The World Economy (2012)doi: 10.1111/j.1467-9701.2012.01471.x

The World Economy

Horizontal and Vertical Intra-

industry Trade: An Empirical Test

of the ‘Homogeneity Hypothesis’

Rosanna Pittiglio1,2

1Department of Management and Accounting, Second University of Naples, Naples, Italy and2Economic Studies, University of Dundee, Dundee, UK

1. INTRODUCTION

IN the last three decades, intra-industry trade (henceforth IIT) – the simulta-

neous import and export of goods within the same industry – has attracted

the attention of many economists around the world, resulting in a large number

of theoretical and empirical studies.

Theoretical models have suggested that IIT is determined by both country-

specific factors (i.e. income levels, economic size, factor endowments and

distance) and industry-specific factors (i.e. market structure, product differentia-

tion, economies of scale). Yet, empirical studies have found stronger support

for country-specific determinants (Greenaway et al., 1994, 1995; Faustino and

Leitao, 2007).

It should however be noted that these empirical studies have produced

results that are not always consistent with the theoretical expectations concern-

ing the determinants of this kind of trade. A number of reasons and solutions

have been offered for what Greenaway et al. (1999) defined ‘fragility of

results’. For example, Torstensson (1996) referred to measurement and sample

selection problems; Hummels and Levinsohn (1995) focused exclusively on

country-specific effects; Greenaway et al. (1999) tried to overcome this fragility

by focusing on a set of similar industrial countries. In our opinion, one of the

possible reasons for this might lie in the assumption of country and industry

homogeneity, which has characterised, to our knowledge, all empirical studies

The author gratefully acknowledges an anonymous referee for his very helpful comments. She alsothanks Luigi Benfratello, Hassan Molana, Catia Montagna and Filippo Reganati for their veryuseful suggestions. The usual disclaimers apply.

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920 R. PITTIGLIO

on IIT. As a result of this assumption, the effects of country characteristics (e.g.

market size) on the index of intra-industry specialisation are considered invariant

across industries, and the effects of industry characteristics on the index of intra-

industry specialisation are presumed to be invariant across country pairs (i.e. the

home industry characteristics are used as a proxy for those in every country in

the sample). Since the pioneering study of Balassa and Bauwens (1987), this

hypothesis has been common to all analyses of two-way trade. As far as we

know, only a few empirical works have seen this aspect as a limit and then only

with regard to industry-specific characteristics (see Greenaway et al., 1999; Cre-

spo and Fontoura, 2004). To take account of this, some authors (see for example

Greenaway et al., 1999) focused on a set of similar industrial countries.

Given this premise, in this study we consider both industry- and country-

specific determinants of IIT. Specifically, we build on and take forward a meth-

odology pioneered by Balassa and Bauwens (1987) and seek to avoid the more

extreme measurement problems by allowing for heterogeneity among sectors

when country-specific factors are analysed and among countries when industry-

specific factors are considered. In other words, we examine the determinants of

horizontal and vertical IIT (VIIT) using a data set where the variables are

different not only between two countries but also among sectors of the same

country. Moreover, to increase the robustness of our empirical results, we

compare our findings with those obtained using the traditional method (i.e.

using country-level variables that do not consider heterogeneity among sectors

and industry variables) through specific econometric tests designed to compare

one model with another when different sets of explanatory variables are used.

To the best of our knowledge, a similar analysis has not yet appeared in the

empirical literature on IIT.

The analysis refers to OECD IIT in manufactured goods over the period 1997

to 2006. The remainder of the paper is structured as follows: Section 2 reports a

brief survey of the literature on horizontal and vertical IIT and describes the issue.

Section 3 presents the index, the procedure for disentangling vertical and horizon-

tal IIT and some figures regarding the selected countries. Section 4 sets out the

hypotheses to be tested and the variables and includes econometric tests used for

testing the homogeneity hypothesis. Finally, Section 5 concludes.

2. THEORETICAL AND EMPIRICAL FOUNDATIONS

As is standard in the literature, theoretical explanations of IIT can be categor-

ised into two classes of models: horizontal IIT models, which explain two-way

trade in horizontally differentiated goods (exhibiting different characteristics at

a similar level of quality), and vertical IIT models, which explain two-way

trade in vertically differentiated products (characterised by quality variation

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AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 921

between products). These two sets of models are different in their predictions

and effects.

The first set dates back to the pioneering studies of Krugman (1979, 1981),

Lancaster (1980) and Helpman (1981). In these models, horizontal IIT results

from the interaction between, on the supply side, horizontal product differentia-

tion, monopolistic competition and increasing returns to scale and, on the demand

side, idiosyncratic consumer preferences (or love-of-variety). Helpman and Krug-

man (1985) also include factor endowment differences in a two-sector model

(where one sector produces a homogeneous good and the other a differentiated

product) and explain the coexistence of inter-industry trade and IIT. By assuming

monopolistically competitive markets, they find that when two countries have

identical factor endowments, the volume of international trade – exclusively

IIT – is determined by differences in their relative country size. In particular, the

closer two countries are in size, the greater the volume of trade will be.

