A. Preferences

Functional Forms We assume that multiple groups of goods identified by country (within each sector) are strongly separable. The functional form used here to describe such preferences is the two-level constant elasticity of substitution (CES) functional form introduced by Sato (1967).⁶ The empirically useful property of this functional form is that, together with the assumption of two-stage budgeting, where the consumer can allocate total expenditure in two stages, the utility maximization problem can be effectively separated into two stages. More specifically, the utility-maximizing problem of a representative agent in country i for a given level of total demand for goods belonging to a specific industry Y_i is as follows:

Note that σ_i, σ_si ∈ {(0, 1) and (1, ∞)}, and δ_i, ϕ_ij, D_i, M_i, and M_ij all take positive values. U_i is the industry-level utility of the representative consumer in country i. D_i is the industry-specific demand for domestic goods. M_i is the industry-specific aggregate volume of imports. M_ij is the industry-specific volume of imports from a foreign country j. σ_i is country i’s elasticity of substitution between domestic goods and aggregate imports (the intergroup elasticity), and σ_si is country i’s elasticity of substitution among imports from different countries (referred to as the intragroup elasticity).⁷ δ_i and φ_ij are the industry-specific distribution parameters.⁸ $p_i^D$ is the free-on-board (f.o.b.) price of domestic goods in country i, $p_{ij}^M$ is the price of imports from country j inclusive of the cost of insurance and freight (c.i.f.) and customs duties, and, finally, $p_i^M$ is the price of aggregate imports in country i.

The functional form used in Armington (1969),

or the one more commonly used in many studies using multilateral trade data M_i,

can be expressed as a special case of the two-level CES functional form described in equation (1). For example, under the assumption that multiple goods are only weakly separable (σ_i = σ_si), equations (3) and (4) can be derived from equation (1).⁹ In other words, all three specifications (1), (3), and (4) are equivalent, and thus the distinction between multilateral and bilateral is not necessary, if weak separability is a valid assumption.

Optimality Conditions The following optimality conditions, obtained from the utility maximization problem described above, are the bases for our regression equations:¹⁰

Equation (5) implies that, for each 1 percent increase in the relative price of the industry-specific aggregate imports with respect to the price of industry-specific domestic goods, there is a σ_i percent fall in the ratio of the industry-specific aggregate volume of imports to country i’s industry-specific gross output. A similar interpretation applies to equation (6).

B. Production Technology

Let us turn to the case where the products traded are intermediate inputs. We assume analogously that the technology that the representative producer faces in its cost minimization problem has the following property: inputs from different countries are strongly separable within each sector (see Appendix I for the cost minimization problem). Under such a specification, the first-order conditions of the cost minimization problem provide the same optimality conditions as equations (5) and (6), where D_i now represents domestic inputs and M_i and M_ij represent imported inputs.

In the empirical part of this paper, elasticities are estimated from the same regression equations based on equations (5) and (6). The elasticities σ_i and σ_si estimated for the intermediate inputs sectors are, however, the elasticity of technical substitution in production rather than the elasticity of substitution in consumption.¹¹

C. Special Features in Intermediate Inputs Trade

The international trade literature refers to the growth of intermediate inputs trade in many different ways: for example, as an increase in vertical specialization of production (Hummels, Ishii, and Yi, 2001; Yi, 2003), as an increase in forward and backward linkages between firms across countries (Krugman and Venables, 1995), as an increase in the fragmentation of production processes across borders (Jones, 2000; Arndt and Kierzkowski, 2001), as an increase in outsourcing (Feenstra, 1998; Feenstra and Hanson, 2001), as the thinner slicing of the value chain (Krugman, 1995), and so on. These papers, despite the differences in terminologies used, have made and analyzed the same observation that the volume as well as the variety of intermediate inputs traded across countries has increased over time.¹²

What is the implication of these new varieties of intermediate inputs on the estimation of the elasticity of technical substitution? Feenstra (1994) shows that new varieties of imports can be thought of as having prices that fall from infinity (where the cross-border demand is zero) to the actual level (where the cross-border demand becomes positive). In other words, ignoring the entry of new varieties can undermine the actual fall in prices.

By ignoring new varieties in the estimation of Armington elasticities, therefore, we may be asking a smaller-than-actual fall in relative prices (for example, a smaller-than-actual fall in $\frac{p_i^M}{p_i^D}$ or $\frac{p_{ij}^M}{p_{ik}^M}$) to explain the observed increase in the relative demand for inputs (for example, an increase in $\frac{M_i}{D_i}$ or $\frac{M_{ij}}{M_{ik}}$), and thus the elasticity estimates may be biased upward. Assuming that the rate at which new varieties are entering the international markets is similar across countries, such a bias is more likely in the intergroup elasticity estimates (obtained from multilateral trade data) than in the intragroup elasticity estimates (obtained from bilateral trade data).

For example, consider a simple scenario where the United States and France used to export only large engines for aircraft and Germany only medium-size engines for automobiles, but now all three countries export both types of engines.¹³ In this scenario, changes in the price of the basket of German engines relative to that of the basket of French engines (changes in $\frac{p_{ij}^M}{p_{ik}^M}$) would be unaffected by the variety change as long as both the German and the French baskets have added the same number of new varieties each. On the other hand, changes in the price of the basket of foreign inputs relative to that of domestic inputs (changes in $\frac{p_i^M}{p_i^D}$) would be affected by the variety change, since the basket of foreign inputs has relatively more new varieties (for example, two new engines in the foreign basket versus one new engine in the domestic basket). Thus an upward bias in the elasticity estimates is more likely if multilateral trade data are used instead of bilateral trade data.

The variety explanation may not be a sufficient explanation, since the use of the variety-adjusted price index in Feenstra (1994) does not eliminate the intergroup versus intragroup differences.¹⁴ Nevertheless, an increase in the variety in the basket of foreign goods relative to that of domestic goods is a likely explanation for the evidence found in the intermediate inputs sector.¹⁵

A. Decomposition of Prices

The key data in both of our regression equations are price data, in particular, import price data. Import price data (typically constructed by dividing the value of imports by the volume of imports) are not widely available and can be very sensitive to the quality of the volume data.

