The results may explain why trade among industrial countries has increased, despite having similar industrial structures. When trade was dominated by goods that depend heavily on endowments, less developed countries — economies dominated by raw materials production — made up more of world trade. This explanation does not rely on increasing returns or agglomeration economies, as in Krugman (1980), to explain the concentration of trade among similar countries. In fact, it is precisely because productivity differences in parts production between industrialized countries are small that relatively small declines in trade costs have such a large impact on trade growth. Since the productivity differences in tradeable goods between rich and poor countries are large (Herrendorf and Valentinyi, 2007), even high trade barriers (such as those used by import substitution programs) are not sufficient to prevent poor countries from specializing in materials production.

Table 3 Model moments. Variable VS trade (model) VS trade (data) Interm. trade share (model) Interm. trade share (data) Mat. share of goods GDP (model) Mat. share of goods GDP (data)

1967

1972

1992

1997

2002

5.1

5.8 5.9 45.1 50.4 14.1 15.6

16.2

17.0 12.3 54.0 51.9 14.1 13.5

19.7

52.3 14.0 14.0

53.8 14.1 14.9

58.5 14.1 15.2

5.2.1. Robustness The Armington parameter ρ and the materials elasticity σ were assigned rather than calibrated directly. I examine the robustness of the model to changes in these parameters below. I also examine the impact of changes in the comparative advantage parameter θ. There is controversy over the proper value of the Armington parameter ρ. Simonovska and Waugh (2011) show that one of the prominent estimates of this parameter is subject to bias in small samples that tends to overestimate it. (The estimate I use, from Ruhl (2005), is not subject to this bias.) Simonovska and Waugh (2011) obtain estimates close to 4, lower than the 6.4 that is used in the baseline estimate. I check the robustness of the model by setting ρ ¼ 1− 14 ¼ 0:75 and redo the calibration. The only parameter that changes is that the home bias parameter ϕ increases to 0.6. The qualitative predictions of the model are unchanged. Manufacturing trade still grows faster than overall goods trade and the other moments of the model are almost unchanged. The amount of trade expansion falls somewhat. The predicted increase in manufacturing output exported falls from 350% to 297%. Predicted goods export share increases 146% compared to 174% in the baseline model. The model still predicts trade expansion very close to what is observed in the data. The empirical range for the materials elasticity parameter σ is quite wide. I experimented with alternative values for this parameter and it has a negligible effect on the results. Even large changes in σ have a small effect on materials trade. Since the growth of materials trade is a very small part of the increase in overall trade, changes in the materials sector have a tiny impact on the predictions of the model.

B. Bridgman / Journal of International Economics 86 (2012) 133–140

139

of the rise in VS and manufacturing trade even if other sources were considered.

Table 4 Counterfactuals. Variable

1967 tariffs

1967 freight

1967 parts tariffs

Total trade growth 1967–2002 Mfg. trade growth 1967–2002 ¯z 1

20.6% 35.2% 1

78.0% 156.9% 0.89

73.4% 137.4% 1

The estimate of the comparative advantage parameter θ was near the top of the range found in the literature. Low values of θ reduce predicted trade growth. There is little difference in parts productivity across the two countries. Therefore, even modest levels of trade costs shut down manufactured parts trade, closing off a major source of trade growth. 5.2.2. Offshoring: tariffs or freight? The model allows us to decompose the importance of the various trade costs for the increase in offshoring, the shift of production from domestic to foreign sources. Falling tariffs are widely cited as the primary reason for increasing trade and offshoring. Others, such as Levinson (2006), have suggested that improvements in shipping technology such as containerization are a first order source of increasing trade. 9 The amount of offshoring is measured by the cut-off ¯z . The measure of offshored industries in the symmetric equilibrium is given by 1−¯z 1 ¼ ¯z 2 . The baseline model predicts that all possible domestic industries operate in 1967. As trade expands, the set of industries that a country operates contracts. By 2002, 26% of domestic parts manufacturing industries have closed. (In terms of the model, ¯z 1 ð2002Þ ¼ 0:74.) To examine the relative importance of these two forces, I run counterfactual simulations holding trade costs at their 1967 levels. The first counterfactual simulation (1967 Tariffs) reported in Table 4 holds all tariffs at their 1967 levels. Freight costs fall as they do in the baseline simulation. The next simulation (1967 Freight) does the opposite: it holds freight rates at their 1967 levels while tariffs fall as they do in the baseline. Trade growth is much stronger, indicating a stronger role for tariffs. Falling freight costs alone generate very little trade growth. They cannot induce parts trade while falling tariffs do. Table 4 shows that the 1967 Tariffs counterfactual does not cause any of the manufactured parts to be traded, while there is parts trade in the 1967 Freight counterfactual. In terms of model quantities, ¯z 1 does not fall from one in the 1967 Tariffs counterfactual while it falls to 0.89 in the 1967 Freight counterfactual. In fact, simply maintaining tariffs on manufactured parts at their 1967 levels (the counterfactual named “1967 Parts Tariffs” in Table 4) is sufficient to prevent trade in parts through 2002 (¯z 1 1). While manufactured goods trade still grows significantly due to growing finished goods trade, there is no trade in parts. 6. Conclusion This paper shows that trade costs can explain the change in the composition of international trade. However, it does not consider alternative causes of VS trade growth. Improvements in technology, both production (allowing better standardization) and communication (allowing better coordination across locations), may have had a role. Financial liberalization has encouraged foreign direct investment, allowing firms to offshore while keeping production within the firm. Trade among affiliated firms within multinationals has been an important source of trade growth. However, the strength of the results suggests that trade costs would remain a significant source 9 Technological change may improve transportation in ways that are not reflected in price, such as increasing reliability (Hummels, 2007). The importance of timeliness is emphasized by Harrigan and Venables (2006).

