Local Export Spillovers in France∗
Pamina Koenig†

Florian Mayneris‡

Sandra Poncet§

Abstract
This paper investigates the presence of local export spillovers on both the extensive (thedecision to start exporting) and the intensive (the export volume) margins of trade, using
data on French individual export flows, at the product-level and by destination country,
between 1998 and 2003. We investigate whether the individual decision to start exportingand exported volume are influenced by the presence of nearby product and/or destination
specific exporters, using a gravity-type equation estimated at the firm-level. Spillovers are
considered at a fine geographical level corresponding to employment areas (348 in France).
We control for the new economic geography-type selection of firms into agglomerated areas,
and for the local price effects of firms agglomeration. Results show evidence of the presenceof export spillovers on the export decision but not on the exported volume. We interpret
this as a first evidence of export spillovers acting through the fixed rather than the variable
cost. Spillovers on the decision to start exporting are stronger when specific, by product anddestination, and are not significant when considered on all products-all destinations. Moreover, export spillovers exhibit a spatial decay within France: the effect of other exporting
firms on the export decision is stronger within employment areas and declines with distance.

JEL Codes: F1, R12, L25.
Keywords: firm-level export data, destination specific spillovers, agglomeration.

1

Introduction

The concern of French policy makers relative to the performance of French firms on international
markets is not new: back in 2003, the foreign trade minister allocated specific public spending
to the promotion of French exports on targeted markets. At the beginning of 2007, following the
publication of the French trade balance for 2006 showing a growing trade deficit, the existing∗
We thank Andrew Clark, Lionel Fontagné, Henry Overman, Jacques Thisse, Hylke Vandenbussche, participants of the 2008 “Empirical Investigations in International Trade” conference in Boulder, Colorado, the CEPII
seminar and the Glasgow “Spatial Economics and Trade” conference, and two anonymous referees for helpfuladvices. Funding acknowledgement: This paper is produced as part of the project European Firms in a Global
Economy: Internal policies for external competitiveness (EFIGE), a Collaborative Project funded by the European Commission’s Seventh Research Framework Programme, Contract number 225551. P. Koenig also thanks
the Cepremap, program ‘International trade and development’ for financial support.
†
University Paris West & Paris School of Economics, 48 boulevard Jourdan 75014 Paris. pkoenig@u-paris10.fr‡
Paris School of Economics, 48 boulevard Jourdan, 75014 Paris. mayneris@pse.ens.fr§
Université Paris I and CEPII, 9 rue George Pitard, 75015 Paris. sandra.poncet@cepii.fr

dispositive was backed up by measures facilitating the flow of export-specific information among
French firms (see the renewal of the Ubifrance device for example).
The idea behind such initiatives is that a better knowledge about foreign markets may have apositive impact at the microeconomic level on the export performance of firms. More generally,
information on international markets may originate from public interventions but also fromthe pool of existing exporters. Proximity to other exporters may bring benefits like positive
information externalities, cost-sharing opportunities and mutualized actions on export markets.
Several national and local organizations are specifically aimed at fostering these effects throughconferences on specific destination countries, financial support to participate to international
fares, etc. For example, the Chamber of Commerce of Val-de-Marne (a French département near
Paris) organizes gatherings dedicated to sharing export experiences. Its clubs Maghreb/MiddleEast and America propose specific support to member firms in their attempts to develop theirexport activities in these regions. The usefulness of such actions to develop trust and informal
relationships between local firms and successfully conquest new markets is attested by several
entrepreneurs. The CEO of “La Papeterie du Poitou”, member of the export club FuturExportin the Vienne département, for example acknowledges that “the experts invited to speak at
FuturExport’s meetings, thanks to the quality of their interventions, support SMEs in theirsteps to cross the borders and succeed at exporting”1 .
This paper builds on the existing literature analyzing the existence and the nature of local
export spillovers2 among exporters. Using a uniquely detailed dataset comprising French exports
at the product, firm, and destination country level for 1998-2003, we analyse the impact of thegeographical agglomeration of exporters on both aspects of firms’ export performance: theirexport decision (the extensive margin) and their export volume (the intensive margin).
From the theoretical point of view, few insights have been provided for export spillovers.
Building on network theory, Krautheim (2008) shows how the exchange of information between
firms exporting to the same country reduces the individual fixed cost to export and increases theprobability to export. As far as the intensive margin is concerned, Rauch and Watson (2003)
show that when a commercial relationship begins, there might be uncertainty for the buyer on
the ability of the supplier to successfully fill larger orders. The agglomeration of exporters canincrease the buyer’s information on the quality of the suppliers, favoring larger orders and hence
more important exports at the firm-level.
Export spillovers have been mostly studied in empirical papers. Results show mixed evidence
on the existence of export spillovers. However, existing studies looking for export spillovers
differ in several important aspects, among which the definition of export spillovers (restricted tomultinational firms or including all exporters), or the level of data disaggregation (exporters inthe same regional location or in the whole country). Aitken, Hanson, and Harrison (1997) study1