The use of import price data, therefore, tends to limit the variation in the sample–either the cross-sectoral variation, the cross-country variation, or both. For example, Feenstra (1994) covers a wide cross-country variation by using U.S. bilateral trade data, but the number of products he considers is limited. All other studies using U.S. multilateral trade data, such as Blonigen and Wilson (1999), Gallaway, McDaniel, and Rivera (2003), and Shiells, Stern, and Deardorff (1986), have wider cross-sectoral coverage, but small cross-country variations. One exception is the recent contribution by Erkel-Rousse and Mirza (2002). They use unit value indices that they have constructed from bilateral trade data from the French statistical institute INSEE. Their data have both large cross-sectional and cross-country variations, but access to these data is limited.

Many recent studies (such as Baier and Bergstrand, 2001; Head and Ries, 2001; Hummels, 1999) have turned away from price data to tariff and transport cost data to estimate Armington elasticities. These studies, however, ignore the effect of changes in productivity on prices.

Assumptions To cover a wide range of bilateral trade relations and to allow prices to reflect the cost of production in each country, we make two assumptions about the domestic price, $p_i^D$, import prices, $p_{ij}^M$, and the price of the import aggregate, $p_i^M$. First, we assume that industries are perfectly competitive. This assumption is consistent with the specification that Head and Ries (2001) use for the Armington model, which is a constant-returns-to-scale model with varieties differentiated by nationality, in contrast to an increasing-returns model where varieties are linked to firms. Second, we make the standard Samuelson iceberg assumption for transport costs (Hummels, 1999; Baier and Bergstrand, 2001; and many others). The idea is that, for each unit of goods shipped from another region, only a fraction arrives, and this melting effect is captured in import prices.

Domestic Price The assumption of perfect competition allows us to let unit total costs equal prices. Unfortunately, comparable total cost data across countries and across sectors are not available. At best, the producer price indices are available, but these indices are not comparable at levels. As a second-best solution, we let domestic prices, $p_i^D$, be a log-linear function of the unit labor cost, c_i:¹⁶

The markup, γ_1i, is the price elasticity with respect to unit labor costs in country i. Under perfect competition this markup should equal the labor cost share.¹⁷

Import Price With Samuelson’s iceberg assumption, the import price can be described as $p_{ij}^M=(1+ta{r}_{ij})(1+\mathit{tra}{n}_{ij})p_j^D$, where $p_j^D$ is the f.o.b. price of a good produced in country j, tar_ij is the tariff rate imposed on imports from country j, and trans_ij is the transport cost (cost of insurance and freight) for shipping goods from country j. For simplicity, we let τ_ij = (1 + tar_ij) (1 + tran_ij), and therefore the import price is expressed as follows:

where c_j is the industry-specific unit labor cost in country j.¹⁸

Price of the Import Aggregate Finally, the price of the import aggregate, $p_i^M$ for all i, is assumed to take the Laspeyres price index form

where the weight (ω_j) is the share of imports from country j, ${\omega }_j=\frac{M_{ij}{p}_{ij}^M}{{\displaystyle \sum _{j\ne i}{M}_{ij}{p}_{ij}^M}}$.¹⁹

B. Regression Equations

Two regression equations are derived from the optimality conditions in equations (5) and (6). Taking the natural logarithm of both sides of equations (5) and (6) and adding time subscripts in appropriate places yields the following equations:

By substituting prices, as specified in equations (7), (8), and (9), the relative prices in the right-hand side of equations (10) and (11) take the form

By substituting equations (12) and (13) into equations (10) and (11), two regression equations are derived:²⁰

where u_it and υ_ijkt are disturbance terms. The reduced forms of these equations are

Notice that the second term (home bias) and the third term (transport costs and customs duties) on the right-hand side of equations (14) and (15) are jointly captured by the time-invariant unobserved fixed effects β_12i and β_22ijk in equations (16) and (17), respectively. β_12i is a fixed effect specific to each importing country i, and β_22ijk is a fixed effect specific to each combination of trading partners (j and k) for each importing country i. Notice also that the slope parameters β_11i and β_21i in these equations are not a priori assumed to be the same across countries. That is, the inter- and intragroup elasticities (σ_i and σ_si) are not a priori assumed to be the same across countries.

There are two points to note on the empirical specifications described above. First, the time invariance of transport costs and tariff rates is a strong assumption.²¹ To relax this assumption, time dummies are included in regression equation (16), which assumes that the reduction in transport costs and tariff rates is common across the industrial countries. If this assumption is valid, then the time-specific reductions in τ_ij and τ_ik in equation (15) should cancel out. Time dummies are therefore not included in regression equation (17).

Second, the markup (γ_1i) is set to equal the labor cost share, which results from the perfect competition assumption. Identifying the size of the markup (γ_1i) is critical in obtaining the structural parameters (σ_i and σ_si) from the reduced-form parameters (β_11i and β_21i). It is, however, possible that the markup (γ_1i) is higher than what we propose (possibly because of the presence of market power); in that case the true elasticities would be lower than the estimated ones.²² Such a bias, however, would be present in both the intergroup and the intragroup elasticities and hence is not likely to invalidate the main findings of this paper.

C. Choice of Estimators

Random effects, fixed effects, and first differencing are still the most popular approaches to estimating unobservable effects in panel data models under strict exogeneity of the explanatory variables.²³ Even in the case of endogenous explanatory variables (as in the case of our model), it is common to use a transformation to eliminate the unobserved effect and then to select appropriate instruments in order to apply the instrumental variable (IV) estimator. For example, Erkel-Rousse and Mirza (2002) apply a family of IV estimators after removing the mean in the time dimension.

The estimates obtained from the IV estimator, however, can be sensitive to the choice of instrumental variables, as correctly pointed out by Erkel-Rousse and Mirza (2002). Moreover, and more important, removing the mean to eliminate the unobserved effect does not rule out serial correlation in the disturbance terms (in our regression equations, u_it and υ_ijkt). It is well known that conventional panel techniques based on the generalized method of moments (GMM) break down and produce very large biases when the data exhibit unit root behavior.²⁴ Since our data contain unit roots, an alternative method is needed.