The second set of models considers vertically differentiated products. Falvey

and Kierzkowski (1987), by extending the work of Falvey (1981), showed the

existence of vertical IIT without resorting to imperfect competition and increas-

ing returns to scale. In this model, on the supply side, it is assumed an econ-

omy with two sectors, one producing a single homogenous good and the other

producing different qualities of the same product. The model also considers

technology differences and product quality linked to the capital intensity of

production. On the demand side, consumers have identical preferences, and the

demand for each quality, given relative prices, depends on the income of indi-

viduals. In this framework, a large difference in income levels increases the

share of vertical IIT because income differences generate dissimilarity in

demand. Inequalities in income distribution ensure that both countries will

demand all the available quality levels. Therefore, Falvey and Kierzkowski

(1987) suggest that since a higher-quality product requires higher capital inten-

sity in production, in an open economy, the capital-rich country will export

high-quality products, whereas the labour-rich country will export low-quality

products; as a consequence, the share of IIT in bilateral trade should be greater

the greater the difference in relative factor endowments between the two coun-

tries. A similar vertical IIT model was developed by Flam and Helpman

(1987), who find that the North–South trade structure is determined by techno-

logical differences, income differences and income distribution differences

between the North and the South. The source of quality differentiation is not

the amount of capital used in producing the product, like in the Falvey and

Kierzkowski (1987) model, but the technology used. Labour input per unit of

output of the quality-differentiated product differs between countries, and the

North has a comparative advantage in high-quality products. So, the North

exports industrial products of high quality and imports industrial products of

lower quality from the South. Given an overlap in income distribution, IIT

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922 R. PITTIGLIO

emerges. To sum up, these models showed that VIIT takes place between coun-

tries with different factor endowments (supply side) and with differences in

per-capita income (demand side).1

More recently, some researchers focused on the role of demand patterns in

explaining the two-way trade. Tharakan and Kerstens (1995), within a VIIT

model, find that income distribution patterns in the North and in the South

could lead to the situation in which low-income groups in the former will gen-

erate demand for low-quality varieties and the high-income groups in the South

for high-quality varieties, which in turn will lead to IIT within industry

between the two types of countries. Moreover, since horizontal IIT is more

likely to take place between countries with similar factor endowments, such

countries are prone to have similar income levels, and hence, the demand for

different (or all) varieties of the same product is likely to be higher than in the

case of countries with different factor endowments. By considering a modified

version of Falvey and Kierzkowski model, Gullstrand (2002) pay attention to

the role of income distribution and per-capita income as demand-side determi-

nants of VIIT and find that both the distribution of income within industries

and per capita income differences between countries, as well as the average

market size, are major explanatory factors behind vertical IIT.

Gullstrand (2002) modifies the demand side of Falvey and Kierzkowski

(1987) so as to clarify the importance of considering a direct effect of, as well

as an interaction between, income distribution within, and income distribution

(or income differences) between, countries. In this model, an increase in average

per capita income leads to a larger share of the population being found in rela-

tively high-income strata. As a consequence, a larger share of consumers can

afford the high-quality variety. The disposable income within each income stra-

tum depends, however, on the redistribution scheme, which, as highlighted by the

authors, affects the demanded quantity for the high- and the low-quality varieties.

From an empirical point of view, several scholars have looked at the deter-

minants of IIT and their policy implications. Broadly speaking, if we exclude

the studies of Helpman (1987) and Hummels and Levinsohn (1995), which are

the only two studies testing a specific theoretical model, most empirical works

have sought to identify characteristics that are common to all, or most, of these

models using an eclectic approach. In general and following the distinction pro-

vided by Greenaway and Milner (1989), the empirical works on the topic can be

divided into three groups according to whether they aim to test industry-specific

characteristics (i.e. market structure, minimum efficient scale, product differen-

tiation and foreign direct investment, see among others Pagoulatos and

1 Vertically differentiated products can be also produced by more or less qualified employees (Gab-szewics et al., 1981) or depend on the differences in research and development expenditure (Shakedand Sutton, 1984).

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AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 923

Sorensen, 1975; Caves, 1981; Lundberg, 1982; Toh, 1982; Hamilton and Kniest,

1991; Ray, 1991; Lundberg, 1992; Clark, 1993; Greenaway et al., 1995; Aturupane

et al., 1999; Crespo and Fontoura, 2004), country-specific characteristics (i.e. fac-

tor endowments, income levels, level of development, trade imbalance, distance,

trade agreements, see among others Loertsher and Wolter, 1980; Havrylyshyn and

Civan, 1983; Balassa, 1986; Greenaway and Milner, 1986; Helpman, 1987;

Globerman and Dean, 1990; Ballance et al., 1992; Greenaway et al., 1994; Stone

and Lee, 1995; Montout et al., 2002; Reganati and Pittiglio, 2005) or both (see

among others Somma, 1994; Greenaway et al., 1999; Hu and Ma, 1999; Blanes and

Martın, 2000; Veeramani, 2002).

The obvious shortcoming of these empirical studies is the assumption of

homogeneity between countries when industry-specific factors are analysed and

between sectors when country factors are examined. In fact, in these studies,

the researchers have measured the characteristics of a particular industry refer-

ring exclusively to information on the conditions of the domestic country, not

the two countries involved in bilateral trade, as we would consider appropriate

(i.e. home characteristics to proxy those in every country in the sample). In

addition, by assuming that the homogeneity hypothesis also exists among sec-

tors, these earlier studies overlooked the factorial endowment differences

between industries within the same country (i.e. country characteristics are con-

sidered invariant across industries).2

Such aspects were already present in Balassa and Bauwens, 1987, who anal-

ysed the country-specific and industry-specific determinants in the two-way

trade of 38 countries (20 developing countries and 18 industrialised countries),

using information on US industry characteristics as proxy for the industry char-

acteristics of other countries considered. Furthermore, in this analysis, the

authors did not consider the factorial endowment differences between sectors.

Greenaway et al. (1999) were the first to ‘criticise’ in part the study by Balassa

and Bauwens, but referring exclusively to industry characteristic determinants,

pointing out that the method of measuring industry characteristics used by these

authors could be considered appropriate only if the analysis focused on countries

with similar development levels and not when trade flows between developed

countries and developing countries were considered: ‘. . ..using US concentration

ratios, or US scale economy measures to proxy the equivalent characteristics in

Sudan and Singapore to explain their bilateral IIT does not inspire confidence in

the credibility or robustness of the results’ (Greenaway et al., 1999, p. 367).

Consequently, and in an attempt to mitigate this aspect, the authors narrowed

their analysis to a smaller number of countries with a similar development

level, such as the European Union countries before the enlargement in 1995. In

2 Therefore, in these works, for example, the differences in terms of factorial endowments amongindustries were overlooked.

