# How do Firms Agglomerate? A Study of FDI in France

Matthieu Crozet, Thierry Mayer, Jean-Louis Mucchielli

► To cite this version:

Matthieu Crozet, Thierry Mayer, Jean-Louis Mucchielli. How do Firms Agglomerate? A Study of FDI in France. 2000. halshs-03718427

HAL Id: halshs-03718427

https://shs.hal.science/halshs-03718427

Submitted on 8 Jul 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

UNIVERSITÉ PARIS 1
-PANTHÉON - SORBONNE -

<figure><img src="image_3.png" /></figure>

Cahiers
de la
MSE

How do Firms Agglomerate?
A Study of FDI in France

Matthieu CROZET

Thierry MAYER

Jean-Louis MUCCHIELLI

2000.50

CENTRE NATIONAL
DE LA RECHERCHE
SCIENTIFIQUE

Maison des Sciences Économiques, 106-112 boulevard de L'Hôpital, 75647 Paris Cedex 13

ISSN : 1624-0340
# How do Firms Agglomerate?

## A Study of FDI in France *

Matthieu Crozet†

Thierry Mayer‡

Jean-Louis Mucchielli §

April 20, 2000

### Abstract

This paper studies the determinants of location choice by foreign investors in France. A new sample of almost 4000 location choices over 10 years and 92 administrative locations is used to measure two important issues: To what extent do foreign investors cluster spatially and are the decision makers sensitive to investment incentives? Concerning the agglomeration effects, we find very strong evidence of positive spillovers between firms and identify the countries of origin and the industries for which those spillovers are the most substantial. Concerning promotion policies, we find very little evidence of any positive impact of either the national or European regional policy on location choices. Finally, we identify for some European countries a “learning process” of foreign direct investment, the location decisions gradually becoming more remote from the country of origin during the period we study.

JEL classification: F23, R3, R58

Keywords: Industrial location, conditional logit, FDI, regional policy.

<footnote>*This research benefited from financial assistance of L'Istituto Affari Internazionali (Rome) and the Commissionat Général du Plan (Paris, convention 4-98)</footnote>

<footnote>†TEAM, University of Paris I and CNRS, 106-112 Bd de l'hopital 75647 PARIS CEDEX 13, France. Email: crozet@univ-paris1.fr</footnote>

<footnote>‡Corresponding author: TEAM, University of Paris I and CNRS, 106-112 Bd de l'hopital 75647 PARIS CEDEX 13, France. Email: tmayer@univ-paris1.fr</footnote>

<footnote>§TEAM, University of Paris I and CNRS, 106-112 Bd de l'hopital 75647 PARIS CEDEX 13, France. Email: jlmuc@univ-paris1.fr</footnote>
# 1 Introduction

Promotion policies are largely used by local, national or European authorities in order to try and influence the location of economic activity in favor of lagging regions. Public authorities are particularly anxious to attract foreign investors often thought to be source of productivity increases by local firms in addition to creating new jobs. Related to these practices, is the now well documented finding that firms cluster in space (see Henderson and Kuncoro, 1996; Ferrer, 1998; Devereux and Griffith, 1998; Mayer and Mucchielli, 1999; Head et al., 1995 and 1999, for recent results on that topic). The decision makers implementing promotion and regional policies often have this idea in mind that being successful in attracting a first critical set of firms will generate a virtuous mechanism of self-reinforcing agglomeration of firms in the territory targeted.

This paper analyzes location choices of foreign investors in France. It is particularly focused on the measurement of agglomeration effects –the positive influence of a firm’s location choice on the probability that the subsequent firms make the same choice– and the identification of the effect of promotion policies used by local authorities to attract firms.

The existing empirical literature on agglomeration effects has mainly consisted of national level studies (Wheeler and Mody, 1992, Devereux and Griffith, 1998) or choices of states in the USA (Carlton, 1983, Friedman et al. 1992, Head et al. 1995, 1999). The preceding work on location choices in Europe at a lower geographical level than countries has considered choices of regions by foreign investors on the European territory (Ferrer, 1998). Mayer and Mucchielli (1999) considered in an integrated way the national and regional choices of Japanese investors in Europe. Some papers considered location problems at a very thin geographical level for Brazil (Hansen, 1987), China (Head and Ries, 1996) or Indonesia (Henderson and Kuncoro, 1996). However, nobody has yet to our knowledge studied the determinants of location choice at a
very detailed level for a European country that would be a major receiver of FDI (see however
Guimarães et al., 2000 for a very thin analysis of location choice of FDI within Portugal). This
paper is the first to consider individual firms' decisions for over 90 territorial units in France.

One of our objectives is to study in more detail the characteristics of agglomeration effects.
Due to a very large dataset, we are in particular able to differentiate the determinants of location
depending on the country of origin of the investor and the industry of the plant located in France.
Our results give insights for the following questions that are likely be important for the policy
maker: Does the nationality of the firms matter for agglomeration effects? Do firms cluster more
with other firms from the same country? Do firms show a preference for regions within France
that are near to their home market? Do all industries exhibit the same pattern of geographic
concentration? We show that the nationality of the investor is of crucial importance in location
patterns. There are certain countries sharing a border with France for which investors show a
very strong tendency to locate near their country of origin. However this tendency is declining
over time which suggests some learning process in the difficulty of operating a plant abroad.
We also observe very different behaviours depending on industries. Concerning agglomeration
effects, it is shown that firms cluster with their competitors but that this behaviour depends on
the nationality of both the competitors and the investor.

We then analyze the effect of both French and European regional policies on location choice.
We show that foreign investors are, to a large extent, *not* sensitive to investment incentives. Even
when promotion policies appear to have a statistically significant positive effect, the magnitude
of this effects is very low compared to other determinants.

The remainder of the paper is as follows: Section 2 presents the theoretical determinants
of location choice using a very simple oligopolistic competition model that yields an estimable
equation for the location choice of firms. Section 3 describes the econometric model and the
data used. Section 4 presents results on general and industry level location patterns, focusing
on agglomeration effects. Section 5 presents results on the impact of promotion policies and
section 6 concludes.

2 Theoretical foundations

Location choice determinants can be broadly classified in four large groups: Demand that can be expected on the chosen location, factor costs that would be faced, the number of “local” and foreign firms active in that same location and public policies designed to attract firms.

The sign expected on the two first variables is very widely accepted to be respectively posi-
tive and negative. The influence of the number of firms is more complicated and results from a
trade off between agglomeration and dispersion forces. On the one hand, location theory gen-
erally finds that geographic distance isolates firms from competition. A rise in the number of
firms in a given location shifts prices down in that location and therefore decreases incentives to
locate there. On the other hand, positive externalities between firms can emerge from techno-
logical spillovers or other mechanisms recently celebrated by the economic geography literature
(Krugman, 1991, Venables 1996). These forces contribute to the geographical clustering of firms
and the sign of this determinant will therefore depend on the relative strength of the two forces.

Investment promotion policies can take various forms: job creation subsidy, temporary ex-
emption from local taxes, low levels of corporate taxation... Everything else equal, firms should
be positively influenced by this determinant in their choice.

All those determinants can be synthetized in a very simple formal framework yielding an
estimable equation describing the profitability of choosing a particular location. Suppose firms
use quantity as their strategic variable and let demand take a generalized Cobb Douglas form
where price and income elasticities can differ from 1:
$$Q = \frac{\alpha m^{\gamma}}{p^{\beta}}. \qquad (1)$$

$\alpha$ is the share of income spent on the particular good considered, $m$ is income (exogenous here) of consumers, $\beta$ is price elasticity and $\gamma$ income elasticity. $Q$ is total quantity demanded at price $p$, this demand being satisfied by locally producing firms<fnref n="1" />. Suppose that firms have identical production costs when producing in the same location, ensuring the individual quantities will be equal in equilibrium: $Q = Nq$, $N$ being the number of active firms on the market. After tax profits of a representative firm willing to produce and sell at this location is:

$$\pi = (1-t)(p-c)q, \qquad (2)$$

where $t$ is the corporate tax rate (representing promotion policies in a large sense) and $c$ is a unit production cost function. First order condition for profit maximization is:

$$p \left(1 - \frac{q}{\beta Q}\right) - c = 0.$$

Using (1) and adding first order conditions over the $N$ firms, we obtain the following expression for total equilibrium quantity:

$$Q = Nq = \frac{\alpha m^{\gamma}}{N^{\beta}} \left[ \frac{N\beta - 1}{c\beta} \right]^{\beta},$$

which gives equilibrium price: $p = \frac{N\beta}{N\beta-1}c$. Substituting $q$ and $p$ in the profit function, we obtain the following expression:

<footnote n="1">¹This assumption is of course not realistic as firms in each location usually serve consumers in several locations. It is however only made for analytical clarity and our demand variable will take into account the fact that firms serve several locations.</footnote>
$$ \pi = (1-t) \left[ \frac{\alpha m^{\gamma}}{N(\beta+1)} \left( \frac{\beta}{N\beta-1} c \right)^{1-\beta} \right] $$

Supposing that price elasticity is superior to 1 ($\beta > 1$), profit is a decreasing function of corporate tax rate, production costs and number of active firms, that is competition intensity (a traditional effect in location theory, see d'Aspremont et al., 1979). Profit is also an increasing function of consumers' expenditure, i.e. of market size.

