The choice of modes of International VerticalSpecialization by French Manufacturing Firms

Liza JABBOUR∗

Abstract

This paper examines the determinants of vertical specialization choices.Vertical specialization is adominant feature of the international economy, it can take place through FDI and intra-firm trade aswell as through arm’s length import strategy.Following the models of Grossman and Helpman (Grossman and Helpman) and of Antras and Help-man (2004), we present a multinomial logit analysis of the economic determinants of vertical special-ization at the firm level.We found that technological intensive inputs are usually traded within firms and that in high tech sec-tors firms prefer integration in the north and that human capital intensity increases northern importsof intermediates.

∗TEAM-University of Paris 1 Panthéon-Sorbonne and CNRS, Maison des Sciences Economiques, 106-112, Bdde L’Hôpital 75013 Paris. ljabbour@univ-paris1.fr

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1 Introduction

Globalization is changing the patterns of international economy. Foreign direct investment

(FDI) and international trade are growing faster than world GDP. Through the FDI and trade,

national economies are getting more linked and interdependent.

The decline of trade barriers and of transportation costs is the most perceptible explanation of

the growing internationalization of economies. However, it seems that this decline explains

only a part of the growth of FDI and trade. In fact many observations, case studies [Feen-

stra (1998) and Hummels et al. (1998)] and empirical analysis [Campa and Goldberg (1997),

Feenstra and Hanson (1999) and Hummels et al. (2001)] indicate that the explanation is the

changing structure of the economic activity toward vertical specialization.

Vertical specialization means the decomposition of the production process into different stages

located in one or different countries. Outsourcing, vertical integration and vertical FDI are all

aspects of the vertical specialization.

Those changing patterns of economies affect different aspects of the economic activity such

as, employment, trade balance, wages and productivity. Especially, vertical specialization on

one hand and the importance of vertical linkages between enterprizes and between coun-

tries on the other hand can enhance the international diffusion of knowledge. In fact, recent

findings of the literature on "international technology transfer" argue that inter-sectors rela-

tionships between enterprizes are more effective for technology spillovers than intra-sector

ones. For example, Kugler (Kugler (2000)) argues that there is a weak theoretical foundation

for econometric studies that analyze spillovers at the intra-sector level. This is because firms

have no interest in diffusing their know-how to their competitors, however the diffusion of

non-specific know-how in upstream sectors, through linkages with suppliers, is less expen-

sive.

Moreover, Javorcik (2004), Blalock and Gertler (2003) and Jabbour and Mucchielli (2004) pro-

vide evidence on the effectiveness of vertical linkages as a channel of knowledge diffusion.

Using firm level data respectively for Lithuania, Indonesia and Spain, the authors have cre-

ated "backward linkages" variables that are extracted from the input-output tables of each

country, and have showed that backward linkages associated with foreign presence positively

affect the productivity of local firms.

The aim of this paper is the study of the choice of vertical specialization. We present an

empirical analysis of the determinants of international vertical specialization. The compre-

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hension of the motivation behind the choice of a certain mode of vertical specialization is

essential for the understanding and the analysis of technology spillovers through the vertical

linkages.

The question of vertical specialization has mainly been addressed by theoretical models like

those of Grossman and Helpman (Grossman and Helpman), Antras (2003) and Antras and

Helpman (2004). In these models, final good producers who are located in the North, have

the choice between sourcing their inputs in the North or in the South and also between out-

sourcing or vertical integration. In Antras (2003) the capital intensity of inputs and the factor

endowments of countries are the main determinants of choosing the mode of vertical spe-

cialization. In Antras and Helpman (2004) the heterogeneity of firms, represented by pro-

ductivity, as well as the intensity of goods in headquarter services determine ,on one hand,

whether the firm will vertically integrate or if it will outsource from an independent supplier

and, on the other hand whether the sourcing will take place in the North or in the South. In,

Grossman and Helpman (Grossman and Helpman) the vertical organization of production is

affected by the heterogeneity of firms, represented by productivity, and by the difference in

managerial incentives.

In this paper, we are inspired by the conclusions of these models to elaborate an empirical

estimation of the elements that affect the vertical organization of production. Particularly, we

are interested in the effect of the elements that may have an influence on the technological

spillovers such as the productivity of firms and the research and development intensity of

inputs. For this purpose, we use data from the "International Intra-group exchanges" sur-

vey and the annual Firm survey "Enquête annuelle d’entreprise"(EAE) realized by the French

Ministry of Industry .

For the analysis of the choice of vertical specialization modes, we estimate a multinomial

logit model and we find that R&D intensity of inputs is a significant determinant of the choice

of vertical integration. We also find that the productivity of firms favors vertical specializa-

tion in the North, mainly integration, but only in the case of high-tech sectors.

The organization of the paper is as follows; The next section presents the data and some

descriptive statistics about Intra-group trade. The third section analyzes the choice of the

mode of vertical specialization. the fourth section presents the the multinomial logit model

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while the fifth one presents the results. Conclusions are presented in the fifth section.

2 Data Description

2.1 Data Presentation

Our analysis is based on a data set extracted from the "International Intra-group exchanges"

survey conducted by the French Ministry of industry. The survey’s objective is the study of

trade within international industrial groups for the year 1999. A group is considered as in-

dustrial when it posses1 at least one industrial firm wherever it is located. And a group is

defined as international when it posses at least one affiliate located outside France.