http://www.futurexport.com/Default.asp?module=temoignages
In this paper, we are interested in the broad effect, encompassing informational externalities and costmutualization economies, that agglomeration of exporters has on export performance of firms. In the following,
we use the terms export agglomeration economies and export spillovers interchangeably.
2the export behavior of Mexican plants and find that the probability that Mexican plants export
in 1986 and 1989 is positively linked to the presence of multinational firms in the same state, but
uncorrelated to the presence of exporters in general. Greenaway, Sousa and Wakelin (2004) showthat multinational firms located in the UK influence positively the export decision of domestic
firms over 1993-1996. Further export spillovers from FDI are investigated by Kneller and Pisu
(2007) on UK data from 1992 to 1999, who find that the presence of foreign multinationals inthe same industry or region affect positively the intensive and extensive margins of trade. Veryrecently and also on UK data, Greenaway and Kneller (2008) show that regional and sectoral
agglomeration is beneficial to the entry of new firms on export markets during 1988-2002. Two
papers underline the absence of evidence of export spillovers. Barrios, Görg and Strobl (2003)
study the export decision and the export intensity of Spanish firms between 1990 and 1998 anddo not find evidence that Spanish firms benefit from spillovers through the presence of otherexporters or multinational firms. Bernard and Jensen (2004) find no role for export spillovers on
the export decision in a panel of U.S. manufacturing firms, be the exporters region-specific but
outside the industry, industry-specific but outside the region, or region and industry-specific.
It appears that the existing literature has only been able to look at a reduced set of questions,surely because of the lack of detailed data both on the location of exporters and the destinationof their exports. Important issues are for example the nature of export spillovers (are theyspecific to the product, or the destination country) and the channel through which they impact
a firm’s behavior (through a productivity effect or through a trade cost effect, and in thelatter case, through the variable or the fixed cost). Recently, Koenig (2009), analyzing theindividual decision to start exporting to a given country, uses French firm-level exports anddetailed geographical information on exporters for the period 1986-1992. She identifies positive
export spillovers from neighboring exporters at a detailed geographical level and finds that themechanism is clearly destination-specific.
In the line of these results, the contributions of our paper are threefold. First, we build ouranalysis on a uniquely detailed dataset comprising French firm-level exports by 8-digit productand by destination country, for a recent period of time (1998-2003). With respect to the existing
literature, the product dimension allows us to investigate spillovers at a more adequate levelin terms of activity3 and the destination-country dimension provides us valuable information
to assess the nature of spillovers, as suggested by Krautheim (2008). Second, we explore theimpact of export spillovers on both the decision of firms to start exporting abroad and the volume
exported by each firm. As explained in Chaney (2008), a change in the fixed cost is expected toaffect the extensive margin of trade only. A change in the variable cost is expected to affect boththe intensive and the extensive margins. Our analysis thus allows to provide the first empiricalevidence to disentangle these channels. Third and finally, we wish to describe in details theeffect of exporting firms’ agglomeration on the export behavior of individual exporters. The
global picture states indeed that the agglomeration of exporters in the same area may give rise3

Industry classifications regroup very different producers under a same heading.

to market and non-market externalities, but also to higher competition on the export market.
An example of market externality is the cost-sharing devices that allow firms to communicate
together on their products to foreign consumers4 . Non-market externalities involve informal
information transfers, which may benefit local firms through a decrease in variable or fixedexport costs. Our estimation procedure captures those two types of externalities, as well as
the competition effect on the exported good’s market. We measure the net effect of positive
externalities and higher competition associated with the agglomeration of exporters.
Our results show a positive effect of product and destination specific-exporters’ agglomeration on the export decision, hence on the extensive margin, but not on the intensive marginof trade. Export spillovers are prevalent when considered destination or product specific, arestronger when destination and product specific, and exhibit a spatial decay within France. The
effect remains through numerous robustness checks. The paper is structured as follows. Section(2) presents the empirical strategy and estimation issues. In section (3), we present the export
and firm data and show some descriptive statistics on exporters. Section (4) contains the results
for the decision to start exporting and the export volume, and section (5) concludes.

2

Empirical strategy

The structure and the determinants of international trade flows are now commonly studied using
gravity equations. We detail the two estimated equations for the decision to start exporting andexport volume, both inspired by the gravity equation. Ideally, we would have liked to estimate
those two equations in an integrated Heckman selection model. This proved impossible sincesimilar explanatory variables are used in both the decision to start exporting and the exported
volume equations: we do not have any valid excluded variable for the selection equation. Moreover, the Heckman procedure does not allow the inclusion of the fixed-effects needed to estimate
correctly our model (see section 2.2). We consequently estimate successively our two equations.
We first go through the main estimation issues, among which the endogeneity problem, reverse
causality and omitted variables.

2.1

The empirical model

We assume that a firm i starts exporting a product k to a country j at time t if the actualized
sum of its profits abroad is positive, i.e. Uijkt = πijkt + ijkt > 0. Uijkt is the net export profit
earned by a firm on market j. It is the sum of the observed part of the profit, called πijkt , andthe unobserved part ijkt , where ijkt contains characteristics of firms, areas and destination
countries. The net export profit Uijkt increases with the supply and demand capacities ofrespectively the firm and the importing country. It decreases with bilateral trade frictions,among which distance between France and the destination country. Export spillovers is our4
See for example the Cosmetic Valley, a network of perfume and cosmetics producers located in Centre andNormandie regions, aimed at communicating on their know-how on international markets.

variable of interest. They are assumed to act through the cost variable, potentially lowering
either the variable or the fixed cost of exporting. The probability that a firm i starts exportinga product k to country j at time t writes:
Probikjt = Prob (α0 emplit + α1 demandjkt + α2 distj + α3 spillit + ikjt > 0) ,(1)

where emplit is the log of the number of employees of firm i at time t, demandjkt is the logof total imports of product k by country j at time t (in dollars), distj is the log of distancein kilometers between France and country j, and spillit is the spillover variable for firm i attime t. Note that our left-hand side variable is constructed as a change of export status at thefirm-product-country level, since it takes the value 1 when a firm exports product k to country
j at time t whereas it did not at time t − 1. We estimate this equation with a logit procedure,controlling for firm-product-country fixed-effects. Our effects are therefore estimated on thetime variation within a firm-product-country triad.
We model the individual export volume by adapting the traditional gravity equation at thefirm-level: everything equals, the larger i’s supply potential and j’s demand potential, and thelower bilateral trade costs, the more firm i will export to country j. After log-linearization of
the basic gravity equation, the estimated equation is:
expikjt = β0 emplit + β1 demandjkt + β2 distj + β3 spillit + νikjt ,(2)where expikjt is the log of the volume of exports of product k from firm i to country j at timet (in tons).
Our variable of interest in both estimations is export spillovers, i.e. the effect of exporters’
agglomeration in the same area on the export behavior of a given firm. As detailed in the
introduction, among the indirect effects of firms’ agglomeration are market and non-market
interactions. Hence, in the estimation, the spillover variable will capture not only the flow of
information among neighboring firms but also the fact that agglomerated exporting firms areable to mutualize the costs related to export activity like management of relationships withclients or communication on their product for instance. We are thus studying the presence ofa broader microeconomic phenomenon which the literature has come to call spillovers. The
construction of the spillover variable will be detailed in the next section.
Finally, in equation (1) and (2), ikjt and νikjt are supposed to be i.i.d disturbances. In
the following we discuss some considerations about why one can have serious doubts about theorthogonality of the unobserved terms and the regressors.