Let us specify a bit more the unit cost function such that unit cost is a function of ongoing wage ($w$) and includes a positive externality related to the number of firms<fnref n="2" />:

$$ c = w^{\theta_w} N^{-\theta_N}, \quad \theta_w > 0, \theta_N > 0. $$

The influence of the number of firms is then ambiguous. Suppose the number of firms is such that $N\beta$ constitutes a reasonably good approximation of $N\beta - 1$. The profit function can then be written as:

$$ \pi = (1-t)\alpha m^{\gamma} (w^{\theta_w})^{1-\beta} N^{(-\theta_N-1)(1-\beta)-(\beta+1)}. \quad (3) $$

Equation (3) is log-linear and gives us the theoretical foundations of our empirical estimation of location determinants. The signs of the first three variables are still clearly determined, but the sign of the count of firms variable is now depending on the importance of the externality parameter relative to the negative impact of competition.

<footnote n="2">²This externality takes a form comparable to the one specified by Belleflamme, Picard and Thisse (1999). For the foundations of such externalities and distinctions between different possible types of externalities, the reader can get back to the seminal work of Marshall (1920), recently inspiring a large number of papers including Krugman (1991), David and Rosenbloom (1990), Arthur (1990, 1994), Fujita and Thisse (1996, 1999).</footnote>
# 3 Implementation of the location choice model

## 3.1 The econometric model

The economic decision studied in theoretical location models is by nature a discrete choice among several alternatives made by individual firms. Turning to empirical matters, the econometric model should therefore ideally have these features too. For this reason, models with a qualitative endogenous variable and particularly conditional logit models have been widely used in the preceding empirical work on industrial location.

This model assumes that firms maximize a profit function subject to uncertainty when choosing a location. The deterministic component of the profit function consists of the various attributes of locations that can influence the profitability of building a plant in a particular location (equation 3). The random component consists of maximization errors, unobserved characteristics of choices or measurement errors. While the real underlying profit yielded by alternative locations cannot be observed, what is observed is the actual choice of each firm and the characteristics of the alternative locations.

There is a set $I = (i, j, ...n)$ of possible location sites and the location $i$ offers a profit $\pi_i$ to investors according to a set of variables $U_i = [\ln X_{i1}, ..., \ln X_{ik}]$, $X_i = [X_{i1}, ..., X_{ik}]$ being the vector of observable characteristics of location $i$.

$$ \pi_i = b U_i + \epsilon_i $$

Where $b$ is the vector of unknown coefficients to be estimated and $\epsilon_i$ is the unobservable advantage of location $i$. All characteristics vary among locations, some fluctuate among industries as well but they are constant across firms. It is assumed that an investor chooses location $i$ if it yields higher profits than any other possible choice. The probability of choosing $i$ is thus:
$$ \mathrm{Prob}(\pi_i > \pi_j) = \mathrm{Prob}(\epsilon_j < \epsilon_i + \mathbf{U}_i - \mathbf{U}_j) \quad \forall j \neq i. $$

This probability of choosing location *i* was shown to take the following form (McFadden, 1984) under a type I extreme value distribution of the error term:

$$ P_i = \frac{e^{\mathbf{b}\mathbf{U}_i}}{\sum_{j=1}^{n} e^{\mathbf{b}\mathbf{U}_j}}. $$

Using this formula for the probability of investing in each location, the coefficient on each variable is then estimated by maximum likelihood. The expected signs and magnitudes of those coefficients are dictated by equation (3).

## 3.2 The data

### The dependent variable

We consider location decisions by foreign investors in France. The left hand side variable needed is the location choice of each investor over all the 92 French départments<fnref n="3" /> with its nationality, date of investment and industry. The main database (IEF) comes from the Direction du Trésor and was constructed based on the (now softened) legal obligation for foreign firms to ask for an administrative authorization to invest in France. This database reports many information on foreign affiliates operating in France including date and town of investment, employment, the different shareholders, their nationality and respective shares... We use the 1996 version of the database and consider investments from 1985 to 1995 mainly because of data limitations on the right hand side variables. We consider only firms for which the industry reported is manufacturing. We sum up the shares of foreign investors by country of origin and then keep only the firms for which the largest share is held by a foreign country and for which this share

<footnote n="3">³Which correspond to the NUTS 3 level in the Eurostat geographical classification of regions for France.</footnote>
is at least 10%. We attribute the firm to the first country of ownership when several countries
have shares in a given firm.

The main problem of this database is that only the location of the headquarters is reported.
However using the identification number of the firm, it is possible to track the producing units
belonging to the firm and then get its precise location using a different database made available
by the Ministry of Industry. The procedure has therefore been the following : take all invest-
ments with their dates in IEF and keep all producing units that belonged to a firm in IEF and
were "born" the same year or after the date reported in IEF. The date of creation of the plant
is inferred by looking at the first year where that establishment was reported by the French
Ministry of industry. This gives us our sample of location choices which consists of a close to
exhaustive list of producing units owned by foreign companies in France and established during
that period. The sample contains 3902 observations over 206 industries.

Agglomeration variables

The agglomeration variables consist of cumulated counts of firms based on that sample. The calculation follows Head et al. (1995) and consists of the count of firms belonging to the same industry (but not belonging to the same parent company) that were located in each département a year before the considered investment.

We here introduce new dimensions in the measurement of agglomeration effects. We first
try to identify the “nationality” of the agglomerative force. We try to answer the following
question: Does a firm exhibit a tendency to cluster equally with other firms from the same
origin country, other non-French firms and/or French firms? There will therefore be three
agglomeration variables: NH, NO and NF accounting respectively for the effect of the presence
of same home country firms, other foreign firms and French firms. The sign and magnitude
of each coefficient on those variables is dependent on the relative strengths of the competition
effect and positive externalities exposed in the theory section. The comparison of coefficients on those variables will enable us to draw insights on the possibly different effects depending on different nationalities of the investors.

Second, we are able, due to the large size of the sample to differentiate between country of origin and industry in the location behavior of multinational firms. This is of course of primary importance for policy issues as it will appear that there is a large amount of heterogeneity calling for different measures by public authorities if they intend to attract FDI and conduct regional policies.

## Demand variable

Unlike most of the empirical literature on location choice<fnref n="4" />, the demand variable taken here is not local GDP. The French départements are of relatively little size and it seems clear that foreign investors target consumers far beyond the frontier of the département they choose to locate in. However, transport costs make distant consumers more difficult to reach and we must take this difficulty of access into account. We do so by using a variable inspired by the concept of market potential introduced by Harris (1954):

$$MARK_i = GDP_i + \sum_{j \neq i} \left( \frac{GDP_j}{d_{ij}} \right).$$

Our demand variable, labeled MARK, therefore takes into account local GDP of the chosen location and adds the GDPs of all other locations weighted by their distance to the chosen location.

The source of the GDP data is REGIO, the Eurostat regional database that allocates French GDP between French départements. The distance variable consists of real road distances (fastest

<footnote n="4">⁴A notable exception being Friedman et al. (1992) who use a demand variable similar to the one used here in their analysis of foreign investment in the United States.</footnote>
trip between the respective centers of the two départements) that have been collected using an
electronic road atlas. MARK is of course only a proxy for the real demand perceived by firms. It
is very difficult here to obtain data that could allow for more sophisticated demand variable at
this geographical level. We believe that this proxy is however reasonable for a large proportion
of the industries in the sample and the results seem to confirm this belief.

Labour costs variable

We were able to calculate a very precise wage per capita variable at the département and 4-digit industry level. Using data on producing units operating in each département<fnref n="5" />, we include the average wage per capita of the industry, département and year relevant for the investment considered. The variable is labeled W.