Thus, the survey is addressed to industrial and commercial firms2 belonging to a group or to

a joint-venture. They should have international trade flows (export+import) superior to one

million euros or five hundred thousand euros when trading with the emerging countries3.

The data set presents a detailed classification of commercial flows. For each firm and for each

transaction it provides the value of the transaction and its sectoral classification. It provides

also; the country of origin (destination) of imports (exports) and the percentage of the trans-

action treated within the group, with a partner and or with a third party. The survey consid-

ers as partnership: technological alliances, licensing agreements, franchise and outsourcing

agreements. Table 1 gives an example4 of the trade data provided by the data set.

The "International Intra-group exchanges" survey was addressed to 8239 firms controlled

by 4032 groups. Only 53% of the firms answered the survey, that is 4397 firms controlled by

2136 groups. Although this rate of respond is relatively weak, the survey covers 82% of trade

flows.

The detailed classification of trade flows, especially imports, allows us to differentiate four

modes of vertical specialization: vertical integration in northern5countries, in southern ones,

1A firm is considered an affiliate of a group when the rate control of the group over this firm is at least equalto 50%

2The sectors concerned by the survey are the following: 101Z-410Z, 511A-517Z, 503A and 504Z except for thesectors 151F, 158D and 158B. The sectors’definition follows the NAF 700 classification.

3The cut off of one million euros reduce the number of firms concerned by the survey (8000 instead of 12000)but has a limited consequence on the commercial flows covered by the survey (96% of the flows are covered).

4The values presented in the table are fictitious. For confidentiality reasons we are not allowed to divulgedata related to the identity of firms or to their activities.

5The distinction between the North and the South follow the World Bank definition of developed and devel-

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Table 1: Trade Data

Firm Nature of Country Sectoral Value Intra Partners Thirdcode the transaction classification parties

1 Exp Belgium 1701 8802 0 0 1001 Exp Spain 2303 8802 75 0 251 Imp Germany 3130 199 15 85 02 Exp United 6110 6774 0 20 80

States2 Imp Italy 6106 4113 0 20 802 Imp Morocco 5210 2192 100 0 0

outsourcing from the North and from the South.

We completed our data with information on the productive activity of firms. This infor-

mation is extracted from the firm annual survey "EAE" conducted by the French Ministry of

Industry. It is exhaustive, obligatory and concerns all firms with more than twenty employ-

ees.

The "EAE" survey provides data on the productive activity of firms such as output, number

of employees, stock of capital, investment and use of intermediates. This data allows us to

estimate the total factor productivity (TFP) of firms and to construct several control variables

such as scale, sector of main activity, capital intensity, human capital intensity and R&D in-

tensity. However, our data covers only manufacturing sectors. The combination of the two

data sets leaves us with 2723 industrial firms.

Our observation unit is the transaction. Each transaction has three dimensions; the firm, the

exporting country and the supplied input. Each firm can import the same input from several

countries and can import several kinds of inputs from a certain country. The combination of

these three dimensions gives almost 900000 transactions.

2.2 Descriptive statistics of Intra-firm trade

The "International Intra-group exchanges" survey focuses on the determinants, motivations

and evolution of intra-group trade. This allows us to present a descriptive analysis of intra-

group trade which is very interesting for a more complete comprehension of the vertical link-

ages.

oping countries

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Concerning the decision to trade within the group or to outsource, the results of the sur-

vey show that, for the majority of the firms, this decision is taken at the group level and not

at the affiliate level.

Table 2: Decision of Intra-group Trade

At which level the decision to trade within the group is taken:At the firm level 32.79%At a higher level within the group 52.45%At another level within the group 4.02%At a joint level 10.74%

Concerning the choice of intra-group trade, the survey presents a series of 22 motivations

that push the firms to prefer intra-group trade. The answers of the firms go from "Perfectly

agree" to "No object". In table 3, we present for each motivation the percentage of firms choos-

ing a possibility of answer. Table 3 shows that the control of the production process plays an

important role in the choice of intra-group trade. In fact, for 63% of the firms, the control

of the quality of production is a motivation to supply within the group. The control of the

marketing strategies and of the after-sale service is a valid argument in favor of intra-group

trade for almost 54% of the firms. Table 3 shows also that another important matter for firms

is organization; 66% of them in the sample prefer intra-group trade in order to reduce organi-

zational costs and 60% of the firms choose internalization in order to be supplied with more

stability and at lower costs.

Those results support the idea that firms, when they choose outsourcing, are more likely to

diversify sources of supply. By establishing relations with more than one supplier, a firm

guarantees a certain stability of supply and, by creating competition between suppliers, it

reduces the costs of supply (Pack and Saggi (2001)).

Table 4 focuses on the structure of intra-group exports while table 5 presents incentives for

intra-group imports.

On the export side, the international market is divided into 10 regions. For each region, the

firms specify if the exported products are similar or complementary to those produced by the

affiliates located in this region lack the capacity of satisfying the local demand.