2.2

Estimation issues

If there are export spillovers, the number of neighboring exporting firms should have a positive
influence on the export decision of a given firm i to country j at date t and/or on its volume
of exports. However, in order to be sure of the causality, several estimation issues need to becovered.
2.2.1

Reverse causality and simultaneity biases

Equations (1) and (2) both suffer from a patent endogeneity problem. Bernard and Jensen (1999)
show that good firms become exporters (exporting firms are ex-ante bigger, more productive
and pay higher wages than the others); but also that exporting raises ex-post employment
growth rates, for example. The sense of the causality between firms’ size and their export
behavior is consequently not clearly determined. Besides, an entrepreneur anticipating positive(or negative) demand shocks on export markets could hire (or lay off) employees to adapt its
supply capacity to demand. We thus face a reverse causality and a simultaneity issue relative
to firm characteristics variables.
Parallel issues can be raised on the spillover variable. If firm i’s export behavior depends
on the surrounding firms’ behavior, the latter is itself impacted by firm i’s export performance,
which induces a reverse causality problem. Further, simultaneity may be an issue, since unobserved supply-side or demand-side shocks could affect both the export performance of firm iand the performance of its neighbors. To make up for the potential circularity and simultaneity
problems, following Bernard and Jensen (2004), we lag all right-hand side variables one year.
2.2.2

Omitted variables

Melitz and Ottaviano (2008) provide a first important reason why the link of causality between
agglomeration of firms and the export performance of a given individual firm could be altered.
They show that larger and more integrated markets exhibit in equilibrium more productive firms
and lower markups, due to endogenous differences in the toughness of competition. Since only
productive firms are able to face the higher competition, there is a selection of most productive
firms in denser areas. Besides, the existence of Marshallian externalities can also explain that theagglomeration of firms in the same industry generates productivity gains. Martin, Mayer andMayneris (2008) show on French data that agglomeration affects positively firms’ productivity.
Hence, on the one hand, firms in agglomerated areas are more productive, because of a selection
effect or due to a marshallian externalities. On the other hand, more productive firms export
more. Omitting firm productivity could lead to an overestimation of export spillovers. We
thus introduce a TFP variable5 in our regression (see subsection (3.2) for more details on theestimation of firms’ TFP).
A second important concern refers to the reverse causality between the agglomeration variable and the export performance. Do firms export more because they are agglomerated or
are they agglomerated because they export more? To export easily, you need, among others,
airports, railroads or highways. All the areas are not equally endowed in transportation infras5

Firm’s TFP and the spillover variable is weekly correlated, 0.003 for the variables expressed in differences tothe mean and 0.002 for the variables in levels.

tructures; Therefore, our regression should control for time invariant geographic characteristics
by area fixed-effects.
A further issue relates to the economic size of the area. Agglomerated areas are also areas
where local demand is higher. As it is less costly to serve local than foreign markets, all else
equal, in agglomerated areas, firms could tend to serve in priority local consumers. Moreover,everything equals, larger areas in terms of number of producers are subject to larger congestion
effects on the use of local input, which could negatively impact firms’ export performance. If
the spillover variable is positively correlated to the size of the area, the absence of control would
downward bias our estimation of export spillovers. We introduce the total number of employees
in the area, which captures the crowding-out effect on the use of local amenities by a large
number of firms, as well as the effect of local demand. We expect its coefficient to be negative.
Next, it is possible that omitted components of trade costs create the observed relationshipbetween agglomeration of exporters and firms’ export performance. The existence of a common border between the local area and the destination country, or the presence of immigrants
networks could for example explain why there are a lot of firms located in Alsace that tradeintensively with Germany. This area-country specificity will be controlled by fixed-effects.
Finally, an important theoretical literature is now developing on multi-product firms andinternational trade. Empirical evidence acknowledge that exports in most countries are mostly
due to multi-products firms, characterized by a main export product and several side export
products. Bernard et al. (2006) develop a model in which they distinguish firm-level overall
ability and firm-product expertise. Ability and expertise both determine the export behaviorof the firm at the product level. In our data, we control for firm-level TFP, which is a goodproxy for firm-level ability, however we lack firm-product expertise. In the case firms with highproduct expertise are all located in the same place6 , this could upward bias the estimation of
spillovers. Figure (1) displays a very strong geographic concentration of exports7 for different
2-digit products which corresponds to well-known industrial local specialisations and reflects
the historical development of a product-specific expertise in these areas (clocks and watches
in Franche-Comté for example or textile in Northern France). In order to disentangle thoseinherited product specific regional patterns from export externalities, we control for firm-productfixed characteristics.
Our preferred regression contains firms’ TFP and the size of the area. Moreover, the appropriate specification discussed above includes a firm-product-country fixed-effect which willcontrol for all the above-mentioned observable and unobservable time-invariant components.
6
Because they depend on natural resources or, in a marshallian view, because they need specialized services,
employees and know-how which are geographically very localized for example.
7
The maps highlights the share of each employment area in total exports by single-plant firms over 20 employees.

Chapter 19
Preparations of cereals, flour, starch or milk ; pastrycook products

Chapter 53
Other vegetable textile fibres, paper yarn and woven fabrics of paper yarn

12%

26%

10%

21%

5%

11%

1%

0%

0%

Chapter 91
Clocks and watches and parts thereof

54%
30%
<6%

Figure 1: Share of each employment area in total French exports by single-plant firms over 20
employees

3

Data and descriptive statistics

We explain how we build the final database by merging export data and firm data. We thendetail the construction of the variables. Descriptive statistics follow, on the representativenessof the database and on the sample of exporters.