Distance to home country variable

As shown below, the location choices of FDI are also influenced by proximity with the investor's
home market. We take into account this tendency of foreign firms to invest in locations near
their country through a distance variable labeled DIS (the great circle distance between the
economic center of the investing firm's country of origin and the center of each département
available as a location).

Grants variables

The main instrument of regional policy available to the French government is called the *Prime d'Aménagement du Territoire* (PAT) and consists of labor related grants for creating or maintaining jobs in lagging regions. Both French and foreign investments are eligible for these grants and about half the annual funding goes to foreign investors. We estimate the influence of this

<footnote n="5">⁵When data was missing because the département had no firm in the 4-digit industry, we used an average of the average national wage for this industry and the average wage across industries in this location.</footnote>
policy instrument on location choice by introducing a variable called PAT which takes into
account the funds granted to each region for the year of the considered investment.

Some French regions are also eligible to the European Commission regional policy involving
grants that are sometimes used to attract FDI but most of the time consists of attempts to
improve the economic environment with an expected indirect effect on attractiveness.

There are three main Objectives under which the Commission grants structural funds to
European Regions. Objective 1 is aimed at promoting development and structural adjustments
in regions that are lagging behind (defined as those with GDP per head below 75% of the Union
average). No French region considered in this paper is in this case, due to the fact that we do
not consider Corsica as part of the choice set. Objective 2 is aimed at promoting the conversion
of areas affected by industrial decline. Objective 5b is aimed at rural areas affected by problems
linked to the decline of agriculture. There are numerous French regions receiving grants under
those two objectives, the two main receivers being Nord-Pas-de-Calais and Lorraine. We also
include the “community initiatives” funds. The corresponding variables are the grants given to
each region under each relevant objective, respectively : OBJ2, OBJ5B, HOBJ.

# 4 Results

## 4.1 Spatial patterns of investment

Figures 1 and 2 show the spatial distribution of foreign affiliates in France for investors origi-
nating from other European countries (all affiliates in the département being attributed to the
central city for this graph). It is readily apparent that the region around Paris has been a major
recipient of investments for FDI from all origin countries. It is also striking that some investors
have a preference for locations in France that are near the border with their origin country.
This is particularly apparent for FDI originating from Belgium, The Netherlands, Germany and
<figure><img src="image_4.png" /><figcaption>Figure 2: Patterns of foreign affiliates in France: FDI from Italy, Switzerland and the United Kingdom</figcaption></figure>

<figure><img src="image_5.png" /></figure>
Switzerland. Similarities in culture, language, tastes, distribution networks... might therefore be an important determinant of location choices by investors originating from a country that has a common border with France. This behaviour is to be compared with other econometric relationships uncovered in international trade and FDI studies exhibiting a positive impact of cultural similarity on goods or factors flows. Concerning trade flows for instance, it is well known that a common language constitutes a powerful motivation for bilateral trade. It has furthermore been recently showed by Rauch (1999) that colonial ties had a strong positive effect on trade. Head and Ries (1998) also found a positive impact of migrants from the partner country on bilateral trade.

This pattern suggests that regions in France that share a border with the country of origin of the investor offer important advantages in the trade off between the costs of operating abroad and the access to French consumers: Whereas those locations might not offer an ideal access to the rest of French consumers, they present a whole set of features that are common with the country of origin of the investor and can therefore diminish the cost of operating abroad.

## 4.2 Results by country of origin

We now proceed to the estimation of the determinants of location choice of foreign investors in France by country of origin. Tables 1 and 2 present the results for the 8 main investing countries in France, that is the United States (21.3%), Germany (16.2%), the Netherlands (12.5%), Italy (10.9%), the United Kingdom (10.4%), Switzerland (9.3%), Belgium (6.4%), and Japan (2.1%).

The global regression shows, as expected, a positive influence of the market potential and a negative influence of wages. We also get a positive effect of competitors presence. This shows that agglomeration forces dominate dispersion forces on average, this finding being an indicator of spillovers that might exist between firms. It is noteworthy that the agglomeration with French firms is much higher than agglomeration with other foreign firms either of the same or a different
<table><caption>Table 1: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="6">Dependent Variable: Location choice</th></tr><tr><th>Total</th><th>Belgium</th><th>Netherlands</th><th>Germany</th><th>Italy</th><th>UK</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>0.18***<br>(0.06)</td><td>0.31<br>(0.29)</td><td>-0.51**<br>(0.25)</td><td>-0.28*<br>(0.15)</td><td>0.04<br>(0.30)</td><td>0.53**<br>(0.21)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.29***<br>(0.03)</td><td>0.55***<br>(0.14)</td><td>0.12<br>(0.10)</td><td>0.65***<br>(0.09)</td><td>0.28***<br>(0.10)</td><td>0.18<br>(0.11)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>0.76***<br>(0.02)</td><td>1.11***<br>(0.10)</td><td>0.55***<br>(0.06)</td><td>0.79***<br>(0.06)</td><td>0.36***<br>(0.07)</td><td>0.97***<br>(0.07)</td></tr><tr><td>ln market potential (MARK)</td><td>0.43***<br>(0.03)</td><td>0.38***<br>(0.12)</td><td>0.66***<br>(0.10)</td><td>0.32***<br>(0.07)</td><td>0.66***<br>(0.10)</td><td>0.69***<br>(0.10)</td></tr><tr><td>ln wage (W)</td><td>-0.31***<br>(0.09)</td><td>-1.20***<br>(0.36)</td><td>-0.80***<br>(0.25)</td><td>-0.05<br>(0.25)</td><td>-1.25***<br>(0.21)</td><td>-0.24<br>(0.31)</td></tr><tr><td>ln distance to home country (DIS)</td><td>-0.75***<br>(0.05)</td><td>-0.86***<br>(0.12)</td><td>-0.22<br>(0.14)</td><td>-1.32***<br>(0.11)</td><td>-0.51**<br>(0.22)</td><td>0.02<br>(0.14)</td></tr><tr><td>N. obs.</td><td>3902</td><td>251</td><td>489</td><td>632</td><td>425</td><td>404</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.166</td><td>0.273</td><td>0.127</td><td>0.182</td><td>0.096</td><td>0.239</td></tr></tbody></table>

Note: Standard errors in parentheses with **, ** and * respectively denoting significance at the 1%, 5% and 10% level.
<table><caption>Table 2: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="3">Dep. Var: Location choice</th></tr><tr><th>USA</th><th>Japan</th><th>Switzerland</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>0.12<br>(0.10)</td><td>1.33*<br>(0.70)</td><td>-0.27<br>(0.19)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.28***<br>(0.08)</td><td>0.38<br>(0.26)</td><td>0.81***<br>(0.11)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>1.18***<br>(0.05)</td><td>1.50***<br>(0.19)</td><td>1.07***<br>(0.08)</td></tr><tr><td>ln market potential (MARK)</td><td>0.16**<br>(0.07)</td><td>0.53***<br>(0.20)</td><td>0.17*<br>(0.10)</td></tr><tr><td>ln wage (W)</td><td>0.86***<br>(0.21)</td><td>-0.69<br>(0.59)</td><td>0.03<br>(0.34)</td></tr><tr><td>ln distance to home country (DIS)</td><td>-0.95<br>(1.26)</td><td>1.03<br>(6.98)</td><td>-0.59***<br>(0.13)</td></tr><tr><td>N. obs.</td><td>833</td><td>83</td><td>363</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.191</td><td>0.291</td><td>0.198</td></tr></tbody></table>

Note: Standard errors in parentheses with ***, ** and * respectively denoting significance at the 1%, 5% and 10% level.
country of origin. This might indicate that competition faced by a foreign investor in France
is fiercer with other foreign firms than with French firms. This might also be the sign that
the technological spillovers passing through informal communication or inter-firm mobility of
qualified workers is substantially easier with French firms. Another interpretation of that result
can be made in term of informational externalities (see Banerjee, 1992, and De Coster and
Strange, 1993, for theoretical models). Supposing that French firm have a better information
than foreign companies on the "true" compared attractiveness of French départements, the
location of French firms would therefore convey more information than the location of foreign
affiliates for the prospective investor. As a consequence, foreign investors would be more willing
to replicate French firms' choices than choices made by other multinational companies that
might not have any better information than them.