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924 R. PITTIGLIO

this way, they could use UK industry characteristics as a proxy for industry

characteristics in other countries: ‘Given the similarities in industrial structure

among many of the EU countries, this is more admissible than in the Balassa-

Bauwens study’ (Greenaway et al., 1999, p. 367).

More recently, Crespo and Fontoura (2004) have again underlined this

aspect, highlighting their scepticism about this approach: ‘we are sceptical

about the advantage of doing this if the observation for the industry (product)

is the same for every country in each bilateral transaction’ (Crespo and

Fontoura, 2004, p. 64). Moreover, these empirical studies do not consider heter-

ogeneity among sectors when country-specific determinants are considered.

We believe that not considering this aspect could distort the results for two

key reasons: first, differences in terms of factor endowments among industries

would be overlooked (i.e. as differences in factor endowments exist not only

among countries but also among sectors within the same country) and second,

the income distribution within sectors would be assumed to be the same among

different countries. Given the importance of these factors in determining the

pattern of horizontal and=or vertical IIT highlighted by the theoretical literature

surveyed earlier, maintaining these assumptions could clearly introduce biases

in the results. Thus, on the supply side, if relative differences exist in terms of

factor endowments across sectors or countries, these will result in differences

in relative prices (in our case at the sectoral level) between countries. These

differences – even assuming the same preference structure across countries over

all the goods produced in the different sectors – will result in a comparative

advantage pattern that could impact on IIT. On the demand side, the literature

on IIT indicates that countries that are similar in terms of income and size

should have a larger share of IIT in bilateral trade. In our opinion, when in the

empirical studies the dissimilarity=similarity between two countries and the

market size differences, for instance, are measured by using the absolute differ-

ences in total per capita GDP and the absolute difference in total GDP, respec-

tively, we fail to take into account that countries could have a different

distribution of preferences among different goods.

Before starting the empirical analysis, in Tables 1 and 2, we summarise the

results obtained from the most relevant empirical studies on VIIT (Table 1)

and horizontal (HIIT) (Table 2). It should be noted that this literature review is

not exhaustive but is limited to the research studies that we consider relevant

to the purpose of our paper.

3. INTRA-INDUSTRY TRADE: MEASURE AND DESCRIPTIVE ANALYSIS

In this section, we introduce the unadjusted IIT index developed by Grubel and

Lloyd (1975), hereafter referred to as GL, and explain its use as the dependent

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TABLE 1Empirical Literature Review on Determinants of Vertical Intra-industry Trade

Description Negative Effect Positive Effect

DIFYP Difference inper capitaGDP

Greenaway et al. (1994, 1999);Blanes and Martın (2000);Reganati and Pittiglio(2005); Thorpe and Zhang (2005);Caetano and Galego (2007);Cabral et al. (2008)

Gullstrand (2002); Martın-Montanerand Rıos (2002); Veeramani (2002);Crespo and Fontoura (2004); Byunand Lee (2005); Pittiglio (2009);Leitao et al. (2010);

DIFY Differencein GDP

Blanes and Martın (2000)

SIZE Economicsize

Greenaway et al. (1994, 1999);Stone and Lee (1995);Gullstrand (2002); Martın-Montanerand Rıos (2002); Crespo andFontoura (2004); Reganati andPittiglio (2005, 2009)Caetano and Galego (2007);Cabral et al. (2008); Yoshida et al.(2008); Jansen and Luthje (2009)

DIST Geographicaldistance

Stone and Lee (1995); Blanesand Martın (2000); Thorpe andZhang (2005); Reganati andPittiglio (2005); Cabral et al.(2008); Jansen and Luthje(2009); Leitao et al. (2010)

MS Marketstructure

Greenaway et al. (1995, 1999);Celi (1999);Crespo and Fontoura (2004);Byun and Lee (2005)

ES Scaleeconomies

Aturupane et al. (1999); Celi (1999)

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 925

variable for our empirical analysis. We also provide some stylised facts regard-

ing the specific data we use in the empirical analysis of the two-way trade

between 12 OECD countries (Belgium, Canada, the Czech Republic, Denmark,

Finland, France, Germany, Japan, the United Kingdom, Spain, Sweden and the

United States). The countries selected are the most representative, accounting

for about two-third of the total volume of OECD countries.3

a. Methodology used for Calculating the Intra-industry Trade Index

Following the prevalent literature on IIT, the unadjusted GL index has been

used for measuring the share of two-way trade on total trade among the sample

3 The impossibility of considering all OECD countries was due to the lack of data on explanatoryvariables with four dimensions (reporter country, partner country, sector and time).

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TABLE 2Empirical Literature Review on determinants of Horizontal Intra-Industry Trade

Description Negative Effect Positive Effect

DIFYP Difference inper capitaGDP

Greenaway et al. (1994, 1999);Blanes and Martın(2000); Thorpe and Zhang (2005);Cabral et al. (2008)

Byun and Lee (2005);

DIFY Differencein GDP

Greenaway et al. (1994); Blanesand Martın (2000)

Greenaway et al. (1999);

SIZE Economic size Greenaway et al. (1994, 1999);Crespo and Fontoura (2004);Cabral et al. (2008)

DIST Geographicaldistance

Stone and Lee (1995); Blanes andMartın (2000); Crespo and Fontoura(2004); Thorpe and Zhang (2005);Cabral et al. (2008)

MS Marketstructure

Greenaway et al. (1995);Emirhan (2002)

Crespo and Fontoura (2004);Byun and Lee (2005);

ES Scaleeconomies

Emirhan (2002) Celi (1999); Greenawayet al. (1999); Thorpe andZhang (2005);

926 R. PITTIGLIO

of 12 major OECD countries considered over time (t = 1997,. . .,2006). For

each commodity i in a specific industry (j = 1,. . ., 22), the GL index of each

reporter country k with partner country k¢ is defined as follows:

GLk0

kijt ¼ 1�Xk0

kijt �Mk0kijt

������

Xk0kijt þMk0

kijt

� � ; i 2 j; ð1Þ

where Xk0kijt and Mk0

kijt are, respectively, the values of OECD reporter country kexports and imports of commodity i in industry j in a specific year t to and

from country k¢. The GL index can take any value between 0 (complete

inter-industry trade) and 1 (symmetric IIT). More specifically, when

Xk0kijt or Mk0

kijt = 0 and there is no overlap of exports and imports in commod-

ity i in industry j, GLk0kijt is zero. Alternatively, if Xk0

kijt ¼ Mk0kijt and there is

complete matching, then GLk0kijt is unity.