3.1

Sources

The main data source is a database collected by the French Customs.8 It contains French export
flows aggregated by firm, year, product (identified by a 8-digit code) and destination country,
over the 1998-2003 period.9 As it does not provide information on the size of firms and on
their location, we use a second data source, the French Annual Business Surveys (Enquêtes
Annuelles d’Entreprises.) for the manufacturing sector over the same period, provided by the
French ministry of Industry. Those surveys contain information on firms over 20 employees10
such as the address, the identification number of the firm (siren), sales, production, number of
employees, and wages.
The address of the firm is detailed up to the street name so that we can choose to investigate
for export spillovers at different geographical scales. Two administrative levels coexist in France,
the region (22 in metropolitan France) and the département (96), the latter being included in
the former. The employment area (341 in continental metropolitan France) is an additional
level used by the French statistical institute, which perimeter is based on workers’ commuting
schemes.11 Because of their economic and non-administrative definition, in the following wechoose to work at the employment area level, which we will simply call areas. Areas fit into
regions but overlap with départements. Basic checks consist in dropping firms declaring negative
sales, value added or employment. We also drop firms which change location during the period.
Indeed, we do not know whether this is an error or an actual move of the firm and we want to be
sure that the firm-product-country fixed effect also captures area unobservable characteristics.
Finally, firms located in Corsica or in overseas departments are left out of the sample.
Merging the firm information with the export data raises an important question relative tothe sample of exporters. First, our sample covers manufacturing firms larger than 20 employees
since the Annual Business Surveys do not provide information on small firms. Second, theexport dataset gives the identification number of exporting firms however without detailing the
8

Within the EU, French customs collect information on the product (NC8 categories) exported by firms when
the annual cumulated value of all shipments of a firm (in the previous year) is above 100,000 euros from 2001
onwards. This threshold was 99,100 euros in 2000 and 38,100 euros before. For extra-EU exports, all shipments
above 1,000 euros are reported. As regards intra-EU exports, we consequently restrict our attention to flows fromfirms with an annual cumulated value of intra-EU15 shipments above 100,000 euros in order to avoid the bias
due to the evolution in the reporting thresholds imposed to exporting firms by the French customs.
9
We drop product lines that changed classification at the 4-digit level over the period.
10
Smaller firms can also figure in these surveys, if their sales amount at least to 5 millions euros.
11
In the sample used in our regressions, 340 employment areas are included, for which the average surface areais 1570 km2 . Assumingthat employment areas are circular, we compute that the average internal distance, i.e.
pradius which is Surf ace/π, is 23 kilometers.

plants from which the flows originate. Since spillovers are evaluated as the number of exportingneighbors next to the exporting firm, we face an important issue concerning multi-plant firms.
This is why, among exporting firms represented both in the Customs’ data and in the Annual
Business Survey for the manufacturing sector, we choose to keep single-plant firms only, bothfor the left-hand side variable and for the definition of spillovers. Hence for a given single-plantfirm, we evaluate the impact of other neighboring exporting single-plant firms on its export
performance. The restriction of our sample makes sense in the light of a number of French
public reports, which emphasize that the firms which encounter difficulties in entering anddeveloping on international markets, and who are interested in support to export activities, arethe small and medium ones (see for example Artus and Fontagné (2006)).
Another possibility is to consider that all multi-plant firms’ export flows originate from thefirm’s headquarter. Spillover variables for these firms are then computed as the number ofneighbors in the headquarters’ area. As explained more in details in Section (4.2.2) our results
are robust to this alternative specification.

3.2

Variables

The dependent variables are as follows. For the extensive margin, we use a dummy variablewhich takes the value 1 if the firm starts exporting product k at time t to country j and 0
otherwise. We restrict our sample to firm-product-country series of zeros followed by a decision
to start exporting.12 We construct a specific database, incorporating the set of alternatives facedby each firm. These are defined as the product-country couples for which we observe at least
one export start during the 1998-2003 period. Since we use firm-product-country fixed-effects,
taking into account a broader definition of possible exported products or destination countries
would not change the sub-sample used for the estimation. For the intensive margin, we use
the volume of exports, expressed in tons, at the firm-product-country level. We use the export
volume instead of the export value in order to avoid firm-level quality sorting and pricing issues
mentioned in Crozet, Head and Mayer (2009).
The next step consists in building the export spillover variables. These are built at a detailed
geographic and sectoral disaggregation level, using the French Business Annual Surveys. The
geographic disaggregation chosen is the employment area; the manufacturing disaggregationlevel is the 4-digit product (1236 products) nomenclature. A 4-digit nomenclature is a rather
fine decomposition. As an illustration, the chapter 91 (2-digit), which corresponds to clocks
and watches and parts thereof, is decomposed into 14 different 4-digit products, differentiating
wrist-watches in precious metal from wrist-watches in base-metal, alarm clocks, wall clocks, andtime registers. Components disentangles clock movements, watch cases and watch straps. We
compute the spillover variable as the number of exporting plants (hence firms, because theseare single-plant firms) in the area. In each case, spillovers can be of four different natures. For
12

For a given firm-product-country we can have several starts. For example, the subsequent export statuses
011001 become in our sample .1..01, with . denoting a missing value.

each firm and each year, we define general spillovers (the number of other exporting firms in
the area), destination specific spillovers (the number of other firms of the area exporting to thesame destination), product specific spillovers (the number of other firms of the area exporting
the same product) and product and destination specific spillovers (the number of other firms in
the area exporting the same product to the same destination). Our sample covers 197 countries.
The product and destination spillover variable for firm i, located in area z, exporting productk to country j at time t is defined as follows:
spillizkjt = # of other exporting firmsizkjt(3)

While the other explanatory variables are expressed in logs, the spillover variable is used inlevels (see end of section 4.1 for a discussion of this point). The size of the area is measured by
the total number of employees in the area at year t, estimated by the French Statistical Institute(INSEE) from the 1999 French census. The TFP variable is obtained through the estimationof a production function using the approach developed by Levinsohn and Petrin (2003), whichattempts to solve both omitted variables (ability or risk aversion of the entrepreneur for example)
and simultaneity bias (anticipation of macroeconomic shocks that could affect the choice of thelevel of inputs made by the entrepreneur) the estimation of firm-level productivity usually suffersfrom. We use the Annual Business Surveys to estimate Cobb-Douglas production functions at
the 2-digit industry level. The observations for which value-added, employment or capital ismissing, negative or null13 are dropped. We deflate value-added data by a branch price-index
and capital data by a an investment price index valid for all industrial sectors. Production
functions are estimated on single plant firms only first, and then on the whole sample for ourrobustness checks. The coefficients we obtain are credible, lying between 0.1 and 0.3 for capitaland 0.6 and 0.8 for labor according to industries, the sum of both being generally slightly below
1. Note that all our results are not sensitive to alternative TFP measures (OLS and GMM).
Last, we add the variables concerning destination countries. Distance between France andeach destination country is provided by CEPII14 . The demand variable gives, for each importing
country, total imports from all over the world by product. In our estimation, it consequently
controls for aggregate demand shocks specific to the product and the destination country. It
is issued from the BACI database, a CEPII world database for international trade analysis at
the product-level, detailed in Gaulier and Zignago (2008). All monetary variables are converted
into dollars. At each one of these steps, observations are lost because of imperfect merges, but
in reasonable proportions. The final database is an unbalanced panel.
13