The results show however a substantial variability depending on the country of origin of
the firm. Some countries like Italy or the Netherlands show very low agglomeration effects
compared to the rest of the sample. Those two countries, as Belgium, are also much more
sensitive to the level of labor costs in their location choices. The American investors seem to
search very productive workers despite their higher cost as shown by the positive coefficient on
wages. Together with Swiss investors, they also show a less than average tendency to locate
near final demand perhaps denoting the fact that the products sold are less tied to local demand
than others.

This very large dataset enables to uncover important differences in firms’ behaviour in terms
of clustering behaviour. While on average firms tend to moderately follow the choices of com-
petitors from the same country, there are large differences depending on the nationality of the
investor. English firms for instance tend to follow English competitors as strongly as firms from
the Netherlands tend to avoid other firms from the Netherlands. This is one of the first study
where such heterogeneity, informative of relative strength of competition and agglomeration
effects, is identified. It is also noteworthy that the coefficient on this variable, while it can be of
either sign in theory, had never to our knowledge been significantly negative before this paper.

Finally, we observe in the regressions the impact of distance already seen in figures 1 and 2
for certain countries. The negative effect of distance from the country of origin is specially strong
for Germany, Belgium and Switzerland and negative while insignificant for the Netherlands. We
know focus for a moment on this determinant and its evolution over time.

4.3 The learning process of foreign investment

A possible explanation of the negative sign of distance from the origin country on location
choice is that proximate départements share common characteristics with the origin country
that make them particularly favorable sites for location<fnref n="6" />. Behind the fact that some investors
show a preference for regions in France contiguous with their home market might be a trade off
between access to French consumers and ease of operation. The disadvantage faced by foreign
affiliates with respect to local firms due to their lower knowledge of the characteristics of the
local market is one of the oldest ideas in the theory of FDI (see Hirsch, 1976 for instance). It
is likely that locations in France near the German border share a sufficient amount of common
"cultural" characteristics with Germany so that the cost of operating there (relative to local
firms) is lower, *ceteris paribus*, than the cost of operating in more western locations in France.

The existence of transport costs however do not make those locations ideal basing points to
serve the majority of French consumers. The trade off could then work as follows: Supposing
that firms gradually learn the characteristics of the French market once they have an affiliate
anywhere in France, the first waves of investment could then be concentrated near the German
border in order to lower the disadvantage of being a foreign affiliate. The next waves could

<footnote n="6">⁶One might also envision that this result reflects a relocation pattern of FDI: Investors look for a location in France near their home market to enjoy relatively low production costs compared with the ones they face in their own country, without increasing too much the transport cost to their home market. If this explanation might be plausible for Swiss or German investors, it does not seem credible for Belgian nor Dutch ones.</footnote>
gradually move West in order to improve the access to consumers because firms have learned
some of the characteristics of this market, reducing thereby their initial disadvantage.

Support for this intuition can then be found by looking at the evolution of the coefficient on
distance and market potential. We expect a rise in both, the first becoming less negative with
time and the second more positive. If following your rivals also participate in reducing your
information gathering costs about the characteristics of the French market, we should then also
expect a fall of the coefficient on NH.

<figure><img src="image_6.png" /><figcaption>Figure 3: The evolution of coefficients over time</figcaption></figure>

Figure 3 plots the three coefficients mentioned over the period of our sample and confirms the expected evolution for all three variables<sup>7</sup>. The negative effect of distance is sharply declining over time, with a simultaneous increase of the coefficient on the market potential variable. The coefficient on distance starts at -1.08 and ends at -0.35: At the beginning of the sample, decreasing distance by 50% from the country of origin increases the probability of receiving

<footnote n="7"><sup>7</sup>In order to improve stability and robustness of those estimations, we run logits for each couple of successive years (1985-1986, 1986-1987...).</footnote>
an investment by roughly 50%, only about 17% at the end. We interpret this as evidence of
some learning process of foreign investors gradually moving from “familiar” locations near the
country of origin to more central locations in France, where access to consumers is better. It
is also to be noted that the coefficient on same country of origin competitors is shifting from
a clustering behaviour to an avoiding behaviour (the coefficient is significantly negative at the
end of the period)

## 4.4 Results by industry

We now proceed to regressions at the industry level to see which industries exhibit the most
significant agglomeration effects and thus potentially constitute the most interesting targets
for promotion policies as an initial advantage in the attraction of those industries could then
result in self reinforcing agglomeration in the départements taking a good start. Due to the
very large sample used we are able to conduct the analysis at the 4-digit level guaranteeing
a very precise assessment of the industries that are more subject to agglomeration economies.
Tables 7 and 8 in the appendix rank industries using the average value of the 3 agglomeration
variables coefficients. Table 7 lists industries for which all three agglomeration variables have
significant positive signs and table 8 lists the other industries. There are 76 industries for
which estimation of all variables is possible. Of these, 8 have all three agglomeration variables
significantly positive.

We see that industries often mentioned as characterized by strong positive spillovers appear
in the first group of industries where we find very significant agglomeration effects. Computers
is of course one of those as office machinery for which the coefficients are extremely large<sup>8</sup> , a
10% increase in the number of firms in a département roughly increasing probability of investing
there by 40%, for this industry.

<footnote n="8"><sup>8</sup>All variables are taken in logs. As a consequence, all coefficients can roughly be interpreted as elasticities for an “average” investor of the change of each variable in a location on the probability of investing in that location.</footnote>
Another interesting result is the fact that in the clothing industry, often referred to as characteristic of a low-skill labor intensive industry, the investors are very sensitive to wage differentials and much less to agglomeration economies (there is even a significant repulsion effect of other origins foreign investors in women's apparel). On the other hand, the industries with significant agglomeration effects do not seem to be sensitive to the level of labor costs.

We are therefore able to confirm here differences in the location decisions of investors across industries that were previously largely hypothesized in the literature without being rigorously tested due to lack of data. We found in particular that industries that where often thought to be characterized by strong agglomeration economies more than labor costs considerations (like computers, machine tools or car parts) indeed show this tendency. Conversely, low skill industries like clothing are not very sensitive to agglomeration economies but seem to favor costs differentials. Also, industries that are closely spatially tied to final consumption like magazine or newspaper editing show an overwhelming tendency to follow the spatial distribution of consumers rather than the spatial distribution of competitors.

# 5 The influence of promotion policies on location choice

## 5.1 The impact of the European Commission regional policy

In this section, we estimate the influence of Structural funds granted to French Regions by the European Commission. The data made publicly available by the Commission is surprisingly scarce. However we have the grants by Objective and region in France for the years 1989, 1990 and 1991 which were the years where FDI in France was at its peak. We are therefore able to estimate the influence of those grants on location decisions of foreign investors for those three years because the number of observations remains substantial.

It appears in tables 3 and 4 that very few coefficients on the different grants of the Euro-
<table><caption>Table 3: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="6">Dependent Variable: Location choice</th></tr><tr><th>Total</th><th>Belgium</th><th>Netherlands</th><th>Germany</th><th>Italy</th><th>UK</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>0.33***<br>(0.11)</td><td>-0.17<br>(0.62)</td><td>0.22<br>(0.65)</td><td>-0.53*<br>(0.29)</td><td>0.70<br>(0.89)</td><td>0.55<br>(0.34)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.31***<br>(0.06)</td><td>0.64**<br>(0.27)</td><td>0.18<br>(0.15)</td><td>0.68***<br>(0.16)</td><td>0.40<br>(0.26)</td><td>-0.05<br>(0.19)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>0.78***<br>(0.04)</td><td>0.93***<br>(0.17)</td><td>0.41***<br>(0.08)</td><td>1.02***<br>(0.11)</td><td>0.58***<br>(0.16)</td><td>1.01***<br>(0.14)</td></tr><tr><td>ln market potential (MARK)</td><td>0.42***<br>(0.06)</td><td>0.67***<br>(0.24)</td><td>0.80***<br>(0.14)</td><td>0.35**<br>(0.15)</td><td>0.63**<br>(0.27)</td><td>0.59***<br>(0.18)</td></tr><tr><td>ln wage (W)</td><td>-0.49***<br>(0.17)</td><td>-2.18***<br>(0.70)</td><td>-1.02***<br>(0.35)</td><td>-0.10<br>(0.43)</td><td>-1.42**<br>(0.69)</td><td>-0.02<br>(0.53)</td></tr><tr><td>ln distance to home country (DIS)</td><td>-0.80***<br>(0.09)</td><td>-1.07***<br>(0.33)</td><td>-0.68**<br>(0.28)</td><td>-1.64***<br>(0.19)</td><td>-0.97<br>(0.60)</td><td>0.20<br>(0.28)</td></tr><tr><td>ln objective 2 grants (OBJ2)</td><td>-0.01<br>(0.01)</td><td>-0.01<br>(0.04)</td><td>-0.01<br>(0.02)</td><td>-0.02<br>(0.02)</td><td>0.04<br>(0.03)</td><td>-0.01<br>(0.02)</td></tr><tr><td>ln objective 5b grants (OBJ5B)</td><td>0.00<br>(0.01)</td><td>0.02<br>(0.06)</td><td>0.06**<br>(0.03)</td><td>0.04<br>(0.03)</td><td>-0.04<br>(0.05)</td><td>-0.06*<br>(0.04)</td></tr><tr><td>ln community initiatives grants (HOBJ)</td><td>0.03**<br>(0.01)</td><td>0.03<br>(0.05)</td><td>-0.02<br>(0.03)</td><td>0.05*<br>(0.03)</td><td>0.07<br>(0.05)</td><td>0.04<br>(0.03)</td></tr><tr><td>N. obs.</td><td>1264</td><td>76</td><td>270</td><td>193</td><td>66</td><td>146</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.166</td><td>0.287</td><td>0.13</td><td>0.208</td><td>0.159</td><td>0.208</td></tr></tbody></table>