In this paper, the GL index has been calculated at the 6-digit level of har-

monised system (HS) trade classifications (about 5,100 items) to avoid the cate-

gorical aggregation problem.4 Then, following the main literature on the IIT

4 Using trade flows data at the 6-digit level minimises the problem of categorical aggregation thatoccurs when different products are inappropriately considered as varieties of the same product. Inthe empirical analysis, we have used OECD International Trade by Commodities Statistics (ITCS)data.

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AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 927

(see among others Greenaway et al., 1994, 1995; Veeramani, 2002; Crespo and

Fontoura, 2004; Yoshida et al., 2008), we have aggregated the GL indices at

6-digit HS-level data in each 2-digit manufacturing industry j according to the

International Standard Industrial Classification of all economic activities Rev 2

as follows:

GLK 0

kjt ¼P

i2jðXK 0kijt þMK 0

kijtÞ �P

i2j jXK 0kijt �MK 0

kijtjPi2jðXK 0

kijt þMK 0kijtÞ

: ð2Þ

The GL index for an economy k as a whole with the partner country

k0ðGLk0ktÞ, is the weighted average of GLk0

kjt over all industries of the economy:

GLk0

kt ¼X22

j¼1

wk0

kjtGLk0

kjt; ð3Þ

where the weights ðwk0kjtÞ are given by the share of each sector j in the total

trade of each country k¢:

wk0

kjt ¼Xk0

kjt þMk0kjtP22

j¼1 Xk0kjt þMk0

kjt

� � ; ð4Þ

and satisfy the condition:

X22

j¼1

wk0

kjt ¼ 1: ð5Þ

GLk0kt is, therefore, equal to the sum of IIT for the j industries as a percent-

age of the total export plus import trade of the j = 22 industries (Grubel

and Lloyd, 1975; Greenaway and Milner, 1986).5

Following the theoretical approaches, as well as several empirical studies on

the issue discussed in Section 2, total IIT has been decomposed into its two

components of horizontal IIT and vertical IIT by using the so-called product

similarity criterion (Crespo and Fontoura, 2004). Based on the ratio of the unit

value of exports ðUVXk0kijtÞ to the unit value of imports ðUVMk0

kijtÞ, horizontal IIT

is defined to exist for trade in product i in industry j that satisfies the following

criterion:

5 Following the literature on IIT, we use the weighted average rather than simple arithmetic aver-age of industry indices in order to take into account the contribution of each industry ðXk0

kjt þMk0kjtÞ

on total trade ðP22

j¼1ðXk0

kjt þMk0

kjtÞÞ:

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928 R. PITTIGLIO

UVXk0kijt

UVMk0kijt

2 ½1� a; 1þ1�: ð6Þ

The vertical IIT, on the other hand, is defined to exist for trade in product iin industry j that satisfies at least one of the following conditions:

UVXk0kijt

UVMk0kijt

2 ½1þ a;þa�; ð7Þ

or

UVXk0kijt

UVMk0kijt

2 ½0; 1� a�: ð8Þ

In equations (6–8), the parameter a is a dispersion factor, which is somewhat

arbitrarily fixed although the values of 0.15 or 0.25 have been the most widely

employed in the literature. Following most empirical studies on IIT, in this

paper we set a = 0.15. The assumption is that transport and freight costs are

unlikely to account for a difference of any more than 15 per cent in the

export and import unit values. Abd-el-Rahman (1991), Greenaway et al.

(1994, 1995), Dıaz Mora (2002), Gullstrand (2002) and Crespo and Fontoura

(2004) demonstrate that increasing the range from 15 to 25 per cent does not

radically alter the division of trade into horizontally and vertically differenti-

ated products.

For each industry j, for each reporter country k and for each partner country

k¢, therefore we can calculate the following index:

PIITk0

kjt ¼P

i2jðXk0pkjit þMk0p

kjitÞPi2jðXk0

kjit þMk0kjitÞ�P

i2jðXk0pkjit þMk0p

kjitÞ �P

i2j jXk0pkjit �Mk0p

kjitjPi2jðX

k0pkjit þMk0p

kjitÞ: ð9Þ

In equation (9), i refers to the above-defined 6-digit HS products in each

2-digit industry, j is a subscript for the 2-digit industry, k¢ is the partner country

considered, t is the time and p varies according to the nature of trade flows

(horizontal or vertical).

To sum up, the GL index for each industry j and with regard to each partner

country k¢ can be written as follows:

GLk0

kjt ¼ HIITk0

kjt þ VIITk0

kjt; ð10Þ

where HIITk0kjt and VIITk0

kjt denote horizontal and vertical IIT indices,

respectively.

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AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 929

b. Recent Trends in Intra-industry Trade Between OECD Countries

In this section, we present some stylised facts about OECD IIT over the per-

iod 1997 to 2006. It is worth noting that, unless otherwise specified, in all the

tables, we provide the time averages of different indices defined as follows:

GLk0

kj ¼P

t GLk0kjt

T; ð11Þ

PIITk0

kj ¼P

t PIITk0kjt

T; ð12Þ

where T = 10 for the period of analysis (1997 to 2006).