We also dropped outliers, dropping 1% extremes for the following variables: capital intensity, yearly capital
growth rate, yearly employment growth rate, yearly average work productivity growth rate, yearly average capitalintensity growth rate.
14
Centre d’études prospectives et d’informations internationales, the French research center in International
Economics, http://www.cepii.fr

3.3

Descriptive statistics

Tables (1) and (2) provide summary information on the firms in our database. Table (1) explains
the representativeness of our sample of exporters, which is quite reasonable. Our regressions aredone on exports by manufacturing single-plant firms larger than 20 employees. These accountfor nearly 12 % of total French exports (in value), 9.5% of total French exports (in volume)and 9% of the total number of French exporters. In addition, we evaluate in the last row theshare of our firms (manufacturing single plant firms) in all manufacturing firms of more than 20
employees. Our sample represents 65.5% of large manufacturing exporters, and 23.5% of theirexports in value (22.5% for the volume).
Table (2) describes the sample used to estimate the decision to start exporting. Firms employ
77 workers on average over the period. This number is upper-bounded by the exclusion of multiplant firms, and lower-bounded by the reduction of our sample to exporting firms represented
in the Annual Business Surveys, which mainly cover firms over 20 employees. The table further
shows that firms export in average 11 products, and that each firm sells on average to nearly
11 countries. These relatively high numbers reflect the firm-level threshold of 100,000 euros
of intra-EU15 shipments used by French customs (refer to footnote 7). The lower part of thetable reports the values of the export spillover variables. The more specific by product and/or
by destination is the variable, the smaller is the mean. There are for example on average 59
exporting neighbors in the same area, when considering firms exporting all types of products
to all possible destination countries. Considering only firms in the same product category andfacing the same destination country, there are on average only 0.47 exporting neighbors in thesame area. This very low number is not surprising given the high product and geographicallevel of disaggregation.
Table (3) further stresses that for nearly 85% of the observations, there is no neighboringfirms exporting the same product to the same country as the firm under scrutiny. In 9.4% of
the cases, there is only one other exporting neighbor (to the same country-product pair) in thesame area. The likelihood of having at least one exporting neighbor increases from 15.2% when
the definition of spillovers is product-destination specific to 56.9% when it is product-specific,
to 87.9% when it is destination-specific and to 99.9% when it is defined as all products-all
destinations.

4

Results

The identification of spillovers on the decision to start exporting relies on a conditional logit estimation, whereas spillovers on firms’ export volume are estimated with a linear model. Moulton(1990) showed that regressing individual variables on aggregate variables could induce a downward bias in the estimation of standard-errors. All regressions are thus clustered at the arealevel.
Estimation results on the identification of export spillovers are presented for the decision tostart exporting in Table (4) and discussed in section (4.1). Further results on the decision tostart exporting figure in Tables (5) to (12) and are examined in section (4.2). Estimation resultson the export volume are displayed in Tables (13) to (15) and explained in section (4.3).

4.1

Identifying spillovers on the decision to start exporting

Table (4) details the estimation procedure to identify export spillovers on the individual decision
to start exporting at year t. Regressions are performed using the product and destination specificspillover variable. From left to right, each column contains more control variables. All right-handside variables are lagged one year. All regressions contain firm-product-country fixed-effects.
Since we drop firms that change location, the firm dimension of the triadic fixed-effects allows
to account for the characteristics of local areas such as transport infrastructures. First (natural)and second nature (human-made) local comparative advantages, according to Krugman’s 1992
terminology, could explain the agglomeration of firms together with the fact that exporting
firms are numerous. The product-country dimension of the triadic fixed-effects allows to control
for mean effects in each product line, as well as for the degree of competition in the destination
market. Note that the firm-product-country fixed-effect makes the use of the distance variable
not applicable because the distance between France and the destination country is invariant
across time. Finally, the triadic fixed-effects allow to control for inter-firm heterogeneity withina given area, as well as for firm-country and firm-product heterogeneity. The only remaining
variability is in the time dimension within a given firm-product-country triad. We are thus
estimating the effect of a change in the spillover variable in time on a change in the decision tostart exporting. More precisely, the firm-product-country fixed-effect computes the difference
to the mean within the firm-product-country triad. Hence, we are evaluating the effect of achange in the level of agglomeration in time, with respect to the average level of agglomeration
over the entire period, at the firm-product-country level.
Column 1 displays the basic estimation of the determinants of the decision to start exportingat the firm-level. Column 2 adds the spillover variable. Its coefficient appears positive andsignificant, however this variable captures the overall effect of agglomeration on the decision
to start exporting, without any control for omitted variables nor reverse causality. Column 3
introduces the productivity of the firm in order to control for the fact that more productive firms
are more often exporters and locate in agglomerated areas. The inclusion of the productivity
variable, which coefficient appears positive and significant, does not however affect the coefficienton the spillover variable. The coefficient on the spillover variable remains positive and significant.
In column 4 we add total employment in the area. This variable has an insignificant effecton the decision to start exporting. Its inclusion does not affect the coefficient on the spillover
variable. It remains significant and positive with a coefficient equal to .051, which meansthat when the number of neighboring exporters increases, positive externalities dominate thenegative competition effect on the decision to start exporting. An additional neighbor increases