Appendix A. Data A.1. Fig. 1 A.1.1. 1925–1955 Data are drawn from Vanek (1963). Natural resource share of U.S. imports (Table 5.11) divided by intermediate goods share of imports: 1 minus final manufactured goods (Table 5.8) and manufactured food (Table 5.6) import share. A.1.2. 1967 Import data are from U.S. Department of Commerce (1977), Table 1b. Benchmark I–O table is the 85-industry total requirements table from the BEA website. Imports are estimated by multiplying import share of gross output by total requirements table. Materials share is imported shipments from mining and agriculture industries (I–O codes 1–10) to goods producing industries (mining, agriculture and manufacturing: I–O codes 1–10 and 13–64) over all imported shipments from goods producing industries to goods producing industries. A.1.3. 1972–1990 Data are drawn from 1995 edition of the OECD input–output tables (www.oecd.org/sti/inputoutput/), current dollar imported transactions table (USMIOCXX). Materials share is shipments from mining and agriculture industries (industry codes 1–2) to goods producing industries (mining, agriculture and manufacturing industries: industry codes 1–24) over all imported shipments from goods producing industries to goods producing industries. A.1.4. 1995–2005 Data are drawn from 2006 edition of the OECD input–output tables (www.oecd.org/sti/inputoutput/), imported transactions table. Materials share is shipments from mining and agriculture industries (industry codes 1–3) to goods producing industries (mining, agriculture and manufacturing industries: industry codes 4–25) over all imported shipments from goods producing industries to goods producing industries. The non-energy series removes shipments originating from mining and quarrying (energy) (Industry 2) from both the numerator and denominator. A.2. Fig. 2 A.2.1. 1972–1990 Data are drawn from 1995 edition of the OECD input–output tables, constant price imported transactions table (UKMIOKXX, JPMIOKXX,FRMIOKXX). Materials share is shipments from mining and agriculture industries (industry codes 1–2) to goods producing industries (mining, agriculture and manufacturing industries: industry codes 1–24) over all imported shipments from goods producing industries to goods producing industries. A.3. Figs. 3 and 4 A.3.1. Export share of value added Goods value added and exports from NIPA Tables 1.2.5 and 4.1. Manufacturing value added from BEA's value added by industry (www.bea.gov/industry/io_histannual.htm). Manufacturing exports from UNCTAD (UNCTAD.org). Materials value added and exports: goods-manufacturing series.