Table 3 provides the averages of bilateral GL indices of different kinds of

two-way trade (IIT, HIIT and VIIT) in manufactured goods. The first informa-

tion that we can obtain from the Table 3 is that Canada, France and Belgium

are the three reporter countries with the highest percentage of IIT. VIIT is

quantitatively more important than HIIT with percentages ranging between 66

and 84 per cent of total IIT for Belgium and Finland, respectively. These data

show the tendency of countries to specialise in the production and export of

the same goods as their partners but with a different quality level.6 The large

share of VIIT is probably a consequence of the international division of

production processes that allows multinational firms to specialise their affiliates

in those stages of the value-added chain within a same industry for which they

are advantaged (Fontagne et al., 2005).

To investigate whether the situation outlined above changes among sectors,

Table 4 shows that the level of OECD IIT varies significantly across industries.

Wood and products of wood and cork, Tobacco products, Food products and

beverages, Leather, leather products and footwear and Pulp, paper and paper

products are sectors for which the area considered reached the lower indices of

IIT and, at the same time, a high share of VIIT.

4. AN EMPIRICAL TEST OF THE HOMOGENEITY HYPOTHESIS

Given the above analysis, in this section we investigate whether the assump-

tion that all sectors are homogeneous holds for OECD IIT. To do this, we esti-

mate the model which is used in the literature to examine the determinants of

6 There is a considerable body of evidence which testifies to the importance of VIIT. Amongothers, Durkin and Krygier (2000) conclude that about 70 per cent of US IIT with OECD countriesis vertically differentiated.

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TABLE 3Intra-industry Trade in the Manufacturing Sector Between OECD Countries

GL HIIT VIIT

Belgium 0.458 34.00 66.00Canada 0.489 30.18 69.82Czech Republic 0.415 23.09 76.91Denmark 0.360 23.01 76.99Spain 0.399 29.28 70.72Finland 0.233 16.04 83.96France 0.483 31.17 68.83Germany 0.414 22.65 77.35Japan 0.276 19.05 80.95United Kingdom 0.437 22.16 77.84Italy 0.376 26.20 73.80Sweden 0.334 19.79 80.21United States 0.361 22.36 77.64OECD 0.387 25.33 74.67

Source: Author’s calculations based on OECD data (percentages of GL for HIIT, VIIT).

TABLE 4Sectoral Distribution of intra-industry trade in the OECD (Average 1997 to 2006)

GL HIIT VIIT GL HIIT VIIT

Food products andbeverages

0.24 32.82 67.18 Other nonmetallicmineral products

0.30 18.42 81.58

Tobacco products 0.21 27.88 72.12 Basic metals 0.34 41.79 58.21Textiles 0.34 24.08 75.92 Fabricated metal products 0.43 19.46 80.54Wearing apparel, dressingand dyeing of fur

0.33 21.52 78.48 Machinery and equipment 0.41 17.19 82.81

Leather, Leatherproducts and footwear

0.27 15.09 84.91 Office, accounting andcomputing machinery

0.41 18.43 81.57

Wood and products ofwood and cork

0.20 20.60 79.40 Electrical machineryand apparatus

0.45 17.31 82.69

Pulp, paper andpaper products

0.29 36.77 63.23 Radio, television andcommunication equipment

0.38 19.03 80.97

Printing and publishing 0.48 14.64 85.36 Medical, precision andoptical instruments

0.43 10.98 89.02

Coke, refined petroleumproducts andnuclear fuel

0.42 41.60 58.40 Motor vehicles, trailersand semi-trailers

0.42 34.07 65.93

Chemicals andchemical products

0.38 23.16 76.84 Other transport equipment 0.44 13.63 86.37

Rubber and plasticsproducts

0.52 30.13 69.87 Manufacturing n.e.c. 0.34 18.19 81.81

Source: Author’s calculations based on OECD data (percentages of GL for HIIT, VIIT).

� 2012 Blackwell Publishing Ltd.

930 R. PITTIGLIO

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 931

IIT by allowing sectors to be heterogeneous and compare the results to those

obtained when sectors are restricted to be homogeneous.

It is worth stressing that the restricted model has formed the basis for empir-

ical investigation in the literature which addressed the question of explaining

the determinants of IIT. The estimation of the unrestricted model is the contri-

bution of this research. In addition, the empirical literature on determinants of

IIT is dominated by cross-sectional analyses which assume that idiosyncratic

differences between country pairs do not change much over time. So, as in the

study of Faustino and Leitao (2007), to capture both cross-sectional and time-

dependent special effects, we use panel data.7 Finally, since the GL index lies

between zero and one, we use its logit transformation as the dependent variable

in the regression equations.8

In the first part of this section, we intend to carry out our empirical investi-

gation by estimating the following unrestricted model:

yk0

kjt ¼ aþ z0k0

kjtbþ dt þ cj þ ek0

kjt: ð13Þ

In this regression equation,

yk0

kjt ¼ ln PIITk0

kjt=1� PIITk0

kjt

� �

where PIITk0kjt is a measure of the vertical or horizontal IIT (constructed as dis-

cussed above) between each reporter country k and its trading partner countries

k¢ and z0k0

kjt is a vector of explanatory variables. For each reporter country k, both

the dependent and explanatory variables vary in three dimensions: time, indus-

try and partner country, t, j and k¢, respectively. cj is the industry fixed effect

and dt is the time fixed effect. Finally, ek0kjt is the stochastic disturbance term

that we assume to be independently distributed (i.i.d.).

As far as the elements of vector z0k0

kjt are concerned, the literature on determi-

nants of IIT has found greater support for country-specific rather than industry-

specific characteristics both for vertically differentiated (by quality) and for

horizontally differentiated (by attributes) goods. As Faustino and Leitao (2007)

noted, estimated coefficients on proxies for product differentiation and scale

economies (ES) have often been insignificant or have had the wrong sign.