One alternative method is to use the GMM estimator with a proper treatment of time series (as in Feenstra, 1994). However, applying the family of GMM estimators in this paper is problematic in two dimensions. First, a rich cross-sectional variation, which is necessary to apply the GMM estimator, is available in estimating equation (17) but not in estimating equation (16). Second, the GMM estimators implicitly assume that the short-run dynamics are homogeneous across individual members of the panel. Although the short-run dynamics are not explicitly discussed in this paper, the assumption of homogeneity of the serial correlation dynamics is likely to be violated in aggregate trade data of this type.

An alternative solution to take care of unobservables, endogeneity, and serial correlation (with possible heterogeneity in the short-run dynamics) is to use the panel version of the Phillips and Hansen (1990) procedure, the panel fully modified ordinary least squares (FMOLS) estimator (Pedroni, 2000). This method fully modifies the ordinary least squares estimates (and hence eliminates the potential problem caused by endogeneity as well as serial correlation) by transforming the disturbance term (u_it and υ_ijkt) and subtracting off a parameter that can be constructed from the estimated nuisance parameters and a term from the original data; see Pedroni (2000) for more details. To apply this method, both the left-hand side and the right-hand side variables in equations (16) and (17) must be nonstationary, and there must be a cointegrating relationship between the two: that is, the disturbance terms (u_it and υ_ijkt) must be stationary. The group mean unit root tests of Im, Pesaran, and Shin (1997) and the group mean cointegration tests of Pedroni (1999) confirm that our data satisfy these time series properties.²⁵ We therefore use the panel FMOLS estimator.

An additional important benefit of using the panel FMOLS estimator is that the inter- and the intragroup elasticities do not have to be a priori assumed to be the same across countries. The null hypothesis such as H₀ : σ = σ_s without country subscript i could be more restrictive since it makes the a priori assumption that the elasticities are the same across countries. On the other hand, the null hypothesis using the group-mean panel FMOLS estimator is based on elasticities estimated for each i (or each ijk) and averaged together for the group mean:

where $\overline{\sigma }=\frac{1}{N}{\displaystyle \sum _{i=1}^N{\sigma }_i}$ (N is the number of i) and ${\overline{\sigma }}_s=\frac{1}{N_s}{\displaystyle \sum _{i=1}^{N_s}{\sigma }_{si}}$, (N_s is the number of ijk). Our test results therefore do not rely on an a priori assumption under either the null or the alternative hypothesis.

The panel FMOLS approach does not estimate the intergroup and intragroup elasticities jointly as in the case of the maximum likelihood estimator used in Brown and Heien (1972).²⁶ Rather, we estimate the elasticities separately for two reasons. First, the panel FMOLS estimator, although it does not minimize the residuals jointly, is a useful consistent estimator for the reasons discussed above. Second, assuming the independence of disturbances of a demand system expressed in terms of ratios, as in equations (16) and (17), is not as implausible as making that assumption for a demand system expressed in terms of levels (as in the demand system in Brown and Heien, 1972).²⁷

D. Data

Trade and Production Data This analysis uses the International Sectoral Data Base at the two-digit ISIC level (1970-90) for the production data of 14 industrial countries.²⁸ It also uses the International Trade by Commodities Statistics at the two-digit Standard International Trade Classification (SITC) level (1970-90) for the trade data for the same set of countries. Tables 1 and 2 show the concordance of these two data sets.

Table 1.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS)

Source: Created by author.

^1/Goods in these classifications are allocated into two industries, agriculture and the food products industry (see Table 2).

Table 1.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS)

International Sectoral Data Base (ISDB) at the two-digit ISIC level				International Trade by Commodities Statistics (ITCS) at the two-digit SITC level
1	Agriculture, hunting, forestry, and fishing (AGR)	11	Agriculture	00	Live animals chiefly for food
				01	Meat and meat preparations 1/
				02	Dairy products and birds’ eggs 1/
				04	Cereals and cereal preparations 1/
				05	Vegetables and fruit 1/
				06	Sugar, sugar preparations, and honey 1/
				07	Coffee, tea, cocoa, spices, and manufactures thereof 1/
				29	Crude animal and vegetable materials, n.e.s.
		12	Forestry and lodging
		13	Fishing	03	Fish, crustaceans, molluscs, and preparations thereof 1/
2	Mining and quarrying (MID)	21	Coal mining	32	Coal, coke, and briquettes
		22	Crude petroleum and natural gas production	33	Petroleum, petroleum products, and related materials
				34	Gas, natural and manufactured
		23	Metal ore mining and other mining	35	Electric current
31	Manufacturing of food, beverages, and tobacco (FOD)	311/312	Food manufacturing	01	Meat and meat preparations 1/
				02	Dairy products and birds’ eggs 1/
				03	Fish, crustaceans, molluscs, and preparations thereof 1/
				04	Cereals and cereal preparations 1/
				05	Vegetables and fruit 1/
				06	Sugar, sugar preparations, and honey 1/
				07	Coffee, tea, cocoa, spices, and manufactures thereof 1/
				08	Feeding stuff for animals, excluding unmilled cereals
				09	Miscellaneous edible products and preparations
				22	Oil seeds and oleaginous fruit
				4	Animal and vegetable oils, fats, and waxes
		313	Beverage industries	11	Beverages
		314	Tobacco manufactures	12	Tobacco and tobacco manufactures
32	Textiles, wearing apparel, and leather industries (TEX)	321	Manufacture of textiles	21	Hides, skins and fur skins, raw
		322	Manufacture of wearing apparel except footwear	26	Textile fibers (except wool tops) and their wastes
				65	Textile yarn, fabrics, made-up articles, and related products
				84	Articles of apparel and clothing accessories
		323	Manufacture of leather and products of leather	61	Leather, leather manufactures, n.e.s., and dressed fur skins
		324	Manufacture of footwear	85	Footwear
34	Manufacturing of paper, and paper products (PAP)	341	Manufacture of paper and paper products	25	Pulp and waste paper
				64	Paper, paperboard, articles of paper, and paper-pulp/board
		342	Printing and publishing
35	Manufacturing of chemicals and of chemical, petroleum, coal, rubber, and plastic rubber products (CHE)	351/352	Manufacture of industrial chemicals and other chemical products	27	Crude fertilizers and crude materials (excluding coal)
		353/354	Petroleum refineries and manufacture of miscellaneous products of petroleum, and coal	5	Chemicals and related products, n.e.s.
		356	Manufacture of plastic products
		355	Manufacture of rubber products	23	Crude rubber (including synthetic and reclaimed)
				62	Rubber manufactures, n.e.s.
36	Manufacturing of nonmetallic products, except petroleum and coal products (MNM)	361	Manufacture of pottery, china, and earthenware	66	Nonmetallic mineral manufactures, n.e.s.
		362	Manufacture of glass and glass products
		369	Manufacture of other nonmetallic mineral products
37	Basic metal industries (BMI)	371	Iron and steel basic industries	28	Metalliferous ores and metal scrap
				67	Iron and steel
		372	Nonferrous metal basic industries	68	Nonferrous metals
38	Manufacturing of fabricated metal products, machinery, and equipment (MEQ)	381	Manufacture of fabricated metal products, except machinery and equipment	69	Manufactures of metal, n.e.s.
		382	Manufacture of machinery except electrical	71	Power-generating machinery and equipment
				72	Machinery specialized for particular industries
				73	Metalworking machinery
				74	General industrial machinery and equipment, and parts
		383	Manufacture of electrical machinery, apparatus, appliances, and supplies	76	Telecommunications and sound recording apparatus
				77	Electrical machinery, apparatus, and appliances, n.e.s.
				81	Sanitary, plumbing, heating, and lighting fixtures
		384	Manufacture of transport equipment	78	Road vehicles (including air-cushion vehicles)
				79	Other transport equipment
		385	Manufacture of professional and scientific, and measuring and controlling equipment	75	Office machines and automatic data processing equipment
				87	Professional, scientific, and controlling instruments
				88	Photographic apparatus, optical goods, and watches
39	Other manufacturing industries (MOT)	39	Other manufacturing industries	82	Furniture and parts thereof
				83	Travel goods, handbags, and similar containers
				89	Miscellaneous manufactured articles, n.e.s.