Note: Standard errors in parentheses with ***, ** and * respectively denoting significance at the 1%, 5% and 10% level.

pean Commission are significantly positive. Furthermore, when the impact of those grants is significantly positive, the size of the effect is lower than all other determinants by an order of magnitude. This means that, even in the few cases where some results of such policies are to be expected, they will be very largely outweighed by agglomeration or market potential considerations. Worse, the only significant coefficient that appears to be non negligible is a negative one for Japanese investors. This result is not the first to find little or even negative effect of European grants on attractiveness (see Ferrer, 1998). This does not mean that those policies
<table><caption>Table 4: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="3">Dep. Var: Location choice</th></tr><tr><th>USA</th><th>Japan</th><th>Switzerland</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>-0.15<br>(0.19)</td><td>1.75<br>(1.31)</td><td>-0.48<br>(0.34)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.53***<br>(0.14)</td><td>0.37<br>(0.49)</td><td>0.91***<br>(0.20)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>1.21***<br>(0.10)</td><td>1.47***<br>(0.36)</td><td>1.11***<br>(0.14)</td></tr><tr><td>ln market potential (MARK)</td><td>0.24*<br>(0.14)</td><td>0.34<br>(0.38)</td><td>-0.51**<br>(0.23)</td></tr><tr><td>ln wage (W)</td><td>0.81**<br>(0.38)</td><td>-0.55<br>(0.95)</td><td>0.26<br>(0.65)</td></tr><tr><td>ln distance to home country (DIS)</td><td>0.37<br>(2.49)</td><td>15.45<br>(14.11)</td><td>-0.51**<br>(0.24)</td></tr><tr><td>ln objective 2 grants (OBJ2)</td><td>0.00<br>(0.02)</td><td>-0.04<br>(0.06)</td><td>0.02<br>(0.03)</td></tr><tr><td>ln objective 5b grants (OBJ5B)</td><td>0.01<br>(0.03)</td><td>0.09<br>(0.10)</td><td>-0.07*<br>(0.04)</td></tr><tr><td>ln community initiatives grants (HOBJ)</td><td>0.00<br>(0.03)</td><td>-0.23**<br>(0.10)</td><td>-0.04<br>(0.04)</td></tr><tr><td>N. obs.</td><td>242</td><td>29</td><td>107</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.212</td><td>0.274</td><td>0.188</td></tr></tbody></table>

Note: Standard errors in parentheses with **, ** and * respectively denoting significance at the 1%, 5% and 10% level.

are inefficient generally speaking, but it casts doubts on the idea that the regional policy of the European Commission can actually influence or even reverse the “natural” location patterns of economic activity in Europe.

There is a problem with the evaluation of the influence of those funds which is often mentioned to explain the apparent poor and even sometimes negative influence of those grants. The regional policies are designed to help the catching up of lagging regions or compensate a fall in their employment due to a structural crisis in the industries in which those regions are
specialized. The fact that a region was not successful at attracting FDI is often a cause for
its economic problems and the consequence of this can be a negative coefficient on the grant
variable observed by the researcher. Our econometric implementation taking into account fun-
damental characteristics of regions, the market potential and wage variables should capture
these structural issues and avoid any potential reverse causality problem when estimating the
effect of grants on foreign investors.

We however evaluate the robustness of this apparent inefficiency of European grants on
regional attractiveness by assessing the influence of past regional policy on present location
choices. With some time, the funds might have improved the attractiveness of the region and
the investors in subsequent years might be less repelled by this region. We therefore estimate
the impact of the cumulated funds received by French regions between 1989 and 1991 on the
choices between 1992 and 1995 and compare the coefficients with the preceding regression<sup>9</sup>. In
order to save space, we do not include the results table here<sup>10</sup> but the results are strikingly
similar. No coefficient is significantly positive and all significant coefficients are negative. All
estimates of the impact of regional European grants are furthermore of very low magnitude.
We can therefore safely conclude that, if the European grants in French regions have had any
impact on the location of FDI, this impact was very slightly negative for some origin countries.
In general there is simply no identified relationship between the amount given to a region by the
European Commission and the probability that foreign investors choose to locate an affiliate in
a département belonging to that region.
<table><caption>Table 5: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="6">Dependent Variable: Location choice</th></tr><tr><th>Total</th><th>Belgium</th><th>Netherlands</th><th>Germany</th><th>Italy</th><th>UK</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>0.18***<br>(0.06)</td><td>0.30<br>(0.29)</td><td>-0.55**<br>(0.25)</td><td>-0.29**<br>(0.14)</td><td>0.05<br>(0.30)</td><td>0.53**<br>(0.21)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.29***<br>(0.03)</td><td>0.54***<br>(0.14)</td><td>0.11<br>(0.10)</td><td>0.65***<br>(0.09)</td><td>0.27**<br>(0.10)</td><td>0.18<br>(0.11)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>0.76***<br>(0.02)</td><td>1.10***<br>(0.10)</td><td>0.54***<br>(0.06)</td><td>0.79***<br>(0.06)</td><td>0.40***<br>(0.07)</td><td>0.97***<br>(0.07)</td></tr><tr><td>ln market potential (MARK)</td><td>0.48***<br>(0.03)</td><td>0.42***<br>(0.13)</td><td>0.77***<br>(0.10)</td><td>0.38***<br>(0.08)</td><td>0.77***<br>(0.11)</td><td>0.67***<br>(0.11)</td></tr><tr><td>ln wage (W)</td><td>-0.20**<br>(0.10)</td><td>-1.12***<br>(0.38)</td><td>-0.50*<br>(0.28)</td><td>0.09<br>(0.26)</td><td>-1.05***<br>(0.22)</td><td>-0.29<br>(0.33)</td></tr><tr><td>ln distance to home country (DIS)</td><td>-0.74***<br>(0.05)</td><td>-0.83***<br>(0.13)</td><td>-0.24*<br>(0.13)</td><td>-1.28***<br>(0.11)</td><td>-0.33<br>(0.22)</td><td>0.04<br>(0.14)</td></tr><tr><td>ln French regional policy grants (PAT)</td><td>0.02***<br>(0.00)</td><td>0.01<br>(0.02)</td><td>0.04***<br>(0.01)</td><td>0.02*<br>(0.01)</td><td>0.05***<br>(0.02)</td><td>-0.01<br>(0.01)</td></tr><tr><td>N. obs.</td><td colspan="6">3902</td></tr><tr><td>Pseudo R<sup>2</sup></td><td colspan="6">0.166</td></tr><tr><td></td><td colspan="6">0.273</td></tr><tr><td></td><td colspan="6">0.129</td></tr><tr><td></td><td colspan="6">0.182</td></tr><tr><td></td><td colspan="6">0.099</td></tr><tr><td></td><td colspan="6">0.24</td></tr></tbody></table>

Note: Standard errors in parentheses with ***, ** and * respectively denoting significance at the 1%, 5% and 10% level.