Moreover, the firms specify if the complementary exports have a higher, equal or lower tech-

nological level than the products of the affiliates in the destined region. The technological

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Table 3: Intra-group Trade Motivations

Perfectly Agree Does not Does not Noagree agree agree object

at allLesser sensibility to conjuncture uncertainties 12.75% 36.20% 12.48% 7.62% 30.95%Lesser sensibility to exchange rates’changes 13.04% 24.11% 14.94% 12% 35.91%Lesser sensibility to raw materials’ prices 7.98% 23.39% 17.91% 13.54% 37.18%Ability to plan exportation in advance 9.53% 30.05% 15.41% 8.84% 36.17%Stability of supply 18.40% 43.36% 7.97% 4.11% 26.17%To increase exportation 17.29% 32.04% 12.49% 5.6% 32.57%To minimize supply’s price 20.44% 38.37% 12.60% 4.37% 24.21%To avoid complicated contracts 16.11% 34.29% 12.05% 5.68% 31.86%Better flexibility to adjust quantities 10.56% 37.25% 19.43% 8.75% 24.01%Better flexibility to adjust prices 15.32% 44.59% 15.04% 5.53% 19.53%To reduce organization costs 20.06% 45.98% 13.96% 4.58% 15.68%Easier financing 21.52% 34.79% 11.34% 5.39% 26.95%To dissociate components productionand assembling 6.07% 16.57% 10.42% 6.40% 60.54%Economy of scale 28.94% 35.09% 4.31% 2.4% 29.26%To easily control the quality of products 22.59% 40.96% 11.14% 4.39% 20.92%To avoid imitation of products 14.20% 24.90% 13.14% 6.9% 41.47%To Maximize the profitability of marketingexpenses 8.65% 23.15% 12.03% 6.88% 49.30%To control marketing strategies 23.40% 35.46% 5.47% 3.14% 32.53%To avoid protectionist barriers 4.88% 18.63% 13.67% 7.88% 54.96%To access a distribution network 8.57% 23.17% 15.38% 9.98% 43.05%To get closer to the clients 23.75% 33.67% 10.64% 5.46% 26.49%To control the quality of after-sale services 18.37% 35.52% 9.82% 4.60% 31.69%

level is based on the R&D and the innovation incorporated in the products. More precisely, a

product is of a higher (equal or lower) technological level if it incorporates more (as much or

less) R&D and innovation than the products in the destined region.

Table 4 shows that for Europe and the NAFTA region, exports are almost equally divided be-

tween similar and complementary products. For the North African region, exports are more

oriented towards similar products. However for the remaining regions, exports are more

oriented toward complementary products. With respect to the technological level, exported

complementary products seem to be similar to the production of the affiliates in the desti-

nation region, except for the African region where more technologically advanced products

represent a significant percentage of the exports.

On the imports’ side, table 5 shows that intra-group imports are not motivated by quality

upgrading. Only 16% of the imports have a higher technological level than the production of

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Table 4: The Structure of Intra-group Exports

Destination of intra-group Nb of firms Nature of products: Technological level:exports Similar complementary Both Higher Similar LowerEuropean Union 1687 54.12% 42.62% 3.26% 22.83% 73.93% 3.24%East Europe 195 46.67% 52.31% 1.03% 34.62% 59.62% 5.77%Occidental Europe(non E.U member) 141 47.52% 49.65% 2.84% 31.08% 67.57% 1.35%North Africa 93 63.44% 35.48% 1.08% 58.82% 38.24% 2.94%Southern and Sub-saharan Africa 46 39.13% 58.70% 2.17% 46.43% 50% 3.57%Near andMiddle-East 43 41.86% 55.81% 2.33% 40% 56% 4%NAFTA 447 48.77% 46.98% 4.25% 29.26% 68.12% 2.62%Southern andCentral America(out of Mexico) 126 40.48% 57.14% 2.38% 45.33% 52% 2.67%Japan 73 42.47% 53.42% 4.11% 19.05% 78.57% 2.38%Asia(out of Japan) 180 41.67% 54.44% 3.89% 35.24% 59.05% 5.71%

the group in France and only 8% have a higher quality. Essentially, intra-group imports are

goods that are not produced by the group in France.

Table 5: Incentives for Intra-group Imports

The firm imports products from firms within the group because Yes NOThe group does not produce the same products in France 81.48% 18.52%The group does not have the necessary capacity in Franceto satisfy the demand 54.13% 45.83%Those imports incorporate higher R&D and innovationthan the goods produced by the group in France 16.11% 83.89%Those imports have higher quality than the goods producedby the group in France 7.77% 92.23%Those imports result from the competition betweenthe affiliates of the group 27.20% 72.80%The firm is a logistical platform for the region 42.86% 57.14%

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3 The Choice of Vertical Specialization Modes: the RelatedLiterature

As mentioned in the introduction, vertical specialization have been the dominant feature of

the globalization of modern economy. The rapid growth of trade in intermediate inputs and

the increasing extent of vertical specialization has been analyzed by some recent case studies

and empirical works. Feenstra (1998) presents the example of the Barbie doll and of Nike.

Both products are designed and marketed by their American firms; however their production

process is disintegrated and outsourced to firms in China, Taiwan, Philippines, South Korea

and other Asian countries.

Hummels et al. (1998) present four case studies: The United States-Canada Auto Agreement,

the Mexican Maquiladoras, the Japan-Asia electronics trade and the Opel’s subsidiary in

Spain. The authors show that, after the United States-Canada Auto agreement, the share

of Canadian vehicles exported to US has raised from 7% to 60% and the share of imported

US cars into the Canadian auto market has raised from 3% to 40%. Nevertheless, a closer

look at the data shows that most of this trade is vertical; Up to 60% of the US auto exports to

Canada are engines and parts while up to 75% of the US auto imports from Canada are cars

and trucks.

For the case of the Mexican maquiladoras, the authors’ calculations indicate a significant

growth of the share of the maquiladoras vertical trade in the global US-Mexico trade (from

20% in the seventies to 39% in 1996).