Source: Created by author.

^1/Goods in these classifications are allocated into two industries, agriculture and the food products industry (see Table 2).

View Table

Table 1.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS)

International Sectoral Data Base (ISDB) at the two-digit ISIC level				International Trade by Commodities Statistics (ITCS) at the two-digit SITC level
1	Agriculture, hunting, forestry, and fishing (AGR)	11	Agriculture	00	Live animals chiefly for food
				01	Meat and meat preparations 1/
				02	Dairy products and birds’ eggs 1/
				04	Cereals and cereal preparations 1/
				05	Vegetables and fruit 1/
				06	Sugar, sugar preparations, and honey 1/
				07	Coffee, tea, cocoa, spices, and manufactures thereof 1/
				29	Crude animal and vegetable materials, n.e.s.
		12	Forestry and lodging
		13	Fishing	03	Fish, crustaceans, molluscs, and preparations thereof 1/
2	Mining and quarrying (MID)	21	Coal mining	32	Coal, coke, and briquettes
		22	Crude petroleum and natural gas production	33	Petroleum, petroleum products, and related materials
				34	Gas, natural and manufactured
		23	Metal ore mining and other mining	35	Electric current
31	Manufacturing of food, beverages, and tobacco (FOD)	311/312	Food manufacturing	01	Meat and meat preparations 1/
				02	Dairy products and birds’ eggs 1/
				03	Fish, crustaceans, molluscs, and preparations thereof 1/
				04	Cereals and cereal preparations 1/
				05	Vegetables and fruit 1/
				06	Sugar, sugar preparations, and honey 1/
				07	Coffee, tea, cocoa, spices, and manufactures thereof 1/
				08	Feeding stuff for animals, excluding unmilled cereals
				09	Miscellaneous edible products and preparations
				22	Oil seeds and oleaginous fruit
				4	Animal and vegetable oils, fats, and waxes
		313	Beverage industries	11	Beverages
		314	Tobacco manufactures	12	Tobacco and tobacco manufactures
32	Textiles, wearing apparel, and leather industries (TEX)	321	Manufacture of textiles	21	Hides, skins and fur skins, raw
		322	Manufacture of wearing apparel except footwear	26	Textile fibers (except wool tops) and their wastes
				65	Textile yarn, fabrics, made-up articles, and related products
				84	Articles of apparel and clothing accessories
		323	Manufacture of leather and products of leather	61	Leather, leather manufactures, n.e.s., and dressed fur skins
		324	Manufacture of footwear	85	Footwear
34	Manufacturing of paper, and paper products (PAP)	341	Manufacture of paper and paper products	25	Pulp and waste paper
				64	Paper, paperboard, articles of paper, and paper-pulp/board
		342	Printing and publishing
35	Manufacturing of chemicals and of chemical, petroleum, coal, rubber, and plastic rubber products (CHE)	351/352	Manufacture of industrial chemicals and other chemical products	27	Crude fertilizers and crude materials (excluding coal)
		353/354	Petroleum refineries and manufacture of miscellaneous products of petroleum, and coal	5	Chemicals and related products, n.e.s.
		356	Manufacture of plastic products
		355	Manufacture of rubber products	23	Crude rubber (including synthetic and reclaimed)
				62	Rubber manufactures, n.e.s.
36	Manufacturing of nonmetallic products, except petroleum and coal products (MNM)	361	Manufacture of pottery, china, and earthenware	66	Nonmetallic mineral manufactures, n.e.s.
		362	Manufacture of glass and glass products
		369	Manufacture of other nonmetallic mineral products
37	Basic metal industries (BMI)	371	Iron and steel basic industries	28	Metalliferous ores and metal scrap
				67	Iron and steel
		372	Nonferrous metal basic industries	68	Nonferrous metals
38	Manufacturing of fabricated metal products, machinery, and equipment (MEQ)	381	Manufacture of fabricated metal products, except machinery and equipment	69	Manufactures of metal, n.e.s.
		382	Manufacture of machinery except electrical	71	Power-generating machinery and equipment
				72	Machinery specialized for particular industries
				73	Metalworking machinery
				74	General industrial machinery and equipment, and parts
		383	Manufacture of electrical machinery, apparatus, appliances, and supplies	76	Telecommunications and sound recording apparatus
				77	Electrical machinery, apparatus, and appliances, n.e.s.
				81	Sanitary, plumbing, heating, and lighting fixtures
		384	Manufacture of transport equipment	78	Road vehicles (including air-cushion vehicles)
				79	Other transport equipment
		385	Manufacture of professional and scientific, and measuring and controlling equipment	75	Office machines and automatic data processing equipment
				87	Professional, scientific, and controlling instruments
				88	Photographic apparatus, optical goods, and watches
39	Other manufacturing industries (MOT)	39	Other manufacturing industries	82	Furniture and parts thereof
				83	Travel goods, handbags, and similar containers
				89	Miscellaneous manufactured articles, n.e.s.