Hence, we propose two specifications: the first focusing only on the country-

7 Having data over time for the same cross-section units is useful for the following reasons: first, itallows us to look at dynamic relationships, which it is not possible to do with a single cross-section,and also controls for unobserved cross-section heterogeneity (Wooldridge, 2002).8 About 8 per cent for HIIT and 1 per cent for VIIT are zero in our data set. Yet another problemwith panel data models is the possibility of heteroscedasticity of the residuals as well as correlationsof some forms among country pair residuals. We address this problem by using robust standarderrors (White, 1980).

� 2012 Blackwell Publishing Ltd.

932 R. PITTIGLIO

specific (as opposed to industry-specific) explanatory variables, and the second

also including industry-specific variables such as market structure (MS) and

ES. Our hypotheses are based both on the theoretical models and on some

previous empirical studies that we discussed in Section 2.

In the empirical analysis, we consider the following variables:

Difference in per capita income (DIFYP): defined as the difference in abso-

lute values in GDP per worker between the reporter country k and the partner

country k¢ in industry j, the intensity and the probability of IIT are positively

correlated with the differences in per capita income between trading partners

in the presence of VIIT (Falvey, 1981; Falvey and Kierzkowski, 1987). Vice

versa, the impact on HIIT is found to be negative or neutral (Helpman and

Krugman, 1985).

Market Size (SIZE): measured by the average GDP between reporter country

k and each partner country k¢ in industry j, this variable is considered a proxy

for the size of the market, since economic size influences the volume of trade.

Larger average market size is expected to benefit from the potential economies

of scale in production and trade and, as a result, increases both the variety of

differentiated products and that of different quality products. In this instance,

we would expect to find a positive relationship between the average size of the

market and the share of both VIIT (Falvey and Kierzkowski, 1987) and HIIT

(Lancaster, 1980; Loertsher and Wolter, 1980).

Difference in market size (DIFY): calculated using the absolute difference in

total GDP between country k and its trading partner k¢ in each industry, the DIFY

is traditionally considered to be an obstacle to IIT in similar products. Therefore,

it is expected that the greater the difference, the lower the share of HIIT will be.

Geographical distance (DIST): calculated as the number of kilometres

between the capital cities of the countries considered, the distance between

trading partners is used as a proxy for the costs of information necessary to

trade non-standardised products. According to Balassa and Bauwens (1987),

more information is required on the characteristics of non-standardised products

than those of standardised goods. Therefore, distance has a greater influence on

IIT than on inter-industry trade, because differentiated products have a higher

number of national substitutes than homogeneous goods. IIT (both horizontal

and vertical) is expected to be negatively correlated with the distance between

reporter country k and its trading partner k¢.Market Structure (MS): proxied by the number of firms in industry j of

each country partner k¢, as discussed in the literature, there is no clear rela-

tionship between IIT and the number of firms for both horizontal and vertical

trades. With regard to horizontal IIT, Helpman (1981) argues that markets

characterised by the presence of a large number of firms are more likely to

generate IIT than markets with a smaller number of firms. For vertical IIT,

according to Falvey (1981), market structures with a large number of firms

� 2012 Blackwell Publishing Ltd.

TABLE 5Variable Definitions and Data Sources

Description Source Expected Sign

VIITa HIITa

DIFYP Differences in absolute value in GDP perworker between each reporter country kand partner country k¢ in industry j

OECD: STAN + �

DIFY Differences in absolute value in GDPbetween each reporter country k andpartner country k¢ in industry j

OECD: STAN na �

SIZE Average size calculated as average betweenGDP of country k and partnercountry k¢ GDP in industry j

OECD: STAN + +

DIST Geographical distance in km between thecapital cities of country k and its partner

Jon Haveman’sInternationalTrade Data

� �

MS Market structure defined as the number offirms in each country partner’s (k¢) industry j

OECD: SSIS +=� +

ES Proxy variable for scale economies in eachindustry j calculated as average ofestablishment (net output per establishmentin partner country k¢)

OECD: SSIS +=� �

Note:(i) a VIIT and HIIT have been measured using OECD International Trade by Commodities Statistics (ITCS).

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 933

are more likely to generate vertical IIT owing to the increase in varieties of

different quality. By contrast, Shaked and Sutton (1984) argue that the num-

ber of varieties in quality of the same product may also increase in market

structures with a small number of firms. In conclusion, the impact of market

structure on horizontal IIT is positive, whereas the influence of this variable

on vertical IIT is ambiguous.

Scale Economies (ES): in the literature, a number of methods are pro-

posed to measure ES. In this paper, following the main studies on the topic,

ES have been proxied by the minimum efficient scale (Clark, 1993; Hu and

Ma, 1999; Montout et al., 2002). Helpman and Krugman (1985) and other

authors suggest that ES is a vital determinant for the existence of IIT in

the production of differentiated products within the context of monopolistic

competition, since the existence of increasing returns in production causes

increased specialisation and a fall in production costs. In the absence of ES,

all product varieties could be produced domestically, so no IIT would take

place.

Table 5 summarizes the variables used, their expected signs and statistical

sources.

� 2012 Blackwell Publishing Ltd.

TA

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6)

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� 2012 Blackwell Publishing Ltd.

934 R. PITTIGLIO

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 935

a. The Estimation Results under the Heterogeneity Hypothesis

The regression results that test the hypotheses mentioned under the heteroge-

neity hypothesis are shown in Table 6. As discussed above, in this table we

include two specifications of equation (13). The first considers only country-

specific determinants (V.1 and H.1). The second (V.2 and H.2) also includes

industry-specific determinants.