## 5.2 The impact of French regional policy

We now turn to the analysis of the impact of French regional policy on location choices by foreign investors. One can justify the extremely small consequences of European grants on FDI flows, saying they have for objective to promote the regional development and attractiveness but not to affect directly location choices. On the contrary, the PAT is explicitly a direct regional policy in favor of foreign investments. However, we see in the tables 5 and 6 that the impact of French regional policy (at least the PAT) is far from being as influential on location choices as

<footnote n="9">⁹Such a test is only relevant if one supposes that *past grants* do not directly improve the *current* regional market potential, nor increase the wage and the number of local firms. We regressed these variables on the amount of grants received during the preceding period. We found no influence on the number of local firms and a very small negative impact on market potential and wage.</footnote>

<footnote n="10">¹⁰They are available upon request.</footnote>
<table><caption>Table 6: Location choice of foreign investors in France.</caption><thead><tr><th rowspan="2">Model :</th><th colspan="3">Dep. Var: Location choice</th></tr><tr><th>USA</th><th>Japan</th><th>Switzerland</th></tr></thead><tbody><tr><td>ln Nb. of same home country firms (NH)</td><td>0.10<br>(0.10)</td><td>1.30*<br>(0.70)</td><td>-0.31<br>(0.19)</td></tr><tr><td>ln Nb. of other foreign firms (NO)</td><td>0.28***<br>(0.08)</td><td>0.39<br>(0.26)</td><td>0.82***<br>(0.11)</td></tr><tr><td>ln Nb. of French firms (NF)</td><td>1.19***<br>(0.05)</td><td>1.50***<br>(0.19)</td><td>1.07***<br>(0.08)</td></tr><tr><td>ln market potential (MARK)</td><td>0.12<br>(0.07)</td><td>0.50**<br>(0.22)</td><td>0.10<br>(0.11)</td></tr><tr><td>ln wage (W)</td><td>0.78***<br>(0.21)</td><td>-0.76<br>(0.61)</td><td>-0.21<br>(0.35)</td></tr><tr><td>ln distance to home country (DIS)</td><td>-0.41<br>(1.33)</td><td>1.35<br>(7.12)</td><td>-0.63***<br>(0.13)</td></tr><tr><td>ln French regional policy grants (PAT)</td><td>-0.02<br>(0.01)</td><td>-0.01<br>(0.03)</td><td>-0.04**<br>(0.01)</td></tr><tr><td>N. obs.</td><td>833</td><td>83</td><td>363</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.191</td><td>0.292</td><td>0.199</td></tr></tbody></table>

Note: Standard errors in parentheses with ***, ** and * respectively denoting significance at the 1%, 5% and 10% level.

the policy makers would like it to be. The global effect is significantly positive but again the magnitude of the effect is the lowest of all determinants. The only three countries for which we find a positive impact are The Netherlands, Germany and Italy. Even for those countries, the effect of grants on location choice is very marginal when compared to other determinants.

The Italian case is very interesting as it appears that investors from this country are very sensitive to monetary incentives compared to other investors. Indeed the positive effect of the grants is highly significant and the largest of all countries. Furthermore, Italian firms proves to be also among the most highly sensitive to wage differentials and show very little tendency to agglomerate relative to other nationalities. German and Dutch investors also show a much
smaller tendency to cluster than the rest of the sample (even showing significant dispersion in
the NH coefficient). Combined with the fact that those three countries are the ones which seems
to be attracted by investment incentives, our study leaves therefore very little prospective for
an efficient attractiveness policy. Indeed the investors that are sensitive to these measures, are
only slightly so and do not cluster, hence the benefits of the grants to the region are likely to
be very small. Succeeding in attracting initial investments from those countries is not sufficient
to generate a self-reinforcing agglomeration of firms from those countries.

6 Conclusion

We assess in this paper the determinants of location choice of foreign investors in France. We focus in particular on the existence and magnitude of agglomeration economies and identify the countries of origin and industries for which positive spillovers suggested by the observed clustering between firms are the more pronounced. It appears that computers, car parts, machine tools and office machinery are instances of sectors characterized by the strongest agglomeration effects. A 10% increase in the number of competitors in a location can raise by up to 40% the average probability of investing in that location. On the other hand, firms originating from countries like Italy or the Netherlands and from industries like clothing are much less subject to clustering and more sensitive to the level of wages in their location patterns.

We also uncovered general patterns of FDI location that had not been, to our knowledge,
the subject of rigorous empirical investigation. Among these, the location behaviour of foreign
firms in France over time seems to suggest that a learning process is taking place in location
choices over time: Firms tend to choose locations in France that are near their home market
over the whole period, particularly when the investor is from Germany, Belgium, Switzerland
and the Netherlands. However, this pattern gradually fades out over the period as the impact
of the market potential gets bigger and clustering behaviour gets weaker. We interpret this
evolution as evidence of learning of the French market characteristics by foreign firms: As time
passes it becomes more and more important to be near final demand and less and less important
to be in locations sharing common attributes with the home country.

We finally try to assess the impact of French and European regional policies through the inclusion of investment incentives and structural funds in the location choice but hardly find any positive impact of either measure on the actual choices of investors. Even if important agglomeration economies might be a favorable ground for effective attractiveness and regional policies, the evidence for France is very negative: We don't find any rise in the attractiveness of French départements when investing there is associated with grants from public authorities.

7 References

ARTHUR, W.B., 1990, “Silicon Valley Locational Clusters: When do Increasing Returns Imply Monopoly?” *Mathematical Social Science* 19(3): 235-251.

ARTHUR, W.B., 1994, *Increasing Returns and Path Dependence in the Economy*. The University of Michigan Press.

BANERJEE, A., 1992, “A Simple Model of Herd Behaviour” *Quarterly Journal of Economics* 57: 797-817.

BELLEFLAMME, P., P. PICARD AND J-F. THISSE, 1999, “An Economic Theory of Regional Clusters” Mimeo, Université Catholique de Louvain.

CARLTON, D., 1983, “The Location and Employment Choices of New Firms : an Econometric Model with Discrete and Continuous Endogenous Variables” *Review of Economics and Statistics* 65 : 440-449.

DAVID, P.A. AND J.L. ROSENBLOOM, 1990, “Marshallian factor market externalities and the dynamics of industrial localisation” *Journal of Urban Economics* 28(3) : 349-370.

D'ASPREMONT, C., J.J. GABSZEWICZ AND J.F. THISSE, 1979, "On Hotelling's Stability in Competition" *Econometrica* 47: 1045-1050.

DE COSTER, G. AND W. STRANGE, 1993, “Spurious Agglomeration” *Journal of Urban Economics* 33 : 273-304.

DEVEREUX, M. AND R. GRIFFITH, 1998, “Taxes and the Location of Production : Evidence from a Panel of US Multinationals” *Journal of Public Economics* 68(3) : 335-367.
FERRER, C., 1998, "Patterns and Determinants of Location Decisions by French Multinationals in European Regions" in J.-L. Mucchielli, *Multinational Location Strategy*, Greenwich : JAI Press

FRIEDMAN, J., D. GERLOWSKI AND J. SILBERMAN, 1992, "What Attracts Foreign Multinational Corporations" *Journal of Regional Science* 32(4) : 403-418.

FUJITA, M. AND J.F. THISSE, 1996, "Economics of agglomeration" *Journal of the Japanese and International Economies* 10(4) : 339-378.

FUJITA, M. AND J.F. THISSE, 1999, *Economics of agglomeration*. Manuscript

GUIMARÃES, P., O. FIGUEIREDO AND D. WOODWARD, 2000, "Agglomeration and the Location of Foreign Direct Investment in Portugal" *Journal of Urban Economics* 47(1) : 115-135.

HANSEN, E.R., 1987, "Industrial Location Choice in Sao Paulo, Brazil, a Nested Logit Model" *Regional Science and Urban Economics* 17(1) : 89-108.

HARRIS, C., 1954, "The Market as a Factor in the Localization of Industry in the United States" *Annals of the Association of American Geographers* 64 : 315-348.

HEAD, K. AND J. RIES, 1996, "Inter-city Competition for Foreign Investment : Static and Dynamic effects of China's Incentive Areas" *Journal of Urban Economics* 40(1) : 38-60.

HEAD, K. AND J. RIES, 1998, "Immigration and Trade Creation: Econometric Evidence from Canada" *Canadian Journal of Economics* 31(1) : 47-62.

HEAD, K., J. RIES AND D. SWENSON, 1995, "Agglomeration Benefits and Location Choice : Evidence from Japanese Manufacturing Investment in the United States" *Journal of International Economics* 38(3-4) : 223-247.

HEAD, K., J. RIES AND D. SWENSON, 1999, "Attracting Foreign Manufacturing: Investment Promotion and Agglomeration" *Regional Science and Urban Economics* 29(2) : 197-218.