Source: Created by author.

^1/Goods in these classifications are allocated into two industries, agriculture and the food products industry (see Table 2).

Table 2.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS): Supplement ^1/

Source: Created by author.

^1/We need to divide import data at the two-digit SITC into two different industries at the two-digit ISIC level. For example, trade data classified as “Meat and meat preparation” (SITC 01) include both raw meat (which should be classified as agriculture) and processed meat (which should be classified as the food products industry). We take a very simply method. For example, we first compute the share of country A’s imports of processed meat products using multilateral trade data (e.g., the share of imports of meat and edible offal, salted, in brine, dried or smoked (SITC 012) and of meat and edible offal, prepared or preserved, fish extracts (SITC 014) in total imports of meat and meat preparation (SITC 01)). We then use this share to compute country A’s imports of processed meat products from country B (e.g., country A’s imports of meat and meat preparation (SITC 01) from country B times the share).

Table 2.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS): Supplement ^1/

Industry Data (ISDB) at the two-digit ISIC level			Trade Data (ITCS) at the three-digit SITC level
1 AGR	01	011	Meat, edible meat offals, fresh, chilled or frozen
31 FOD		012	Meat & edible offals, salted, in brine, dried/smoked
31 FOD		014	Meat & edible offals, prep./pres., fish extracts
1 AGR	02	022	Milk and cream
31 FOD		023	Butter
31 FOD		024	Cheese and curd
1 AGR		025	Eggs and yolks, fresh, dried or otherwise preserved
1 AGR	03	034	Fish, fresh (live or dead), chilled or frozen
31 FOD		035	Fish, dried, salted or in brine smoked fish
1 AGR		036	Crustaceans and molluscs, fresh, chilled, frozen etc.
31 FOD		037	Fish, crustaceans and molluscs, prepared or preserved
1 AGR	04	041	Wheat (including spelt) and meslin, unmilled
1 AGR		042	Rice
1 AGR		043	Barley, unmilled
1 AGR		044	Maize (corn), unmilled
1 AGR		045	Cereals, unmilled (no wheat, rice, barley or maize)
31 FOD		046	Meal and flour of wheat and flour of meslin
31 FOD		047	Other cereal meals and flours
31 FOD		048	Cereal preparations & preparations of flour of fruits or vegetables
1 AGR	05	054	Vegetables, fresh, chilled, frozen/preserved; roots, tubers
31 FOD		056	Vegetables, roots & tubers, prepared/preserved, n.e.s.
1 AGR		057	Fruit & nuts (not including oil nuts), fresh or dried
31 FOD		058	Fruit, preserved, and fruit preparations
1 AGR	06	061	Sugar and honey
31 FOD		062	Sugar confectionery and other sugar preparations
1 AGR	07	071	Coffee and coffee substitutes
1 AGR		072	Cocoa
31 FOD		073	Chocolate & other food preparations containing cocoa
1 AGR		074	Tea and mate
1 AGR		075	Spices

Source: Created by author.

^1/We need to divide import data at the two-digit SITC into two different industries at the two-digit ISIC level. For example, trade data classified as “Meat and meat preparation” (SITC 01) include both raw meat (which should be classified as agriculture) and processed meat (which should be classified as the food products industry). We take a very simply method. For example, we first compute the share of country A’s imports of processed meat products using multilateral trade data (e.g., the share of imports of meat and edible offal, salted, in brine, dried or smoked (SITC 012) and of meat and edible offal, prepared or preserved, fish extracts (SITC 014) in total imports of meat and meat preparation (SITC 01)). We then use this share to compute country A’s imports of processed meat products from country B (e.g., country A’s imports of meat and meat preparation (SITC 01) from country B times the share).

View Table

Table 2.

Concordance Between Industry Data (ISDB) and Trade Data (ITCS): Supplement ^1/

Industry Data (ISDB) at the two-digit ISIC level			Trade Data (ITCS) at the three-digit SITC level
1 AGR	01	011	Meat, edible meat offals, fresh, chilled or frozen
31 FOD		012	Meat & edible offals, salted, in brine, dried/smoked
31 FOD		014	Meat & edible offals, prep./pres., fish extracts
1 AGR	02	022	Milk and cream
31 FOD		023	Butter
31 FOD		024	Cheese and curd
1 AGR		025	Eggs and yolks, fresh, dried or otherwise preserved
1 AGR	03	034	Fish, fresh (live or dead), chilled or frozen
31 FOD		035	Fish, dried, salted or in brine smoked fish
1 AGR		036	Crustaceans and molluscs, fresh, chilled, frozen etc.
31 FOD		037	Fish, crustaceans and molluscs, prepared or preserved
1 AGR	04	041	Wheat (including spelt) and meslin, unmilled
1 AGR		042	Rice
1 AGR		043	Barley, unmilled
1 AGR		044	Maize (corn), unmilled
1 AGR		045	Cereals, unmilled (no wheat, rice, barley or maize)
31 FOD		046	Meal and flour of wheat and flour of meslin
31 FOD		047	Other cereal meals and flours
31 FOD		048	Cereal preparations & preparations of flour of fruits or vegetables
1 AGR	05	054	Vegetables, fresh, chilled, frozen/preserved; roots, tubers
31 FOD		056	Vegetables, roots & tubers, prepared/preserved, n.e.s.
1 AGR		057	Fruit & nuts (not including oil nuts), fresh or dried
31 FOD		058	Fruit, preserved, and fruit preparations
1 AGR	06	061	Sugar and honey
31 FOD		062	Sugar confectionery and other sugar preparations
1 AGR	07	071	Coffee and coffee substitutes
1 AGR		072	Cocoa
31 FOD		073	Chocolate & other food preparations containing cocoa
1 AGR		074	Tea and mate
1 AGR		075	Spices

Source: Created by author.