Before commenting on the table, some remarks are in order. First, in the

econometric specification, we use the fixed-effects approach, as a consequence

of the Hausman test. In this way, we avoid the bias deriving from the existence

of country effects correlated with the explanatory variables, and the within-

group estimator is the only consistent estimator. Second, we know that the

standard error component given by equation (13) assumes that the regression

disturbances have the same variance across time and individuals. This may be

a very restrictive assumption for panel analysis owing to the very high proba-

bility of having error variances specific to the cross-sectional unit – groupwise

heteroscedasticity (Wooldridge, 2002). Therefore, after calculating a modified

Wald test for groupwise heteroscedasticity in the residuals of a fixed-effects

regression model for both of our dependent variables (VIIT and HIIT) and hav-

ing ascertained our heteroscedasticity problems, to ensure validity of our statis-

tical results, we present estimation results obtained by adjusting the standard

errors for heteroscedasticity (Hoechle, 2007).

Examining Table 6, we first observe that the determinants of VIIT (columns

V.1 and V.2) and HIIT (columns H.1 and H.2) are not the same, because the

signs and the significance of variables differ. Moreover, the estimated specifica-

tions for VIIT are more precise than those for HIIT. Looking at the regression

results in column V.1, we also observe that the results obtained for VIIT con-

firm the theoretical expectations and all coefficients are statistically significant.

Therefore, the share of VIIT will grow with market size (SIZE) and with the

level of the partner countries’ per capita income (DIFYP). Furthermore, as

expected, the VIIT will decline with higher costs of transport, proxied by the

DIST between trading partners. These results support the conclusions of neo-

factor proportions models, according to which the differences in factors endow-

ments enhance VIIT. When we embody in the model a number of testable

hypotheses about industry-specific influences on IIT determinants (V.2), we

also note that vertical IIT is inversely related to the number of firms in the

industry. This seems to confirm that vertical IIT may predominate in oligopo-

listic market structures. At the same time, the negative and statistically signifi-

cant sign of ES seems to be consistent with the large numbers model.

Turning to the results of HIIT (columns H.1), we observe that all coefficients

have the expected signs and are statistically significant at 1 per cent level

except for DIFYP. Thus, larger market size increases the opportunity to take

� 2012 Blackwell Publishing Ltd.

936 R. PITTIGLIO

advantage of economies of scale in the production of differentiated products

and thereby offers greater opportunity for trade in horizontally differentiated

products. With regard to the other variables, per capita income differences,

country size differences and distance confirm the theoretical expectations.

When we include industry-specific variables (column H.2) in our specification,

we note on the one hand that the coefficients of country-specific variables do

not notably change and on the other that industry-specific variables are not

significant.9

The results of regressions above presented are partially different from those

obtained in other studies on IIT that have used exclusively country-specific

variables (e.g. with regard to VIIT, Greenaway et al., 1994 for DIFYP in UK;

Blanes and Martın, 2000 for DIFYP in Spain; Greenaway et al., 1999 for

DIFYP in UK; Reganati and Pittiglio, 2005 for DIFYP in Italy; relating to

HIIT, Greenaway et al., 1999 for DIFY in UK; Crespo and Fontoura, 2004 for

DIFYP in Portugal), which do not consider heterogeneity among sectors when

country-specific determinants are examined and heterogeneity among countries

when industry-specific determinants are analysed.

These findings stimulated our curiosity about which is the best way to mea-

sure variables that are to be used in the analysis of IIT. In an attempt to evalu-

ate the relevance of controlling the effects of heterogeneity, we first present

regression results obtained by using the traditional method of measuring coun-

try- and industry-specific variables. Second, the results deriving from both

methods are compared by using the specific econometric tests that are designed

to compare one model with another when different sets of explanatory variables

are used.

The results obtained by estimating equation (13), including variables that do

not consider heterogeneity among sectors when country-specific factors are

analysed and among countries when industry-specific factors are examined,

provide some unexpected signs on some specific variables (Table 7). Focusing

on VIIT, we find, for instance, that the coefficient of DIFYP becomes not sta-

tistically significant, whereas with regard to HIIT, the same variable changes of

sign now becoming positive and statistically significant. Vice versa, the vari-

able MS now becomes significant with a sign contrary to the theoretical expec-

tations. Since we have obtained relevant empirical differences in the results of

our econometric specifications, we would like to formally examine whether one

model is better the other at explaining the determinants of VIIT and HIIT.

To this end, we postulate that there are two competing models that explain

the determinants of IIT. The first (Model S1) assumes that sectors are homoge-

9 In order to evaluate the robustness of the results, we conduct the same regression analysis settingequal to 25 per cent. In this case, we find that the results did not differ significantly. These estima-tion results are provided in the Appendix.

� 2012 Blackwell Publishing Ltd.

TA

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tica

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.1)

(V.2

)(H

.1)

(H.2

)

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E0

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8(7

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)**

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0(4

.36

)**

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0.0

43

(8.5

2)*

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0.0

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(4.5

0)*

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+D

IFY

P1

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6(0

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)3

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0)

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)�

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� 2012 Blackwell Publishing Ltd.

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 937

938 R. PITTIGLIO

neous when country-specific factors are analysed and that countries are homo-

geneous when industry-specific factors are included in the analysis. The second

(Model S2) treats sectors and countries as being heterogeneous. In this way, we

want to test the specification of an econometric model in the presence of

another model that explains the same phenomenon by treating the two hypothe-

ses as non-nested alternatives.

Given these considerations, the two models can be formally written as follows:

Model S1:

yk0

kjt ¼ f ðx0k0kt x0k;j;t;/; pÞ þ e�kjt: ð14Þ

Model S2:

yk0

kjt ¼ f ðz0k0kjtbÞ þ ek0

kjt; ð15Þ

or, alternatively, in explicit form as follows:

Model S1:

yk0

kjt ¼ aþ x0k0

kt /þ x0k;j;t; pþ dt þ cj þ e�kjt: ð16Þ

Model S2:

yk0

kjt ¼ aþ z0k0

kjtbþ dt þ cj þ ek0

kjt; ð17Þ

where yk0kjt is the above-defined measure of vertical or horizontal IIT between

the reporter country k and its trading partner country k¢, x0k 0

kt, x0k;j;t and z0kjt are

three vectors of explanatory variables – the first two in three dimensions (k, k¢,t or k, j, t), the second in four dimensions (k, k¢, j, t), u, p and b are three vec-

tors of parameters to be estimated, and e�kjt and ek0kjt are the correspondent error

terms assumed to be NID(0, r2). Models S1 and S2 are non-nested because one

cannot be derived as a special case of the other. In this paper, we use the

following two tests belonging to the category of model selection tests for test-

ing non-nested hypotheses: J test by Davidson and Mackinnon (1981) and JA

test by Fisher and McAleer (1981).