140

B. Bridgman / Journal of International Economics 86 (2012) 133–140

A.4. Table 1 A.4.1. IO tables Benchmark input–output tables are drawn from the BEA Industry Economic Accounts website. The 1967 and 1972 tables are the 85industry total requirements tables. The 1992, 1997 and 2002 are the use tables at the detailed level after redefinition. A.4.2. Import margins: 1967 and 1972 The imports and trade costs are reported in U.S. Department of Commerce (1977), Table 1b for 1967 and Ritz et al. (1979), Table D for 1972. A.4.3. Import margins: 1992, 1997 and 2002 Import, duties and freight data come from Feenstra (1994) and U.S. International Trade Commission (dataweb.usitc.gov). This data is concorded into the IO classification. The 1992 concordance is an unpublished concordance provided by BEA's Industry Economic Accounts. The 1997 and 2002 concordances are taken from the BEA website. A.4.4. Calculation Commodities originating from service industries and government are excluded: two digit IO Industries 65–79 (1967/72/92) and one digit industries 4–9 and two digit industry 22 (Utilities) (1997/2002). Manufacturing industries are two digit IO industries 13–64 (1967/72/92) and one digit industry 3 (1997/2002). A.5. Table 3 A.5.1. VS and intermediates trade Calculations from Chen et al. (2005), Tables 1 and 2. A.5.2. Materials share BEA's historical GDP by industry: agriculture and mining value added share of GDP divided agriculture, mining and manufacturing value added share of GDP. (www.bea.gov/industry/gdpbyind_data. htm). References Alessandria, George, Choi, Horag, 2010. Do falling iceberg costs account for recent U.S. export growth? Working Paper 10–10, Federal Reserve Bank of Philadelphia. Alvarez, Fernando, Lucas Jr., Robert E., 2007. General equilibrium analysis of the Eaton– Kortum model of international trade. Journal of Monetary Economics 54 (6), 1726–1768. Amiti, Mary, Konings, Jozef, 2007. Trade liberalization, intermediate inputs, and productivity: evidence from Indonesia. American Economic Review 97 (5), 1611–1638. Anderson, James E., 1972. Effective protection in the U.S.: a historical comparison. Journal of International Economics 2 (1), 57–76. Anderson, James E., Neary, Peter, 2003. The mercantalist index of trade policy. International Economic Review 44 (2), 627–649. Anderson, James E., van Wincoop, Eric, 2004. Trade costs. Journal of Economic Literature 42 (3), 691–751. Armington, Paul S., 1969. A theory of demand for products distinguished by place of production. International Monetary Fund Staff Papers 16 (1), 159–178. Baier, Scott L., Bergstrand, Jeffrey H., 2001. The growth of world trade: tariffs, transport costs, and income similarity. Journal of International Economics 53 (1), 1–27. Balassa, Bela, Kreinin, Mordechai, 1967. Trade liberalization under the “Kennedy Round”: the static effects. Review of Economics and Statistics 49 (2), 125–137. Bergoeing, Raphael, Kehoe, Timothy J., 2003. Trade Theory and Trade Facts, Staff Report 284, Federal Reserve Bank of Minneapolis. Bergoeing, Raphael, Kehoe, Timothy J., Strauss-Kahn, Vanessa, Yi, Kei-Mu, 2004. Why is manufacturing trade rising even as manufacturing output is falling? American Economic Review Papers and Proceedings 94 (3), 134–138. Bridgman, Benjamin, 2008. Energy prices and the expansion of world trade. Review of Economic Dynamics 11 (4), 904–916. Bridgman, Benjamin, 2010. Market Entry and Trade Weighted Import Costs, Working Paper 2010-05, Bureau of Economic Analysis. Chen, Hogan, Kondratowicz, Matthew, Yi, Kei-Mu, 2005. Vertical specialization and three facts about U.S. international trade. North American Journal of Economics and Finance 16 (1), 35–59. Dalton, John T. 2009, Explaining the growth in manufacturing trade, mimeo, University of Minnesota. Dixit, Avinash, Grossman, Gene, 1982. Trade and protection with multistage production. Review of Economic Studies 49 (4), 583–594.