^1/We need to divide import data at the two-digit SITC into two different industries at the two-digit ISIC level. For example, trade data classified as “Meat and meat preparation” (SITC 01) include both raw meat (which should be classified as agriculture) and processed meat (which should be classified as the food products industry). We take a very simply method. For example, we first compute the share of country A’s imports of processed meat products using multilateral trade data (e.g., the share of imports of meat and edible offal, salted, in brine, dried or smoked (SITC 012) and of meat and edible offal, prepared or preserved, fish extracts (SITC 014) in total imports of meat and meat preparation (SITC 01)). We then use this share to compute country A’s imports of processed meat products from country B (e.g., country A’s imports of meat and meat preparation (SITC 01) from country B times the share).

The two-digit ISIC data are highly aggregated and cover heterogeneous products. A much higher level of disaggregation would be desirable; however, as discussed in Section III.A, improving the level of disaggregation, especially of the production cost data, would come only at the cost of losing cross-sectoral variations.

Input-Output Data This study also uses the OECD Input-Output Database, which can aid the analysis in two ways. First, it allows us to obtain industry-level domestic demand D_it, which is defined as $D_{it}=X_{it}-{\displaystyle \sum _{j\ne i}{E}_{ijt}}$, where X_it is industry-level demand for gross output (or total output) of country i and E_ij is industry-level exports to foreign country j. More specifically, these data are used to obtain a proxy for industry-level gross output (X_it). The trade literature typically sets X_it ≈ V_it, where V_it is industry-level value added, and therefore input-output data are not used.²⁹ This approximation, however, can grossly understate or overstate the actual value of industry-level gross output (X_it), because value added (a proxy for the supply of gross output) equals gross domestic product (a proxy for the demand for gross output) only at the aggregate level and not at the industry level.³⁰ This paper therefore sets $X_{it}\approx \frac{1}{{\upsilon }_{i,90}}{V}_{it}$, where υ_i,90 is the industry-level ratio of value added to gross output for 1990.³¹

Second, the OECD Input-Output Database is used to empirically distinguish the final (consumption) goods and intermediate inputs industries at the two-digit ISIC level. More specifically, the shares of intermediate and final demand in gross output are computed for 10 OECD member countries in 1990 for each industry (see Table 3 for these shares). If, for more than half of the countries in the sample, more than two-thirds of demand for the output of a given industry is for use as intermediate inputs, we classify that industry as part of the intermediate inputs sector. Final (consumption) goods industries are classified in an analogous manner.

Table 3.

The Final and the Intermediate Demand in Gross Output

Source: Created by author using the OECD STAN Input-Output Database for 1990 for all except AUS, ITA and NLD. The input-output tables from 1989, 1985 and 1986, are used for these countries, respectively.

^1/If an industry has more than half of the countries in the sample with the share of intermediate demand greater than 2/3, then we call such an industry as the intermediate inputs industry (see industries in bold).

^2/If an industry has more than half of the countries in the sample with the share of final demand greater than 2/3, then we call such an industry as the final/consumption goods industry (see industries in bold).

Table 3.