Using data from the Electronic Industries Association in Japan and from the Japan Electronics

Bureau, the authors find that, between 1985 and 1995, the export share of components and de-

vices has increased while one of consumer and industrial equipments has remained constant.

Finally, the case study of Opel’s subsidiary in Spain shows that Opel España relies greatly on

imported inputs to produce cars exported at a rate of 90%.

More empirical evidences are given by articles such as Campa and Goldberg (1997), Feen-

stra and Hanson (1999) and Hummels et al. (2001).

Campa and Goldberg (1997) analyze the external orientation of manufacturing industries for

the United States, Canada, Japan and the United Kingdom. The authors combine data on

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imports with input-output tables to construct a measure of vertical trade for each industry

in each country. An input-output table provides the share of each industry in the inputs of

other industries. The component input shares are then weighted by the imported share of that

component input industry. Their results show that the use of imported inputs by US manu-

facturing industries is steadily increasing (from 4.8% in 1974 to 8.2% in 1995) and that the

growth of imported inputs in Canadian manufacturing industries has been relatively slow

(from 15.9% to 20.2%) over two decades. For the UK, they show that the use of imported

inputs increased from 13% in 1974 to 22% in 1993, however for the Japanese manufacturing

industries, the results show that the use of the imported inputs has decreased with the time.

Hummels et al. (2001) develop a concept of vertical specialization which measures the im-

ported input use in the production of exported products. The authors use the input-output

tables for OECD and emerging countries to calculate their "vertical specialization" measures.

Their calculations show that, for these countries, vertical specialized exports represent more

than 21% of total exports. Moreover vertical specialization has grown almost 30% since 1970

and this growth accounts for the third of total exports growth.

Vertical specialization is directly related to the "make-or-buy" decision. In fact, specialized

inputs may be provided from arms-length suppliers or produced within the boundaries of the

firm. FDI and international trade give firms the possibility to procure their inputs at home

or abroad in northern countries or in southern ones. The existence of multiple possibilities of

vertical specialization and the heterogonous response of firms when facing the choice of verti-

cal organization, have induced the development of theoretical models aiming to explain these

choices (Grossman and Helpman (Grossman and Helpman), Antras and Helpman (2004)6and

Antras (2003)).

These models share some fundamental hypotheses; First, firms face four choices of vertical

specialization: integration in the North, in the South, outsourcing in the North and in the

South.

Second, outsourcing is regulated by contractual agreements between final good producers

and input suppliers. However, these agreements are characterized by their incompleteness.

In other words, it is assumed that an outside party can not verify the quality of intermediate

inputs. Hence, enforceable contracts specifying the quality and the price of the intermediate

6Those two models consider the choice of a Northern firm between two locations, the home country and theSouthern country. However they can easily be adapted to a choice between a Northern country, different fromthe home country, and a southern country since they do not take account of trade costs.

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input, can not be signed between the two parties. In the presence of enforceable contracts,

suppliers would have an incentive to produce a low quality input. Moreover, the contract

incompleteness creates a potential holdup problem (?, Hart (1995)). If the supplier produces

a specialized input tailored to the specific needs of the final good producer and that has no

value for other firms, the final good producer can impose low prices upon ex post nego-

tiation. Foreseeing this outcome, the supplier will be motivated to undertake suboptimal

investments. Contract incompleteness is not exclusive to arms-length relationships, but the

final-good producer have more leverage under vertical integration. In Grossman and Help-

man (Grossman and Helpman), the final good producer can monitor only a fraction δ of the

integrated supplier’s effort, while in Antras and Helpman (2004) and Antras (2003), the prin-

cipal 7 have the possibility to fire the integrated supplier and seize the input if the two parties

fail to reach an agreement. However he can only seize a fraction δ of the input 8. Under out-

sourcing, if the two parties fail to reach an agreement they are both left with no income.

Third, one important element affecting the organizational choice of the firm is the final-good

and the factor intensity of inputs. In Antras and Helpman (2004), final-goods require two

specific inputs, headquarter services and manufactured components. The former can only be

produced in the North while the later may be produced in both locations. In Antras (2003)

intermediate inputs require capital and labor. However, cost sharing between the two parties

is only related to the investment in capital formation. In fact, factor intensity affect the pay-

offs of the two parties. The higher the headquarter services intensity is, the higher will be the

payoff received by the principal.

To summarize, firms face several tradeoffs: production costs, represented by wages, are

higher in the North while fixed organizational costs are lower in the North regardless of the

ownership structure because of the geographical proximity. Nevertheless, when the North-

ern country is different from the home country we can assume that organizational costs are

lower is the North because of technological proximity between firms. Moreover, organiza-

tional costs are higher under integration regardless of the location (?)9. Finally, the bargaining

power of the principal is higher under integration. Given the choice of vertical integration,

this bargaining power is higher in the North. The managerial incentives given to the compo-

nent’s supplier are also different under the alternative organizational forms.7The final good producer8In both cases, δ is higher in the North than in the South, reflecting better legal protection in the North9? argued that production under vertical integration may entail greater governance costs due to bureaucratic

distortions.

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Concluding, Antras (2003) shows that in capital incentive sectors, firms prefer integration

over outsourcing. He also shows that the higher the capital to labor ratio of the exporting

country is, the higher the intra- firms imports is. Using data on intra-firm imports by US

firms from 28 countries for the years 1987, 1989, 1992 and 1994, Antras (2003) brought econo-

metric evidence confirming the theocratical conclusions of the model.