The J test (Davidson and Mackinnon, 1981) procedure consists of three

steps. In the first step, we estimate the model S2, from which we obtain the

estimated values of dependent variable, yk0S2kjt Then, we estimate the Model S1

with the inclusion of the predicted value yk0S2kjt obtained in Step 1 as an addi-

tional regressor (as in equation 18):

yk0

kjt ¼ aþ x0k0

kt /þ x0k;j;t pþ dt þ cj þ yk0S2kjt hþ e�k

0

kjt: ð18Þ

Finally, we test the hypothesis that h = 0, using the t-test. In this case, if

it is not rejected, it can be accepted that model S1 is the true model

because yk0S2kjt included in equation 18, which represents the influence of vari-

� 2012 Blackwell Publishing Ltd.

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 939

ables not included in model S1, has no additional explanatory power beyond

that contributed by model S1. In other words, model S2 does not contain

any additional information that will improve the performance of model S1.

To control for robustness of this result, we reverse the role of hypotheses

and test:

H0 : Model S2 is true; ð19Þagainst

H1 : Model S1 is true; ð20Þusing the same procedure.

The JA test by Fisher and McAleer (1981) improves upon the J test by mak-

ing the ‘augmented equation’ more robust statistically. In this case, we also

estimate model S1 (equation 16), and from it, we obtain the estimated y values

yk0S1kjt . Then, we estimate an auxiliary regression:

yk0S1kjt ¼ aþ z0k

0

kjtbþ dt þ cj þ ek0

kjt; ð21Þ

from which we obtain the fitted values yk0S1

kjt :Finally, we estimate the augmented S1 model as in equation (22):

yk0

kjt ¼ aþ x0k

t uþ x0k;j;tpþ dt þ cj þ yk0S1

kjt qþ l�k0kjt ; ð22Þ

and test q = 0 using the corresponding t-ratio.

In this case, too, the hypothesis H0: Model S1 is true, cannot be rejected if

q = 0 is not rejected at the chosen confidence level.

Similar to the J test, we reverse the roles of hypotheses and test the hypothesis

H0 : Model S2 is true; ð23Þagainst

H1 : Model S1 is true; ð24Þfollowing the same steps.

As we can see in the left panel of Table 8, when the hypothesis sug-

gested by the existing studies, that is, the model under the homogeneity

hypothesis (model S1) is treated as the null hypothesis (H0: S1 is the cor-rect specification), the J test and JA test indicate that we reject H0. This is

always true both for VIIT and for HIIT with exception of the specification

H.2 (in the JA test). When we test the model S2 under heterogeneity

hypothesis (H0: S2 is the correct specification), in the right panel of the

table, we reject H0 only in column (V.3) for the J test. Therefore, since the

overall conclusion from Table 8, there is stronger support for the validity of

this new approach.

� 2012 Blackwell Publishing Ltd.

TA

BL

E8

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est

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enden

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)(V

.2)

(V.3

)(V

.4)

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st Ho:

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ot

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isth

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� 2012 Blackwell Publishing Ltd.

940 R. PITTIGLIO

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 941

5. SUMMARY AND CONCLUSIONS

The present paper has been motivated by the observation of a gap in the

empirical literature on IIT: the non-consideration of heterogeneity between

sectors when country-specific determinants are examined and the non-consider-

ation of heterogeneity between countries when industry-specific factors are

analysed. To date, in fact the existent empirical literature on IIT considers the

effects of country characteristics on the index of intra-industry specialisation to

be invariant across industries (e.g. market size is considered invariant across

industries) and the effects of industry characteristics on the index of intra-

industry specialisation to be invariant across countries (e.g. market structure is

assumed to be the same independently of partner countries). We believe that

this is a very important aspect since the acceptance of such a hypothesis could

bias the empirical results of analyses on IIT determinants.

In this paper, we have therefore investigated the determinants of OECD IIT

in horizontally and vertically differentiated products by using a data set that

considers the above-mentioned heterogeneity between sectors and between

partner countries.

Our econometric analysis has produced results for both vertical and horizontal

IIT that confirm the theoretical expectations. Therefore, as expected, vertical IIT

with OECD countries will grow with market size and difference in factorial

endowments, whereas it declines with higher costs of transport. On the other hand,

horizontal IIT increases with the similarity between countries and larger market

size, whereas it decreases with differences in country size and distance. When

industry-specific variables are included in our specification, we found that vertical

IIT was inversely related to both the number of firms in the industry and ES,

whereas in horizontal IIT, we did not find any significant relationship between

industry-specific variables and two-way trade in horizontally differentiated goods.

Finally, the tests used to evaluate the validity of our assumptions – J test

(Davidson and Mackinnon, 1981) and JA test (Fisher and McAleer, 1981) –

have provided two main results. First, using the new set of explanatory vari-

ables produces results that are different from the old variables. Second, the

econometric tests allow us to confirm that the new method for measuring

variables is econometrically preferable.

To sum up, the results obtained suggest the importance of checking for dif-

ferences between sectors and countries, which may influence the intensity of

IIT with the well-known ensuing consequences in terms of policy implications.

� 2012 Blackwell Publishing Ltd.

AP

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� 2012 Blackwell Publishing Ltd

942 R. PITTIGLIO

.

AN EMPIRICAL TEST OF THE ‘HOMOGENEITY HYPOTHESIS’ 943

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