The Final and the Intermediate Demand in Gross Output

ISIC code	ISIC definition	Country										No. of Countries with Share > 0.66
		AUS	CAN	DNK	FRA	DEU	ITA	JPN	NLD	GBR	USA
The Share of Intermediate Demand in Gross Output1/
1	Agriculture, forestry & fishing	0.56	0.71	0.70	0.63	0.69	0.70	0.77	0.66	0.66	0.78	7
2	Mining & quarrying	0.52	0.58	0.76	0.85	0.96	0.88	1.00	0.73	0.65	0.86	7
31	Food, beverages & tobacco	0.24	0.32	0.24	0.25	0.31	0.34	0.33	0.32	0.36	0.33	0
32	Textiles, apparel & leather	0.37	0.36	0.24	0.32	0.28	0.33	0.41	0.24	0.36	0.39	0
33	Wood products & furniture	0.56	0.48	0.42	0.44	0.39	0.50	0.82	0.52	0.57	0.57	1
34	Paper, paper products & printing	0.74	0.53	0.69	0.68	0.77	0.74	0.88	0.62	0.74	0.67	8
351	Industrial chemicals	0.81	0.65	0.61	0.53	0.56	0.70	0.79	0.44	0.50	0.70	4
352	Drugs & medicines	0.64	0.20	0.18	0.10	na	0.32	0.68	0.25	0.53	0.35	2
353-4	Petroleum & coal products	0.60	0.54	0.77	0.42	0.48	0.59	0.74	0.34	0.44	0.47	2
355-6	Rubber & plastic products	0.79	0.71	0.51	0.69	0.62	0.68	0.87	0.58	0.73	0.78	7
36	Non-metallic mineral products	0.93	0.82	0.75	0.73	0.76	0.76	0.91	0.72	0.80	0.86	10
371	Iron & steel	0.85	0.92	0.73	0.76	0.80	0.79	0.94	0.56	0.79	0.95	9
372	Non-ferrous metals	0.48	0.46	0.74	0.69	0.73	0.93	0.92	na	0.68	0.90	8
381	Metal products	0.70	0.67	0.55	0.66	0.64	0.61	0.84	0.53	0.47	0.85	4
382	Non-electrical machinery	0.32	0.32	0.29	0.23	0.27	0.24	0.30	0.30	0.36	0.37	0
385	Office & computing machinery	na	0.24	na	0.25	0.20	0.19	0.18	0.22	0.18	0.20	2
383	Electrical apparatus, n.e.c.	0.48	0.55	0.54	0.39	0.36	0.47	0.50	0.32	0.41	0.58	0
383	Radio, TV & communication	0.36	0.39	0.23	0.35	na	0.30	0.43	na	0.33	0.42	2
384	Shipbuilding & repairing	0.07	0.24	0.19	0.24	0.08	0.62	0.20	0.21	0.30	0.07	0
384	Other transport	0.52	0.29	0.14	0.17	na	0.14	0.33	0.19	0.18	0.20	1
384	Motor vehicles	0.33	0.29	na	0.18	0.22	0.20	0.48	0.16	0.23	0.29	1
384	Aircraft	0.19	0.34	na	0.23	0.23	0.44	0.26	0.23	0.24	0.23	1
385	Professional goods	0.34	0.41	0.22	0.24	0.29	0.25	0.25	0.26	0.34	0.29	0
39	Other manufacturing	0.40	0.29	0.20	0.13	0.38	0.09	0.38	0.30	0.15	0.22	0
	Total	0.40	0.43	0.35	0.39	0.43	0.43	0.47	0.36	0.43	0.41
The Share of Final Demand in Gross Output2/
1	Agriculture, forestry & fishing	0.44	0.29	0.30	0.37	0.31	0.30	0.23	0.34	0.34	0.22	0
2	Mining & quarrying	0.48	0.42	0.24	0.15	0.04	0.12	0.00	0.27	0.35	0.14	0
31	Food, beverages & tobacco	0.76	0.68	0.76	0.75	0.69	0.66	0.67	0.68	0.64	0.67	9
32	Textiles, apparel & leather	0.63	0.64	0.76	0.68	0.72	0.67	0.59	0.76	0.64	0.61	5
33	Wood products & furniture	0.44	0.52	0.58	0.56	0.61	0.50	0.18	0.48	0.43	0.43	0
34	Paper, paper products & printing	0.26	0.47	0.31	0.32	0.23	0.26	0.12	0.38	0.26	0.33	0
351	Industrial chemicals	0.19	0.35	0.39	0.47	0.44	0.30	0.21	0.56	0.50	0.30	0
352	Drugs & medicines	0.36	0.80	0.82	0.90	na	0.68	0.32	0.75	0.47	0.65	6
353-4	Petroleum & coal products	0.40	0.46	0.23	0.58	0.52	0.41	0.26	0.66	0.56	0.53	1
355-6	Rubber & plastic products	0.21	0.29	0.49	0.31	0.38	0.32	0.13	0.42	0.27	0.22	0
36	Non-metallic mineral products	0.07	0.18	0.25	0.27	0.24	0.24	0.09	0.28	0.20	0.14	0
371	Iron & steel	0.15	0.08	0.27	0.24	0.20	0.21	0.06	0.44	0.21	0.05	0
372	Non-ferrous metals	0.52	0.54	0.26	0.31	0.27	0.07	0.08	na	0.32	0.10	1
381	Metal products	0.30	0.33	0.45	0.34	0.36	0.39	0.16	0.47	0.53	0.15	0
382	Non-electrical machinery	0.68	0.68	0.71	0.77	0.73	0.76	0.70	0.70	0.64	0.63	8
385	Office & computing machinery	na	0.76	na	0.75	0.80	0.81	0.82	0.78	0.82	0.80	10
383	Electrical apparatus, n.e.c.	0.52	0.45	0.46	0.61	0.64	0.53	0.50	0.68	0.59	0.42	1
383	Radio, TV & communication	0.64	0.61	0.77	0.65	na	0.70	0.57	na	0.67	0.58	5
384	Shipbuilding & repairing	0.93	0.76	0.81	0.76	0.92	0.38	0.80	0.79	0.70	0.93	9
384	Other transport	0.48	0.71	0.86	0.83	na	0.86	0.67	0.81	0.82	0.80	9
384	Motor vehicles	0.67	0.71	na	0.82	0.78	0.80	0.52	0.84	0.77	0.71	9
384	Aircraft	0.81	0.66	na	0.77	0.77	0.56	0.74	0.77	0.76	0.77	9
385	Professional goods	0.66	0.59	0.78	0.76	0.71	0.75	0.75	0.74	0.66	0.71	8
39	Other manufacturing	0.60	0.71	0.80	0.87	0.62	0.91	0.62	0.70	0.85	0.78	7
	Total	0.60	0.57	0.65	0.61	0.57	0.57	0.53	0.64	0.57	0.59

Source: Created by author using the OECD STAN Input-Output Database for 1990 for all except AUS, ITA and NLD. The input-output tables from 1989, 1985 and 1986, are used for these countries, respectively.

^1/If an industry has more than half of the countries in the sample with the share of intermediate demand greater than 2/3, then we call such an industry as the intermediate inputs industry (see industries in bold).

^2/If an industry has more than half of the countries in the sample with the share of final demand greater than 2/3, then we call such an industry as the final/consumption goods industry (see industries in bold).

View Table

Table 3.