The choice of organizational form in the Antras and Helpman (2004) model depends on

firm’s productivity and on the factor intensity of the final-good. In fact, in component-

intensive sectors, vertical integration is not optimal. Thus firms always choose to outsource;

low-productive firms outsource in the North while high-productive ones outsource in the

South. In headquarter-intensive sectors, both integration and outsourcing are profitable but

the choice of the organizational form depend on productivity. Highly productive firms ac-

quire their components in the South and the low productive ones acquire their components

in the North. And in each group, the most productive firms integrate while the others out-

source. Regardless of the sectors, less productive firms outsource in the North while highly

productive ones import their inputs from the South.

In Grossman and Helpman (Grossman and Helpman), the choice of vertical specialization

modes is determined by the productivity of firms. They show that outsourcing in the North

is never optimal. Least productive firms and the most productive ones outsource in the South.

Firms with intermediate levels of productivity choose vertical integration, among these the

more productive ones integrate in the North while the less productive integrate in the South.

4 Methodology: The multinomial logit model

The objective of our empirical estimation is to determine which are the elements that affect the

choice of vertical specialization and in which way. More precisely, we aim to verify the impact

of productivity and of factor intensity of both final goods and inputs on organizational forms

and on the location of production. The most appropriate econometric model to examine those

impacts seems to be a discrete choice model. Each firm faces four different choices and since

the data used in this study encompasses these choices, a multinomial logit model seems to be

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the most suitable for our study.

The model of vertical specialization choice is :

Prob(Yi = j) = Pij =eβ′

jxi∑3k=0 eβ′

jxij = 0, 1, ...j

Pij is the probability that the dependant variable, which is the choice of vertical specializa-

tion, takes the value j at the ith observation. As mentioned earlier, j can be vertical integration

in the North (j=0), vertical integration in the South (j=1), outsourcing in the North (j=2) and

outsourcing in the South (j=3). xi is a vector of explanatory variables at the firm, the sector

and the country level.

However, the model is unidentified in the sense that there is more than one solution to β0,

β1, β2 and β3 that lead to the same probabilities for y0,...y3. The identification of the model

imposes that one of the choices is defined as a base group and its β is set to zero.Thus the re-

maining coefficients would measure the relative change with respect to the base group. Since

in the Antras and Helpman (2004) model outsourcing in the North is chosen by least produc-

tive firms and in the Grossman and Helpman (Grossman and Helpman) model outsourcing

is never profitable, we set outsourcing in the North as our base group. Thus we have:

Prob(Yi = j)

Prob(Yi = 2)= eβ′

jxi

In other words, we interpret the point estimates of the multinomial logit as changes in the

probability of a choice with respect to the base group, these changes are induced by a change

in the explanatory variables. This means that the choice j will be more or less likely relative

to the base group.

As explanatory variables we use:

• Total factor productivity (TFP); this is a firm level variable which is measured by using

the semiparametric estimation proposed by Olley and Pakes (Olley and Pakes (1996)).

The purpose of the O&P methodology is to overcome the selection and simultaneity

problems faced by the econometrician when estimating productivity.

The selection problem is related to the exit decision of firms. In fact, firms base their

exit decision on their expectation of future productivity and profitability thus making

this decision endogenous regarding productivity. The simultaneity problem is related

to the choice of inputs by firms. More precisely, productivity shocks are unobservable

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for the econometrician but are known to the firm and taken in account in the production

process thus making it more appropriate to consider inputs as endogenous variables

(Marschak and Andrews (1944), Griliches and Mairesse (1995)).

The semiparametric estimation is based on a dynamic model of firm behaviour. The

model assumes that some inputs, like labor and intermediates, will immediately adjust

to the productivity shocks while others, especially capital, need a certain lag of time

to adjust. The model also assume that investment is strictly increasing in productivity

shocks (Pakes (1994)).Thus, it uses investment as proxy for productivity shocks and

generates an exit rule to correct the simultaneity and selection bias.

• R&D intensity (firm activity level); this variable is measured as the ratio of research and

development expenditures to total production10 at the sectoral level. In this variable, the

sectoral classification is related to the main activity of firms. It tends to capture the effect

of the technological level of firms’activity on their vertical specialization strategies.R&D

expenditures at the sectoral level are taken from the OECD Science and Technology

Statistics database and total production is calculated using the "EAE" data set.

• Capital intensity at the input level; this variable is measured as the ratio of capital stocks

to total employment at the sectoral level. Sectors in this variable are related to the clas-

sification of the inputs and not to the activity of the firm. Capital stocks and total em-

ployment are calculated using the "EAE" data set.

• R&D intensity at the input level; this variable is measured as the ratio of research and

development expenditures to total production at the sectoral level.Similirarly to the pre-

vious variable, the sectoral classification is related to the input supplied.

The R&D intensity of inputs may affect the choice of vertical specialization in several

ways. In fact the theoretical models cited above consider that the input can be produced

in both locations. Nevertheless, it is very plausible to assume that southern suppliers

may not possess the necessary technological capacities to produce all kinds of inputs

especially high-tech ones.

Moreover, R&D intensity is an indicator of the knowledge embodied in the input. In the

presence of knowledge transfer between buyers and suppliers some firms may prefer to

source high-tech inputs within their boundaries in order to protect their knowledge and

10We also measured R&D intensity as the ratio of R&D expenditures to value added but the results are verysimilar across those two measures

14

some others prefer to outsource in order to have access to the supplier’s technology and

know-how. Similarly to the the first R&D variable the R&D expenditures are taken from

the OECD Science and Technology Statistics database and total production is calculated

using the "EAE" data set.