the probability to start exporting by roughly 1.07 percentage point.15 With the controls we haveused for product, area, and country unobserved characteristics, as well as firm productivity andarea size, the agglomeration of exporting firms has a positive impact on the decision to start
exporting of a given firm in the same area.
Column 5 investigates whether the effect of a higher number of neighbors exporting the same
product to the same destination remains significant for the top end of our sample in terms ofnumber of neighbors. The sample is restricted to observations for which the number of firms in
the area exporting the same product to the same destination is greater than 3. The number ofobservations drops sharply from 645,268 to 9,007 but the explanatory power of the regression
increases from 9.17 to 14.42%. The impact of spillovers declines but remains significant andpositive with a coefficient equal to .033 suggesting that the effect measured in column 5 does
not only reflect the case of firms starting to export because the number of neighbors increases
from 0 to 1. Export spillovers persist for firms surrounded by four or more neighbors.
Table (A-1) in the Appendix further investigates the appropriateness of the linear specification of the spillovers. Column 1 of Table (A-1) replicates, for comparison, the results of column
4 in Table (4), hence our preferred specification using the most specific export spillover variable.
The linearity of the spillovers effect is investigated in the three remaining columns of Table(A-1). In column 4, the sample is restricted to observations for which the number of firms in
the area, same product - same destination, is greater than three. Column 2 uses dummies fordifferent levels of the spillover variable. Results are coherent with a linear specification since
the effect on starting to export of having one neighbor exporting the same product to the same
destination compared to zero (0.072) is very similar to the effect of having two neighbors insteadof one, and of having three neighbors instead of two. Column 3 further highlights that firms
with at least one neighboring exporter have a greater probability (+1.68 percentage point)16 tostart exporting than firms with no exporting neighbors.
Tables (4) and (A-1) allowed to investigate the presence of export spillovers. We now turnto further estimation results analyzing their nature and scope.

4.2

The nature of export spillovers on the decision to start exporting

We first investigate the specificity of export spillovers, and then address further issues about
the mechanisms at work.
15

This figure is obtained from the derivative of the choice probabilities. As stated in Train (2003), the change
in the probability that a firm i chooses alternative x (start exporting) given a change in an observed factor zi,x ,entering the representative utility of that alternative (and holding the representative utility of other alternatives(no exporting) constant) is βz Pi,x (1 − Pi,x ), with Pi,x being the average probability that firm i chooses alternativex (starts exporting). Our results, based on an average probability to start exporting of 30%, suggest that thederivative of starting exporting with respect to an additional neighbor is 1.07%=0.051×(0.30)×(1-0.30).
16This figure is the derivative of the probability of starting to export with respect to having a strictly positive
number of neighbors: 1.68%=0.08*(1-0.30)*(0.30).

4.2.1

How specific are export spillovers?

We continue exploring the existence of export spillovers by detailing their nature, i.e. whether
the effect remains when surrounding firms export different product lines, or when exporting
to different destinations. Results in Table (5) are performed using the preferred specification,
however with four different spillover variables: all products-all destinations, all products-same
destination, same product-all destinations, and same product-same destination. The general
spillover variable is not significant. While the three other spillover variables show positive andsignificant coefficients, the table displays a hierarchy ranking from .008 for country specificspillovers to .051 for product and country specific spillovers. It thus appears that the productspecific characteristic raises the effect of agglomeration.
Nevertheless, a large coefficient does not mean that an independent variable x explains alarge part of the variance of the dependent variable y. The explanatory power of a variable
also depends on its own variability. We compute the explanatory power of the right-hand side
variables. The question we ask is: “How much does the probability to export of a given firmvary if, all else equal, variable x increases by a standard-deviation with respect to its mean?”17
Not surprisingly, Table (6) shows that the firm-specific and country specific variables such as
the firm’s size or TFP and the destination country’s demand have a larger explanatory powerof the decision to start exporting than the spillover variables. Still, a one standard-deviation
increase in each of the three significant agglomeration variables increases the probability toexport by 0.39 to 0.55 percentage point for a given firm-product-country triad over time. It
appears that even after controlling for the variance of independent variables, product-country
specific spillovers are more decisive than other types of spillovers in the within dimension.
Our results on the effect of neighboring exporters are estimated on the time variation withina firm-product-country triad, however the coefficient can also be interpreted as a theoreticalexperiment of moving a firm from her original region to a region where the level of spillovers ishigher, everything else equal. This is what the lower part of the table does, by relying on between
variation to compute standard deviation. Results suggest that spillover differences matter morein the cross dimension (i.e. to explain differences in terms of decision to start exporting acrossfirm-product-country) than in the within dimension (over time). A one standard-deviation
increase in each of the three significant agglomeration variables increases the probability toexport by 1.87 to 7.01 percentage points across firm-product-country triads. This is because
the much larger standard deviations across groups than within groups.
17

Following Head and Mayer (2004), the explanatory power of variable x (which enters in log term) is obtainedby the expression [(1 + σxx )β − 1] × 100, where σx and x are the standard-deviation and the mean of x, and β its
coefficient in the regression. The explanatory power of our spillover variable x which enters linearly is obtainedβ(x+σx )by the expression [ e eβ(x) − 1] × 100, where σx and x. To express them in percentage point of probability, they
are multiplied by 0.30, the average probability of exporting in our sample.