The Final and the Intermediate Demand in Gross Output

ISIC code	ISIC definition	Country										No. of Countries with Share > 0.66
		AUS	CAN	DNK	FRA	DEU	ITA	JPN	NLD	GBR	USA
The Share of Intermediate Demand in Gross Output1/
1	Agriculture, forestry & fishing	0.56	0.71	0.70	0.63	0.69	0.70	0.77	0.66	0.66	0.78	7
2	Mining & quarrying	0.52	0.58	0.76	0.85	0.96	0.88	1.00	0.73	0.65	0.86	7
31	Food, beverages & tobacco	0.24	0.32	0.24	0.25	0.31	0.34	0.33	0.32	0.36	0.33	0
32	Textiles, apparel & leather	0.37	0.36	0.24	0.32	0.28	0.33	0.41	0.24	0.36	0.39	0
33	Wood products & furniture	0.56	0.48	0.42	0.44	0.39	0.50	0.82	0.52	0.57	0.57	1
34	Paper, paper products & printing	0.74	0.53	0.69	0.68	0.77	0.74	0.88	0.62	0.74	0.67	8
351	Industrial chemicals	0.81	0.65	0.61	0.53	0.56	0.70	0.79	0.44	0.50	0.70	4
352	Drugs & medicines	0.64	0.20	0.18	0.10	na	0.32	0.68	0.25	0.53	0.35	2
353-4	Petroleum & coal products	0.60	0.54	0.77	0.42	0.48	0.59	0.74	0.34	0.44	0.47	2
355-6	Rubber & plastic products	0.79	0.71	0.51	0.69	0.62	0.68	0.87	0.58	0.73	0.78	7
36	Non-metallic mineral products	0.93	0.82	0.75	0.73	0.76	0.76	0.91	0.72	0.80	0.86	10
371	Iron & steel	0.85	0.92	0.73	0.76	0.80	0.79	0.94	0.56	0.79	0.95	9
372	Non-ferrous metals	0.48	0.46	0.74	0.69	0.73	0.93	0.92	na	0.68	0.90	8
381	Metal products	0.70	0.67	0.55	0.66	0.64	0.61	0.84	0.53	0.47	0.85	4
382	Non-electrical machinery	0.32	0.32	0.29	0.23	0.27	0.24	0.30	0.30	0.36	0.37	0
385	Office & computing machinery	na	0.24	na	0.25	0.20	0.19	0.18	0.22	0.18	0.20	2
383	Electrical apparatus, n.e.c.	0.48	0.55	0.54	0.39	0.36	0.47	0.50	0.32	0.41	0.58	0
383	Radio, TV & communication	0.36	0.39	0.23	0.35	na	0.30	0.43	na	0.33	0.42	2
384	Shipbuilding & repairing	0.07	0.24	0.19	0.24	0.08	0.62	0.20	0.21	0.30	0.07	0
384	Other transport	0.52	0.29	0.14	0.17	na	0.14	0.33	0.19	0.18	0.20	1
384	Motor vehicles	0.33	0.29	na	0.18	0.22	0.20	0.48	0.16	0.23	0.29	1
384	Aircraft	0.19	0.34	na	0.23	0.23	0.44	0.26	0.23	0.24	0.23	1
385	Professional goods	0.34	0.41	0.22	0.24	0.29	0.25	0.25	0.26	0.34	0.29	0
39	Other manufacturing	0.40	0.29	0.20	0.13	0.38	0.09	0.38	0.30	0.15	0.22	0
	Total	0.40	0.43	0.35	0.39	0.43	0.43	0.47	0.36	0.43	0.41
The Share of Final Demand in Gross Output2/
1	Agriculture, forestry & fishing	0.44	0.29	0.30	0.37	0.31	0.30	0.23	0.34	0.34	0.22	0
2	Mining & quarrying	0.48	0.42	0.24	0.15	0.04	0.12	0.00	0.27	0.35	0.14	0
31	Food, beverages & tobacco	0.76	0.68	0.76	0.75	0.69	0.66	0.67	0.68	0.64	0.67	9
32	Textiles, apparel & leather	0.63	0.64	0.76	0.68	0.72	0.67	0.59	0.76	0.64	0.61	5
33	Wood products & furniture	0.44	0.52	0.58	0.56	0.61	0.50	0.18	0.48	0.43	0.43	0
34	Paper, paper products & printing	0.26	0.47	0.31	0.32	0.23	0.26	0.12	0.38	0.26	0.33	0
351	Industrial chemicals	0.19	0.35	0.39	0.47	0.44	0.30	0.21	0.56	0.50	0.30	0
352	Drugs & medicines	0.36	0.80	0.82	0.90	na	0.68	0.32	0.75	0.47	0.65	6
353-4	Petroleum & coal products	0.40	0.46	0.23	0.58	0.52	0.41	0.26	0.66	0.56	0.53	1
355-6	Rubber & plastic products	0.21	0.29	0.49	0.31	0.38	0.32	0.13	0.42	0.27	0.22	0
36	Non-metallic mineral products	0.07	0.18	0.25	0.27	0.24	0.24	0.09	0.28	0.20	0.14	0
371	Iron & steel	0.15	0.08	0.27	0.24	0.20	0.21	0.06	0.44	0.21	0.05	0
372	Non-ferrous metals	0.52	0.54	0.26	0.31	0.27	0.07	0.08	na	0.32	0.10	1
381	Metal products	0.30	0.33	0.45	0.34	0.36	0.39	0.16	0.47	0.53	0.15	0
382	Non-electrical machinery	0.68	0.68	0.71	0.77	0.73	0.76	0.70	0.70	0.64	0.63	8
385	Office & computing machinery	na	0.76	na	0.75	0.80	0.81	0.82	0.78	0.82	0.80	10
383	Electrical apparatus, n.e.c.	0.52	0.45	0.46	0.61	0.64	0.53	0.50	0.68	0.59	0.42	1
383	Radio, TV & communication	0.64	0.61	0.77	0.65	na	0.70	0.57	na	0.67	0.58	5
384	Shipbuilding & repairing	0.93	0.76	0.81	0.76	0.92	0.38	0.80	0.79	0.70	0.93	9
384	Other transport	0.48	0.71	0.86	0.83	na	0.86	0.67	0.81	0.82	0.80	9
384	Motor vehicles	0.67	0.71	na	0.82	0.78	0.80	0.52	0.84	0.77	0.71	9
384	Aircraft	0.81	0.66	na	0.77	0.77	0.56	0.74	0.77	0.76	0.77	9
385	Professional goods	0.66	0.59	0.78	0.76	0.71	0.75	0.75	0.74	0.66	0.71	8
39	Other manufacturing	0.60	0.71	0.80	0.87	0.62	0.91	0.62	0.70	0.85	0.78	7
	Total	0.60	0.57	0.65	0.61	0.57	0.57	0.53	0.64	0.57	0.59

Source: Created by author using the OECD STAN Input-Output Database for 1990 for all except AUS, ITA and NLD. The input-output tables from 1989, 1985 and 1986, are used for these countries, respectively.

^1/If an industry has more than half of the countries in the sample with the share of intermediate demand greater than 2/3, then we call such an industry as the intermediate inputs industry (see industries in bold).

^2/If an industry has more than half of the countries in the sample with the share of final demand greater than 2/3, then we call such an industry as the final/consumption goods industry (see industries in bold).