HENDERSON, J.V. AND A. KUNCORO, 1996, "Industrial Centralisation in Indonesia" *World Bank Economic Review* 10(3) : 513-540.

HIRSCH, S., 1976, "An International Trade and Investment Theory of the Firm" *Oxford Economic Papers* 28(2) : 258-270.

KRUGMAN, P.R., 1991, *Geography and Trade*, Cambridge : MIT Press.

MARSHALL, A., 1920, *Principles of Economics*, London : Macmillan.

MAYER, T. AND J.L. MUCCHIELLI, 1999, "La localisation à l'étranger des entreprises multinationales : une approche d'économie géographique hiérarchisée appliquée aux entreprises japonaises en Europe" *Économie et Statistique* 326-327 : 159-167.

MCFADDEN, D., 1984, "Econometric Analysis of Qualitative Response Models" in Griliches Z. et M.D. Intriligator, it Handbook of Econometrics vol 2, Amsterdam : Elsevier/North-Holland.

RAUCH, J.E., 1999, "Networks versus Markets in International Trade" *Journal of International Economics* 48(1) : 7-35.
VENABLES, A.J., 1996, "Equilibrium Locations of Vertically Linked Industries" *International Economic Review* 37(2) : 341-359.

WHEELER, D. AND A. MODY, 1992, "International Investment Location Decisions" *Journal of International Economics* 33 : 57-76.
# Appendix: Location choice determinants by industry

<table><caption>Table 7: Most agglomerated industries.</caption><thead><tr><th>Industry</th><th>N. obs.</th><th>NH</th><th>NO</th><th>NF</th><th>MARK</th><th>W</th><th>DIS</th></tr></thead><tbody><tr><td>Office machinery</td><td>9</td><td>3.61**</td><td>3.85**</td><td>5.09***</td><td>-1.64*</td><td>0.91</td><td>-1.9</td></tr><tr><td>Lightning</td><td>19</td><td>2.44**</td><td>2.25***</td><td>1.91***</td><td>-1.26**</td><td>0.79</td><td>-1.44*</td></tr><tr><td>Machine tools</td><td>23</td><td>1.64*</td><td>1.36***</td><td>1.29***</td><td>0.76**</td><td>-0.32</td><td>-1.19**</td></tr><tr><td>Misc. rubber</td><td>36</td><td>1.86***</td><td>0.92***</td><td>1.3***</td><td>0.48</td><td>-0.65</td><td>-0.91</td></tr><tr><td>Computers</td><td>71</td><td>1.16***</td><td>1.05***</td><td>1.35***</td><td>-0.35</td><td>0.99**</td><td>0.43</td></tr><tr><td>Lifts</td><td>85</td><td>1.28**</td><td>0.44*</td><td>1.61***</td><td>-0.27</td><td>-1.05*</td><td>-0.14</td></tr><tr><td>Paints</td><td>65</td><td>0.93**</td><td>0.85***</td><td>1.28***</td><td>-0.09</td><td>-0.12</td><td>-0.06</td></tr><tr><td>Car parts</td><td>115</td><td>0.51**</td><td>0.29*</td><td>0.93***</td><td>0.19</td><td>0.75</td><td>-0.72*</td></tr></tbody></table>

Note: ***, ** and * respectively denote significance at the 1%, 5% and 10% level.

We provide here the complete list of industry level results. Tables 7 and 8 list the coefficient on each variable or the 76 industries where estimation was possible.

<table><caption>Table 8: Location patterns of other industries.</caption><thead><tr><th>Industry</th><th>N. obs.</th><th>NH</th><th>NO</th><th>NF</th><th>MARK</th><th>W</th><th>DIS</th></tr></thead><tbody><tr><td>Springs</td><td>11</td><td>3.45*</td><td>0.82</td><td>3.62***</td><td>0.03</td><td>-4.03**</td><td>0.81</td></tr><tr><td>Plaster for construction</td><td>7</td><td>4.16**</td><td>1.09</td><td>2.58***</td><td>-0.25</td><td>1.4</td><td>-0.93</td></tr><tr><td>Paste and Glue</td><td>21</td><td>1.42</td><td>1.66**</td><td>3.28***</td><td>-0.85*</td><td>2.9*</td><td>-1.39**</td></tr><tr><td>Transmission parts</td><td>17</td><td>2.71</td><td>1.93***</td><td>1.68***</td><td>0.25</td><td>3.25</td><td>0.03</td></tr><tr><td>Basic plastics</td><td>17</td><td>2.31***</td><td>0.53</td><td>2.31***</td><td>1.01</td><td>0.35</td><td>-2.76***</td></tr><tr><td>Screws</td><td>7</td><td>2.28</td><td>1.36</td><td>1.33**</td><td>0.79</td><td>-0.18</td><td>-2.67*</td></tr><tr><td>Soap</td><td>17</td><td>2.35**</td><td>0.43</td><td>2.17***</td><td>-0.4</td><td>2.36</td><td>-1.58*</td></tr><tr><td>Small metal articles</td><td>10</td><td>1.41</td><td>1.91</td><td>1.49**</td><td>-0.03</td><td>0.22</td><td>-3.6***</td></tr><tr><td>Cardboards</td><td>11</td><td>2.68*</td><td>0.36</td><td>1.69***</td><td>-0.14</td><td>1.76</td><td>-0.7</td></tr><tr><td>Curved Glass</td><td>8</td><td>4.17*</td><td>-2.4</td><td>2.68***</td><td>-1.94**</td><td>-5.63**</td><td>-3.77**</td></tr><tr><td>Industrial Gaz</td><td>50</td><td>2.09**</td><td>0.44</td><td>1.85***</td><td>-0.38</td><td>-1.05</td><td>0.3</td></tr><tr><td>Cables and Wires</td><td>20</td><td>1.97**</td><td>0.69</td><td>1.33***</td><td>0.1</td><td>-1.41</td><td>-0.18</td></tr><tr><td>Agricultural Machinery</td><td>11</td><td>1.95</td><td>0.36</td><td>1.59***</td><td>1.03*</td><td>-0.71</td><td>1.2</td></tr><tr><td>Navigation equipment</td><td>26</td><td>1.49</td><td>1.34**</td><td>0.94***</td><td>-0.38</td><td>-0.71</td><td>-1.25**</td></tr><tr><td>Small gen, eng and transf.</td><td>9</td><td>0.04</td><td>1.38</td><td>2.26***</td><td>-1.79**</td><td>2.55</td><td>-3.01**</td></tr><tr><td>Misc elec. mat</td><td>12</td><td>1.59</td><td>1.03</td><td>0.97</td><td>0.48</td><td>1.18</td><td>0.5</td></tr><tr><td>Car bodies</td><td>21</td><td>1.22</td><td>1.1*</td><td>1.08***</td><td>0.52</td><td>1.33</td><td>-0.22</td></tr><tr><td>Dom. elec. appliances</td><td>13</td><td>-0.24</td><td>1.86**</td><td>1.6***</td><td>-0.68</td><td>3.5***</td><td>-0.35</td></tr><tr><td>Mechanical handling eq.</td><td>27</td><td>0.83</td><td>0.98***</td><td>0.98***</td><td>0.46</td><td>0.05</td><td>-0.51</td></tr><tr><td>Active electronic comp.</td><td>21</td><td>-0.12</td><td>1.08**</td><td>1.74***</td><td>1**</td><td>0.19</td><td>0.77</td></tr><tr><td>Locks</td><td>19</td><td>1.08</td><td>0.31</td><td>1.19***</td><td>0.75**</td><td>-1.61</td><td>-0.91</td></tr><tr><td>Tabs</td><td>27</td><td>1.29</td><td>0.31</td><td>0.96***</td><td>0.05</td><td>2.07</td><td>-0.04</td></tr><tr><td>Office furniture</td><td>20</td><td>2.32**</td><td>-0.34</td><td>0.51</td><td>1.05**</td><td>-1.05</td><td>-0.88</td></tr><tr><td>Essential oils</td><td>26</td><td>0.05</td><td>1.07***</td><td>1.32***</td><td>1.38***</td><td>-1.05</td><td>0.28</td></tr><tr><td>Mechanical tools</td><td>51</td><td>0.84</td><td>0.83***</td><td>0.63***</td><td>0.12</td><td>-0.24</td><td>-0.21</td></tr><tr><td>Industrial chemicals</td><td>47</td><td>0.06</td><td>0.93***</td><td>1.25***</td><td>-0.03</td><td>2.38**</td><td>-0.79</td></tr><tr><td>Shoes</td><td>19</td><td>1.21</td><td>0.4</td><td>0.6***</td><td>1.33***</td><td>-1.31</td><td>-1.1</td></tr><tr><td>Transmission equipment</td><td>20</td><td>0.33</td><td>-0.01</td><td>1.74***</td><td>0.36</td><td>0.94</td><td>4.28**</td></tr></tbody><tfoot><tr><td colspan="8">continued on next page</td></tr></tfoot></table>
continued from previous page