Dornbusch, Rudiger, Fischer, Stanley, Samuelson, Paul, 1977. Comparative advantage, trade, and payments in a Ricardian model with a continuum of goods. American Economic Review 67 (5), 823–839. Eaton, Jonathan, Kortum, Samuel, 2002. Technology, geography, and trade. Econometrica 70 (5), 1741–1779. Estevadeordal, Antoni, Taylor, Alan M., 2008. Is the Washington consensus dead? Growth, Openness, and the Great Liberalization, 1970s–2000s, Working Paper 14264, NBER. Feenstra, Robert, 1994. New product varieties and the measurement of international prices. American Economic Review 81 (1), 157–177. Feenstra, Robert, 1998. Integration of trade and disintegration of production in the global economy. Journal of Economic Perspectives 12 (4), 31–50. Glaeser, Edward, 2005. Urban colossus: why New York is America's largest city. FRBNY Economic Policy Review 11 (2), 7–24. Goldberg, Pinelopi K., Khandelwal, Amit, Pavcnik, Nina, Topalova, Petia, 2008. Imported intermediate inputs and domestic product growth: evidence from India, Working Paper 14416, NBER. Gollin, Douglas, 2002. Getting income shares right. Journal of Political Economy 110 (2), 458–474. Grossman, Gene, Rossi-Hansberg, Esteban, 2008a. Task Trade between Similar Countries, Working Paper 14554, NBER. Grossman, Gene, Esteban, Rossi-Hansberg, 2008b. Trading tasks: a simple theory of offshoring. American Economic Review 98 (5), 1978–1997. Harrigan, James, Venables, Anthony, 2006. Timeliness, trade and agglomeration. Journal of Urban Economics 59 (2), 300–316. Herrendorf, Berthold Valentinyi, Akos, 2007, Which sectors make the poor countries so unproductive? Mimeo, Arizona State University. Hummels, David, 2007. Transportation costs and international trade in the second era of globalization. Journal of Economic Perspectives 21 (3), 131–154. Hummels, David, Rapoport, Dana, Yi, Kei-Mu, 1998. Vertical specialization and the changing nature of world trade. FRBNY Economic Policy Review 4 (2), 79–99. Hummels, David, Ishii, Jun, Yi, Kei-Mu, 2001. The nature and growth of vertical specialization in world trade. Journal of International Economics 54 (1), 75–96. Irwin, Douglas A., 2007. Trade Restrictiveness and Deadweight Losses from U.S. Tariffs, 1859–1961, Working Paper 13450, NBER. Johnson, Robert C. Noguera, Guillermo, 2008, Accounting for intermediates: production sharing and trade in value added, mimeo, U.C. Berkeley. Jones, Charles I., 2008. Intermediate Goods and Weak Links: A Theory of Economic Development, Working Paper, U.C. Berkeley. Kee, Hiau Looi, Nicita, Alessandro Olarreaga, Marcelo, 2005, Estimating trade restrictiveness indices, mimeo, World Bank. Kehoe, Timothy J., Ruhl, Kim J., 2003. How important is the new goods margin in international trade? Federal Reserve Bank of Minneapolis Staff Report, 324. Kose, M. Ayhan, Yi, Kei-Mu, 2001. International trade and business cycles: is vertical specialization the missing link? American Economic Review Papers and Proceedings 91 (3), 371–375. Krugman, Paul, 1980. Scale economies, product differentiation, and the pattern of trade. American Economic Review 70 (5), 950–959. Krugman, Paul, 1995. Growing world trade: causes and consequences. Brookings Papers on Economic Activity 1995 (1), 327–377. Levinson, Marc, 2006. The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger. Princeton University Press, Princeton. Ramanaryanan, Anath 2006, International trade dynamics with intermediate inputs, mimeo, Federal Reserve Bank of Dallas. Reimer, Jeffrey J., 2006. Global production sharing and trade in the services of factors. Journal of International Economics 68 (2), 384–408. Ritz, Philip, Roberts, Eugene, Young, Paula, 1979. Dollar-value tables for the 1972 input–output study. Survey of Current Business 59 (4), 51–72. Rose, Andrew, 1991. Why has trade grown faster than income? Canadian Journal of Economics 24 (2), 417–427. Rotemberg, Julio J., Woodford, Michael, 1999. The cyclical behavior of prices and costs. In: Taylor, J.,Woodford, M. (Eds.), Handbook of Macroeconomics, 1. Elsevier, Amsterdam, pp. 1051–1135. Ruhl, Kim J. 2005, Solving the elasticity puzzle in international economics, mimeo, New York University. Sanyal, Kalyan, 1983. Vertical specialization in a Ricardian model with a continuum of stages of production. Economica 50 (197), 71–78. Simonovska, Ina, Waugh, Michael E., 2011. The Elasticity of Trade: Estimates and Evidence, Working Paper 16796, NBER. Trefler, Daniel, Zhu, Susan, 2000. Beyond the algebra of explanation: HOV for the technology age. American Econmic Review Papers and Proceedings 90 (2), 145–149. U.S. Department of Commerce, 1977. Revised Input–Output table for the United States: 1967, Staff Paper 29, Bureau of Economic Analysis. Vanek, Jaroslav, 1963. The Natural Resource Content of United States Foreign Trade, 1870–1955. MIT Press, Cambridge. Waugh, Michael E., 2010. International trade and income differences. American Economic Review 100 (5), 2093–2124. Yeats, Alexander J., 1977. Do international transport costs increase with fabrication? Some empirical evidence. Oxford Economic Papers 29 (3), 458–471. Yeats, Alexander J., 2001. Just how big is global production sharing? In: Kierzkowski, H., Arndt, S. (Eds.), Fragmentation: New Production Patterns in the World Economy. Oxford Press, Oxford, pp. 108–143. Yi, Kei-Mu, 2003. Can vertical specialization explain the growth of world trade? Journal of Political Economy 111 (1), 52–102. Yi, Kei-Mu, 2010. Can multi-stage production explain the home bias in trade? American Economic Review 100 (1), 364–393.