• Human capital intensity at the input level; this variable is measured as the ratio of R&D

personnel to total employment at the sectoral level. As to the previous variable, human

capital intensity is related to the capacity of countries to produce high-tech inputs. Data

on R&D personnel are from the OECD Science and Technology Statistics database.

• Real wages at the country level, this variable is measured as the natural logarithm of

the ratio of wages to value added by employee in each of exporting country. Data on

wages and value added are from the United Nations Industrial development organiza-

tion (UNIDO) statistical database. Real wages represent variable costs of production in

each country. We take account of the productivity of labor to have a more precise esti-

mation of the difference of production costs between countries. The availability of data

on wages and value added limits our analysis to imports from 81 countries.

• Control variables at the country level. As control variables we use the natural logarithm

of the distance between France and each of the exporting countries. This distance vari-

able is a weighted measure using city-level data to assess the geographic distribution

of the population inside each country. The calculation of distance is based on bilateral

distances between the biggest cities of the two countries. Those inter-city distances are

weighted by the share of the city in the overall population.

We also use a dummy variable taking the value of one if the exporter country is a mem-

ber of the European Union and otherwise zero and another dummy variable taking the

value of one if France and the exporting country have a free trade agreement and other-

wise zero.

Moreover we use a contiguity variable and two common languages dummies, the first

one indicates if the French language is an official language of the exporting country and

the other indicates if a language is spoken by at least 9% of the population in France and

in the exporting country.

The aim of those variables is to control for the geographical factors that influence trade

between countries and the choice of trade partners. They may not have a impact on the

choice of organization modes (integration vs outsourcing) but will affect the choice of

locations (North vs South). More precisely, France is a member of the European Union,15

whose members are considered as northern countries, and it shares borders with north-

ern countries. Thus, imports of France may be biased towards northern countries re-

gardless of the other factors that we wish to focus on such as the productivity of firms

and the technological intensity of products. Control variables are from the CEPII’s (Cen-

tre d’Etudes Prospectives et d’Informations Internationales) distance database.

5 Results

A first look at the data shows that imports of intermediates by French firms are mostly orig-

inated from northern countries, and within a certain location, outsourcing is more frequent

than vertical integration. In fact, outsourcing in the North represents 63.26% of imports in our

sample, integration in the North represents 29.9%, outsourcing in the South represents 4.7%

and integration in the South represents 2.14%.

Looking at the determinants of the choice of vertical specialization, table 6 shows that

more productive firms favors outsourcing regardless of the location. In fact, the coefficient on

TFP is negative and significant both in the case of integration in the North and in the South.

This means that an increase of TFP reduces the probability to choose integration in compari-

son to outsourcing in the North. Moreover, the coefficient on TFP is non-significant in the case

of outsourcing in the South, which means that TFP is not a determining factor of the location

choice of outsourcing.

Capital intensity of inputs seems to favor imports from the South regardless of the organi-

zational form, while human capital intensity favors imports from the North. Moreover, be-

tween northern integration and northern outsourcing, human capital intensity increases the

probability of integration. The impact of R&D intensity of inputs is more related to the or-

ganizational form of production than to its location. In fact, it increases the probability of

integration in both locations over outsourcing. The inputs intensity in human capital and in

R&D are an indicator of the Knowledge content of inputs. If they favor production in the

North it is probably because the southern countries do not have the technological capacity to

produce high-tech inputs. And if they favor integration it is probably because firms prefer to

protect their knowledge by keeping the production of high-tech inputs within their bound-

aries.

The technological level of firms’activity, represented by the R&D intensity (at the firm’s activ-

16

ity level) variable, seams to encourage production in the North, especially integration.

Since most of the countries related to France by a free trade agreement are developing ones,

the FTA variable favors imports from the south. The distance variable favors integration in

the North over outsourcing in the North and over imports from the South. The meaning of

this result is twofold; first, it reflects the fact that France is geographically closer to Northern

countries than to southern ones. Second it indicates that the distance between two countries

increases the probability of the presence of an affiliate of the firm in the exporting country.

The fact that FTA agreements favors outsourcing in the North over integration is the North

is probably related to the idea that FTA agreements reduces the probability of the presence of

an affiliate in the exporting country.

In an unreported result, we have run a multinomial logit estimation and set the base group as

integration in the south. We found that the productivity of firms as well as the human capital

and R&D intensities of inputs increase the probability of northern integration over southern

one.

The results in table 6 are from the entire sample and they concern all manufacturing sec-

tors. As mentioned earlier, Antras and Helpman (2004) differentiate in their analysis between

component intensive sectors and headquarter intensive sectors. We associate headquarter

services with R&D activity at the final good level and consider headquarter intensive sectors

as R&D intensive ones 11. We thus split the sample in two sub samples: sectors with a R&D

intensity above the mean are considered intensive in headquarter services and the others are

considered as component intensive.

Table 7 presents the results for the headquarter intensive sectors. The main difference with

the results from the entire sample is that in these sectors, productivity seems to favor vertical

specialization in the North and especially integration.

Component intensive sectors, as shown in table 8, seem to follow the general trend of manu-

facturing sectors. However, the R&D intensity of inputs is no longer a strategic determinant

of organizational forms. It favors imports from the North but not intra-firm imports.