<table><thead><tr><th>Industry</th><th>N. obs.</th><th>NH</th><th>NO</th><th>NF</th><th>MARK</th><th>W</th><th>DIS</th></tr></thead><tbody><tr><td>Batteries</td><td>22</td><td>-0.95</td><td>0.9</td><td>2.09***</td><td>-0.03</td><td>0.86</td><td>-1.2</td></tr><tr><td>Construction machinery</td><td>31</td><td>1.35*</td><td>-0.39</td><td>1.06***</td><td>0.53</td><td>0.3</td><td>-0.7</td></tr><tr><td>Food ind. machinery</td><td>51</td><td>0.58</td><td>-0.02</td><td>1.4***</td><td>0.42</td><td>0.96</td><td>-1.25**</td></tr><tr><td>Tech. plastic parts</td><td>45</td><td>0.81</td><td>0.22</td><td>0.94***</td><td>0.11</td><td>0.5</td><td>-1.03**</td></tr><tr><td>Metal treatment</td><td>29</td><td>-0.27</td><td>1.18***</td><td>1.03***</td><td>0.14</td><td>-0.87</td><td>-1.24**</td></tr><tr><td>Concrete for construction</td><td>29</td><td>0.27</td><td>0.73**</td><td>0.94**</td><td>0.69*</td><td>0.13</td><td>-1.35***</td></tr><tr><td>Low tension elec. eq.</td><td>68</td><td>0.42</td><td>0.66**</td><td>0.7***</td><td>0.54**</td><td>-1.25</td><td>-0.19</td></tr><tr><td>Perfume</td><td>49</td><td>0.36</td><td>-0.71**</td><td>2.09***</td><td>-0.1</td><td>0.57</td><td>-0.92</td></tr><tr><td>Glasses</td><td>12</td><td>-0.2</td><td>0.57</td><td>1.35***</td><td>0.26</td><td>3.5</td><td>-0.62</td></tr><tr><td>Men's apparel</td><td>10</td><td>0.71</td><td>-0.63</td><td>1.64***</td><td>0.22</td><td>-3.64**</td><td>-1.71**</td></tr><tr><td>Clothing accessories</td><td>28</td><td>0.23</td><td>0.07</td><td>1.4***</td><td>-0.68</td><td>1.81</td><td>-2.21***</td></tr><tr><td>Light metal packaging</td><td>14</td><td>0.19</td><td>-0.38</td><td>1.88***</td><td>1.17***</td><td>-2.16</td><td>-0.61</td></tr><tr><td>Cement</td><td>15</td><td>-0.36</td><td>0.86</td><td>1.14**</td><td>-0.17</td><td>1.29</td><td>-1.94***</td></tr><tr><td>Plastic for packaging</td><td>42</td><td>0.85</td><td>0.12</td><td>0.66***</td><td>0.76***</td><td>-0.79</td><td>-0.72</td></tr><tr><td>Telephones</td><td>32</td><td>-0.25</td><td>0.51</td><td>1.3***</td><td>-0.31</td><td>1.87*</td><td>-0.28</td></tr><tr><td>Pumps and compressors</td><td>49</td><td>-0.58</td><td>0.9***</td><td>1.22***</td><td>0.05</td><td>3.12***</td><td>0.51</td></tr><tr><td>Wood structure</td><td>8</td><td>1.36</td><td>0.2</td><td>-0.03</td><td>1.87***</td><td>-2.83</td><td>-3.56***</td></tr><tr><td>Misc. organic chemicals</td><td>19</td><td>-0.07</td><td>0.47</td><td>1.12***</td><td>0.89**</td><td>-0.5</td><td>-1.19*</td></tr><tr><td>Pharmaceuticals</td><td>126</td><td>0.22</td><td>0.43**</td><td>0.78***</td><td>0.32</td><td>0.5</td><td>-0.54</td></tr><tr><td>Scientific inst.</td><td>117</td><td>-0.09</td><td>0.27</td><td>1.06***</td><td>-0.09</td><td>1.3**</td><td>-0.87**</td></tr><tr><td>Agri. use chem.</td><td>37</td><td>-1.89</td><td>0.99**</td><td>2.09***</td><td>-0.43</td><td>2.09***</td><td>1.81**</td></tr><tr><td>Ind. refrigerating mach.</td><td>71</td><td>-0.56</td><td>0.4*</td><td>1.22***</td><td>-0.29</td><td>0.79</td><td>-0.52</td></tr><tr><td>Books editing</td><td>321</td><td>0.31</td><td>0.42***</td><td>0.2**</td><td>0.73***</td><td>-1.29***</td><td>-0.37*</td></tr><tr><td>Boilermaking</td><td>30</td><td>0.08</td><td>0.42</td><td>0.3</td><td>0.62*</td><td>2.23</td><td>-1.12**</td></tr><tr><td>Misc. machinery</td><td>25</td><td>1.46</td><td>-1.65</td><td>0.96***</td><td>0.45</td><td>-0.83</td><td>0.04</td></tr><tr><td>Clocks</td><td>13</td><td>-1.69**</td><td>0.26</td><td>2.08***</td><td>0.91</td><td>1.23</td><td>-2.63**</td></tr><tr><td>Passive electronic comp.</td><td>43</td><td>-0.55</td><td>-0.07</td><td>1.22***</td><td>0.47</td><td>1.15</td><td>-1.45*</td></tr><tr><td>Plastic tubes</td><td>46</td><td>-0.56</td><td>-0.11</td><td>1.24***</td><td>0.55**</td><td>-0.46</td><td>-0.62</td></tr><tr><td>Oil refining</td><td>184</td><td>-0.47</td><td>-0.06</td><td>1.07***</td><td>0.63***</td><td>-0.33</td><td>0.16</td></tr><tr><td>Plastics for construction</td><td>60</td><td>-0.78*</td><td>0.09</td><td>1.07***</td><td>0.74***</td><td>-1.39*</td><td>-1.71***</td></tr><tr><td>Hydraulic transmissions</td><td>40</td><td>-1.1</td><td>-0.1</td><td>1.58***</td><td>0.36</td><td>-0.77</td><td>-0.28</td></tr><tr><td>Paper and cardboard</td><td>17</td><td>-0.2</td><td>-1.07</td><td>1.54***</td><td>-0.41</td><td>3.04**</td><td>-1.52</td></tr><tr><td>Women's apparel</td><td>251</td><td>0.2</td><td>-0.26*</td><td>0.31***</td><td>1.09***</td><td>-0.93***</td><td>-0.1</td></tr><tr><td>Printing</td><td>37</td><td>-0.52</td><td>-0.2</td><td>0.96*</td><td>0.4</td><td>0.96</td><td>-1.65***</td></tr><tr><td>Other paper and cardboard art.</td><td>35</td><td>-2.09</td><td>0.72**</td><td>1.15***</td><td>0</td><td>-0.83</td><td>-0.78</td></tr><tr><td>General Machinery</td><td>28</td><td>-1.14</td><td>0.19</td><td>0.34</td><td>1.35***</td><td>-1.79</td><td>-1.7***</td></tr><tr><td>Silk manufacturing</td><td>13</td><td>-2.2</td><td>0.51</td><td>0.8*</td><td>1.18***</td><td>-2.67**</td><td>0.46</td></tr><tr><td>Newspapers editing</td><td>25</td><td>0.09</td><td>-0.28</td><td>-0.75</td><td>5.17***</td><td>1.66</td><td>0.29</td></tr><tr><td>Underwear</td><td>8</td><td>0.69</td><td>-1.89</td><td>0</td><td>2.98***</td><td>-3.85*</td><td>-1.49</td></tr><tr><td>Magazines editing</td><td>27</td><td>-1.16</td><td>0.2</td><td>-0.63</td><td>3.84</td><td>6.02*</td><td>-1.14</td></tr></tbody></table>

Note: ***, ** and * respectively denote significance at the 1%, 5% and 10% level.