4.2.2

Wages, urbanization economies, and size of industry

We perform several robustness checks and further investigations on the nature of export spillovers
in Table (7). Agglomeration of firms in the area can generate tensions on the labor market andrise wages, weakening firms’ propensity to export. Omitting wages could bias our estimation of
spillovers. Using our preferred specification, in column 1 the estimation is done by including thefirms’ wage (wagebill divided by the number of employees). The coefficient on wages is positiveand significant, potentially because it is correlated with TFP. Still, the interesting result is thatthe coefficient on the spillover variable remains positive, significant, and of the same magnitude
as without the additional wage variable.
In the literature on agglomeration economies, besides intra-sectoral externalities, Jacobs(1969) argues that the diversity of local activity generates cross fertilizations and improves
firms’ performance. Column 2 investigates the possibility that the diversity of exported products
manufactured in the same area impacts the export decision and affects our estimation of intraproduct export spillovers. As a check for these urbanization economies, we perform our preferred
specification and add the number of other exported products in the same area, whatever thedestination country. This variable comes out positive and significant, revealing that the larger
the diversity of exported goods produced in the neighborhood (for a given number of neighboringfirms), the larger the probability to export. Note that the coefficient on spillovers remains
positive and significant.
In column 3, we explore the predominance of the spillovers’ product and country specificity
over the product only specificity. For a given firm-product-country triad, we decompose theproduct specific spillovers in two categories: firms exporting the same product to the same
country and firms exporting the same product to other countries. We thus add to our preferred
specification a variable counting the number of other firms in the area exporting the same
product to different destinations18 . Results show that the coefficient on our product-country
export spillovers remains unchanged. The effect of the second spillover variable is positive andslightly significant. This means that the product specific spillover, which was significant incolumn 3 of Table (5), was identified on the destination-specific variability. We thus confirm
that destination and product specific export spillovers are stronger than product specific export
spillovers.
Columns 4 and 5 of Table (7) investigate whether the effect of spillovers arises from thenumber of surrounding firms or from the size of the surrounding industry. In column 4 wereplace the spillover variable computed on the number of firms by a spillover variable computed
as the total number of employees working in exporting plants located in the area. The coefficientis positive and significant. However, in column 5, when the number of exporting firms and theiraverage size are simultaneously controlled for, the coefficient on firms’ size is only significant at
the 10% level. This result suggests that for a given number of exporters in the area, a bigger
18

For firm i, located in area z and exporting product k to country j at time t, the definition of this variable isconsequently # of other exporting firmszkt − # of other exporting firmszkjt .

size does not bring large additional benefits.
4.2.3

Spatial decay

Columns 6 through 9 investigate the geographical scope of the microeconomic mechanism. We
estimate in column 6 the preferred specification, adding additional spillover variables computed
at different geographical scales: we consider firms in the employment area (as before), firms
outside the initial employment area but within the administrative region, and finally firms in
France outside the administrative region of the firm. Results show that spillovers seem to behighly localized, since coefficients on all three spillover variables are positive and significant,and show a decreasing trend with distance from the initial firm. The probability of starting toexport increases by 0.9 percentage point when an additional firm exporting the same productto the same country locates in the same area. The effect is almost three times smaller for a firm
locating in the region but in a different area (0.3 percentage point) and almost six times smallerwhen locating in a different region (0.17 percentage point).
Column 7 performs the same estimation, however using product specific spillover variables
only (hence all destinations, same product spillovers). Results confirm the spatial decay ofexport spillovers within France. The magnitude of the effect of other same-product exporters
is greater within employment areas (although less significant) and declines when neighbors arecounted in the rest of the region and then in the rest of France. To summarize, results attest
that spillovers on the export decision exist with product and destination specific neighbors, anddecrease with the geographic extent in which we count the number of exporting firms. Thishighlights the localized feature of the positive effects on firms’ export performance captured
in the spillovers, i.e. market externalities of exporters agglomeration (cost sharing etc.) andinformation flows between exporters. Indeed, one can reasonably think that the larger the
distance, the more difficult and costly the cooperation between firms, and consequently the lesspowerful the spillovers. Moreover, flows of information have been shown to be geographically
restricted by Jaffe, Trajtenberg and Henderson (1993), using patent citation data.
The last two columns in Table (7) reproduce the two previous columns adding product-year
fixed-effects defined at the SH2 level.19 We find that the spatial decay resists the inclusionof product-year fixed effect controlling for product-specific factors that vary over time such as
tariffs. Columns 8 and 9 are estimated with a linear probability model. Coefficients on thespillover variables are the marginal effects: they are very close to the marginal effects computedin columns 6 and 7.
The explanatory power of the three spillover variables at different geographical scales (area,
region, and nation) is investigated in Table (8). For both product and destination specific and19
Since it was impossible to account in a logit model for both the firm-product-country triadic fixed-effects andfor product-year fixed-effects, these two columns report results based on linear probability estimations. Moreover,using product-year fixed effect at the SH4 level would have led to introduce more than 5000 dummies, which isbeyond the computational capacities of our econometric software. This forced us to rely on the SH2 classification
to compute product-year fixed-effects.

destination specific spillovers, no spatial decay in terms of explanatory power is observed, due
to stronger variability of spillover variables at the regional and national levels.
4.2.4

Robustness to different sub-samples

We are aware of a possible selection bias in our estimation due to the use of a specific sample offirms. We now show that our results are robust to using alternative measures of spillovers anda variety of different sub-samples. .
In the first two columns of Table (9), we question the comparison between export spillovers
captured by the number of exporting neighbors and export economies within the firm. Column
1 reproduces our preferred specification (column 4 of Table (4)) adding the number of otherdestinations to which product k is exported by firm i. This variable captures product-specificinformation on how to export product k or scope economies across destinations. Column 2alternatively includes the number of other products which are exported to the destination j.
This variable captures destination-specific information on how to export to country j or scope
economies across products. We expect these variables to affect positively the probability ofstarting to export. Our results confirm these predictions while leaving the impact of spillovers
unchanged.
In columns 3 to 10 of Table (9), we find that the influence of spillovers does not depend on
firm’s size. In column 3 we interact our spillover indicator with the firm’s number of employees.
The interactive term fails to enter significantly. In columns 4 to 7 we run separate regressions
for low-employment and high-employment firms. In columns 4 and 5 the cut-off corresponds tothe median size (39 employees) while in columns 6 and 7 we use the mean size. Our results do
not suggest any clear heterogeneity of our spillover effect according to the firm’s size. Indeed thedifference in the coefficients does not seem to be significant. The last three columns investigate
heterogeneity between single and multi-plant firms in the left-hand side variable. Column 8refers to the whole sample. Columns 9 and 10 refer respectively to single-plant firms andmulti-plant firms. It seems that multi-plant firms benefit less from spillovers than single-plant
ones; the insignificance of the interaction term in column 8 suggests that this is not due tosize differences. However the difference in the coefficients is not significant, and it is difficult
to assess if the detected differences in coefficients would reflect measurement errors or a true
heterogeneity of the impact of exporters agglomeration across those two types of firms.
We verify that the main message of the paper holds when restricting our attention to observations corresponding to significant export flows, in Table (10). First, we restrict the sample
to export intensive firms defined as exporting at least 5% of their total sales, as in Mayer andOttaviano (2008), in columns 1 to 4. Second, we focus on significant exported products, defined as products representing at least the sample median of the share of products in the firm’s
total sales, in columns 5 to 8. While the sample size is logically reduced, our estimates areunchanged.20
20