The choice of vertical specialization modes seams to be closely related to the knowledge pro-

tection strategy of firms. In high tech sectors, more productive firms associated with innova-

tive ones or technologically advanced ones, prefer vertical integration. In low tech sectors this

is not valid; but at the same time we can assume that in those sectors firms are less innovative11This differentiation concern the main activity of the firm and not the sectoral classification of inputs

17

and that their competition strategy is less based on technological leadership.

The results that we have presented in the previous tables must be interpreted with cau-

tion. In fact, this analysis does not take into account local vertical specialization, e.g. vertical

integration and outsourcing within France and thus might underestimate the extent of north-

ern vertical specialization.

Moreover, our analysis is unable to incorporate the choice of no vertical specialization. the

data set we have does not offer information on when the firm decide not to outsource or to

vertically integrate.

A more complete analysis of vertical specialization need to incorporate the strategic decision

of the firm to vertically integrate or to outsource at home or abroad or simply not to vertically

integrate. Given these shortcomings, the results of the empirical analysis are related to the

choice of modes of offshoring, given that the decision to offshore has been made by the firm.

18

Tabl

e6:

The

Cho

ice

ofve

rtic

alSp

ecia

lizat

ion

Mod

esVe

rtic

alsp

ecia

lizat

ion

Inte

grat

ion

Nor

thIn

tegr

atio

nSo

uth

Out

sour

cing

Sout

hR

eg1

Reg

2R

eg3

Reg

4R

eg1

Reg

2R

eg3

Reg

4R

eg1

Reg

2R

eg3

Reg

4TF

P-0

.067

∗-0

.087

∗-0

.084

∗-0

.087

∗-0

.257

∗-0

.269

∗-0

.269

∗-0

.257

∗-0

.043

-0.0

19-0

.044

-0.0

17(0

.010

7)(0

.010

8)(0

.010

8)(0

.010

8)(0

.046

)(0

.047

)(0

.047

)(0

.045

)(0

.047

)(0

.047

)(0

.046

)(0

.046

)C

apit

alin

tens

ity

-8.2

7e-0

6-0

.000

04∗∗

∗0.

0000

4∗∗∗

4.54

e-06

0.00

018∗

∗0.

0001

6∗∗∗

0.00

02∗∗

0.00

025∗

0.00

010.

0001

7∗∗

0.00

011

0.00

023∗

(0.0

0002

4)(0

.000

02)

(0.0

0002

)(0

.000

02)

(0.0

0008

)(0

.000

08)

(0.0

0008

)(0

.000

09)

(0.0

0007

)(0

.000

07)

(0.0

0007

)(0

.000

07)

Hum

anca

pita

lint

ensi

ty4.

8∗2.

53∗

1.28

-5.0

2∗.0

0017

∗∗-9

.7∗

(0.2

4)(0

.407

)(1

.26)

(1.8

3)(0

.000

07)

(1.7

1)R

&D

inte

nsit

y5.

10∗

2.72

∗1.

93∗

1.98

∗0.

782

-0.3

66-3

.77∗

∗-3

.84∗

∗-1

1.44

∗-7

.6∗

-9.0

2∗-9

.02∗

(Fir

mLe

vel)

(0.2

5)(0

.28)

(0.2

9)(0

.29)

(1.1

4)(1

.39)

(1.5

9)(1

.59)

(1.0

2)(1

.22)

(1.3

6)(1

.37)

R&

Din

tens

ity

6.26

∗3.

71∗

6.33

∗11

.05∗

-2.9∗∗

6.06

∗

(Inp

utle

vel)

(0.3

1)(0

.51)

(1.6

2)(2

.32)

(1.4

)(2

.1)

Rea

lwag

es-0

.074

∗∗-0

.073

∗∗-0

.072

∗∗-0

.072

∗∗-3

.82∗

-3.8

6∗-3

.82∗

-3.8

2∗-3

.6∗

-3.6

6∗-3

.63∗

-3.6

3∗

(0.0

34)

(0.0

34)

(0.0

34)

(0.0

34)

(0.1

7)(0

.17)

(0.1

7)(0

.17)

(0.1

5)(0

.15)

(0.1

5)(0

.15)

Dis

tanc

e0.

057∗

0.05

8∗0.

05∗∗

0.05

4∗-3

.67∗

-3.6

7∗-3

.68∗

-3.6

7∗-3

.5∗

-3.5∗

-3.5∗

-3.5∗

(0.0

2)(0

.02)

(0.0

2)(0

.02)

(0.0

74)

(0.0

74)

(0.0

74)

(0.0

74)

(0.0

64)

(0.0

65)

(0.0

65)

(0.0

65)

Con

tigu

ity

-0.1

99∗

-0.1

88∗

-0.1

89∗

-0.1

88(0

.018

)(0

.018

)(0

.018

)(0

.018

)C

omm

onla

ngua

ge-0

.074

-0.0

67-0

.068

-0.0

67-2

4.7∗

-24.

6∗-2

4.6∗

-24.

6∗

(0.0

84)

(0.0

84)

(0.0

84)

(0.0

84)

(0.2

6)(0

.26)

(0.2

6)(0

.26)

Com

mon

lang

uage

(9%

)0.

770.

073

0.07

40.

073

24.6

7∗24

.68∗

24.6

4∗24

.67∗

25.7

8∗25

.9∗

25.8

1∗25

.89∗

(0.0

85)

(0.0

86)

(0.0

86)

(0.0

86)

(0.2

6)(0

.307

)(0

.309

)(0

.307

)(0

.208

)(0

.21)

(0.2

09)

(0.2

1)EU

-0.0

012

0.02

40.

016

0.02

2(0

.04)

(0.0

4)(0

.04)

(0.0

4)FT

A-1

.036

∗-1

.035

∗-1

.04∗

-1.0

4∗5.

85∗

5.84

∗5.

84∗

5.76

∗5.

77∗

5.77

∗5.

73∗

5.75

∗

(0.1

3)(0

.13)

(0.1

3)(0

.13)

(0.1

09)

(0.1

09)

(0.1

09)

(0.1

09)

(0.0

94)

(0.0

95)

(0.0

95)

(0.0

95)

Nb

ofO

bser

vati

ons

9035

890

358

9035

890

358

9035

890

358

9035

890

358

9035

890

358

9035

890

358

Log

Like

lihoo

d-5

9840

.55

-596

11.6

45-5

9616

.699

-595

77.0

5-5

9840

.55

-596

11.6

45-5

9616

.699

-595

77.0

5-5

9840

.55

-596

11.6

45-5

9616

.699

-595

77.0

5∗

indi

cate

sco

effic

ient

sar

esi

gnifi

cant

atth

e1%

leve

l∗∗

indi

cate

sco

effic

ient

sar

esi

gnifi

cant

atth

e5%

leve

l∗∗

∗in

dica

tes

coef

ficie

nts

are

sign

ifica

ntat

the

10%

leve

l

19

Table 7: Headquarter Intensive Sectors

Vertical specialization Integration North Integration South outsourcing SouthTFP 0.024∗∗∗ -0.267∗ -0.075

(0.014) (0.05) (0.056)Capital Intensity -0.0003∗ -0.0001 0.0001

(0.00006) (0.0003) (0.0002)Human Capital 1.91 ∗ -8.39 ∗ -8.85 ∗

Intensity (0.513) (2.34) (2.22)R&D Intensity 8.54∗ 17.17 ∗ 10.5 ∗

(0.623) (2.81) (2.62)Real Wages -0.995∗∗∗ -5.14 ∗ -5.02∗

(0.05) (0.32) (0.31)Distance -0.121 -4.01∗ -3.81 ∗

(0.03) (0.122) (0.115)Contiguity -0.161 ∗

(0.031)Common language -0.05 -27.98 ∗

(0.174) (0.401)Common language -0.05 26.43 ∗ 28.17 ∗

(9%) (0.177) (0.37) (0.49)EU 0.15 ∗∗ -56.84

(0.064)FTA -0.808∗ 6.56 ∗ 6.6 ∗

(0.187) (0.168) (0.155)Nb of Observations 32269 32269 32269Log Likelihood -22316.93 -22316.93 -22316.93

20

Table 8: Component Intensive Sectors

Vertical specialization Integration North Integration South Outsourcing SouthTFP -0.261∗ -0.25∗ -0.033

(0.016) (0.091) (0.075)Capital Intensity 0.00007∗∗ 0.00005 0.00017∗∗∗

(0.00002) (0.00011) (0.00009)Human Capital 4.83 ∗ 9.01 ∗∗ -4.59Intensity (0.70) (3.8) (3.47)R&D Intensity -3.42∗ -18.1 ∗ -20.15 ∗

(0.938) (4.99) (4.52)Real Wages -0.035 -3.15 ∗ -2.98∗

(0.047) (0.209) (0.180)Distance -0.133∗ -3.44∗ -3.3 ∗

(0.027) (0.098) (0.081)Contiguity -0.173 ∗

(0.022)Common language -0.047 -23.56∗ -24.34 ∗

(0.097) (0.272) (0.402)Common language 0.12 24.05∗∗∗

(9%) (0.099) (0.311)EU -0.11 ∗∗

(0.053)FTA -1.3∗ 5.003 ∗ 4.86 ∗

(0.184) (0.148) (0.121)Nb of Observations 58089 58089 58089Log Likelihood -36883.14 -36883.14 -36883.14

6 Conclusion

In this paper, we present an analysis of factors determining the choice of international vertical

specialization modes.

We have based our empirical analysis of the vertical specialization’s strategies on theocrat-

ical models by Grossman and Helpman (Grossman and Helpman), Antras (2003) and Antras

and Helpman (2004).

Using a detailed trade data, by country and product for French firms we have defined four

modes of vertical specialization; northern and southern vertical integration and northern and

southern outsourcing. We implemented a multinomial logit model to estimate the impact of

productivity, technological intensity of final products and inputs, capital intensity and human

21

capital intensity on the form and location of vertical specialization.

We found that technological intensive inputs are usually traded within firms and that in high

tech sectors firms prefer integration in the north and that human capital intensity increases

northern imports of intermediates.

These findings seem to indicate a knowledge protection strategy by the firms. Technolog-

ically advanced ones prefer to limit the diffusion of their knowledge by choosing vertical

integration. Technological intensive inputs that might imply larger knowledge sharing be-

tween firms and supplier are imported within firms.

To our knowledge, we are the first to study and to empirically analyze the determinants

of vertical specialization, especially using a detailed trade data base at the firm level. In our

opinion, such analysis is necessary to complete theocratical works on vertical specialization

and to help the understanding of this recent phenomenon which is changing the patterns

of international economy. However, our study do not take into account vertical specializa-

tion within the country neither the choice of no vertical specialization. More comprehensive

studies are, thus, needed to present a more global and clearer image of the reality of vertical

specialization.

22

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