We thank an anonymous referee for suggesting this robustness check. Note that we also estimate our preferredFinally, Table (11) reproduces the estimation relative to the spatial decay, column 6 of Table(7), using different samples. Identical results are obtained while relying on the SH2 nomenclature
instead of the SH4 nomenclature. While the sample size is roughly divided by two (the number ofobservations is reduced from 645,268 to 322,903), our product-country specific spillover variableretains its spatial decay feature. Similarly, the enlargement of our sample to both single andmulti-plant firms (columns 3 and 4)21 does not affect the results.
4.2.5

Specification tests

Further robustness checks involve specification tests. In the right-hand side panel of Table (11),the explained variable is the export status of the firm, and not the decision to start exporting.
It is defined as a dummy variable which takes the value 1 if the firm exports a product k attime t to country j. The sample size is doubled. Again our results are unchanged. Nevertheless,the explanatory power of the regressions is very weak (R2 smaller than 1%), suggesting thatgravity-type equations are not well suited to explain the yearly export status at the firm-productdestination level.
In the benchmark regression based on fixed-effect, we are interested in the effect of a change
in the levels of spillovers with respect to the average level of spillovers within the triad firmproduct-country over the entire period. Table (12) provides an alternative specification test,in which we specify the right-hand variables in first differences. We thus explain the decision
to start exporting by a one-year change in the explanatory variables. The effect of spillovers
is here more short-run than in the fixed-effect specification, which, in identifying the effect ontime-variations with respect to the mean, allows the impact of spillovers to last longer. Table(12) highlights that the effect of all spillover variables is smaller than in the benchmark results(Table 4). The product-country spillover variable is now the only one to be significant.

4.3

Spillovers on the export volume

We now present the results relative to the presence of export spillovers on the intensive marginof trade, hence on the volume exported by individual firms. The database contains all the observations for which firms export a product to a country. Estimation results are thus conditionalon the fact that firms export. Results are displayed in Tables (13), (14) and (15).
Table (13) contains the results of the base estimation in a similar way as Table (4) did forthe extensive margin. From left to right, the columns include more control variables, ending
with the preferred specification in column 4. Column 5 investigates whether our spillover effectspecification on the sub-sample of firms that, having started to export, do not revert back to a non-trade status.
We use two definitions of permanent switchers: firms that start to export and export for at least one, or two
additional years. The sample is respectively divided by 6 and 20. This is consistent with the literature on
volatility of the export status at the firm-product-destination level (Muraközy and Békes, 2009). The effect of
product and destination specific spillovers remains significant at the 10% level.
21
We consider that all multi-plant firms’ export flows originate from their headquarter. Spillover variables forthese firms are thus computed as the number of neighbors in the headquarters’ area. For computational reasons,the estimations are based on a 50% random selection of firms.

remains significant when the sample is restricted to observations for which the number of firms
in the area exporting the same product to the same destination is greater than 3. Traditional
gravity variables impact the export volume in the expected way. Estimations of the coefficienton the spillover variable however do not perform as well as on the extensive margin in assessing
the presence of export spillovers. Columns 2 through 4 show a positive coefficient on the spillover
variable, significant at the 10% confidence level. In column 5 which restricts the sample to the
top end observations in terms of number of neighbors, the number of observations drops sharply
from 691,132 to 26,618. The impact of spillovers declines and loses its significance, indicatingthat the spillover effect measured in column 4 does mainly reflect the case of firms for which
the number of neighbors is low. The coefficient thus appears less general as on the extensive
margin.
Table (14) investigates the nature of potential export spillovers on the export volume. The
positive and significant coefficient discussed above on the product and country specific spillover
variable appears in the last column, however none of the other coefficients are significant.
Table (15) reports robustness checks similar to those in Table (7).

The coefficient on

spillovers appears positive and significant (at the 10% level) with the controls in columns 1 and2. In the remaining columns, the estimates fail to show significant export spillovers. Columns
6 and 8 examine the geographical scope of the agglomeration variable in order to look for a
spatial decay. We include the number of exporting neighbors computed respectively at the area,
region and national levels in column 6. Column 8 replicates this estimation using producttime fixed-effects defined at the SH2 level. A spatial decay structure does come out of theresults, however without any significant coefficient. In column 8, the inclusion of product-time
fixed-effects lowers the coefficient on spillovers, which is still not significant.
Finally, results available upon request check the presence of spillovers on the intensive margin
at the SH2 level of product nomenclature, and examine whether the inclusion of multi-plantfirms in the sample affects the outcome. The regressions exhibit unstable results and again very
weak explanatory power of the regressions. Consequently, by contrast with our analysis on theextensive margin, we believe that our results globally suggest the absence of export spillovers
on the intensive margin.

5

Conclusion

This paper investigates the impact of exporters’ agglomeration on the export behavior of firms,
using a detailed dataset on French exports by firm, product, year and destination country for
1998-2003. We extend the existing literature by questioning the existence of the microeconomic
mechanism between exporters both on the decision to start exporting and on the exported
volume. If export spillovers exist, they are likely to benefit a given firm through a decrease in
its trade costs, allowing the firm to export a larger volume of the good abroad and/or to facilitate
its export decision. With the inclusion of controls, results show a distinct effect of exporters’agglomeration on the intensive and extensive margins of trade. The number of product-country
specific exporters in a given area positively affects the export decision of a firm, however it does
not seem to have an effect on the volume exported by the firm. Spillovers on the export decision
are stronger when specific, by product and destination, and are not significant when consideredon all products or all products-all destinations. More, export spillovers exhibit a spatial decay:
the effect of other exporting firms on the decision to start exporting declines with distance but
remains when computed at the regional and national scale. From a policy point of view, ourresults thus tend to show that devices aimed at promoting exports should be concentrated on
specific product and country markets. Moreover they would need to be limited to the outlinesof smaller geographical areas.

Table 1: Descriptive statistics on the sample of exporters
Share of total ...
export value export volume nb of exporters
Manufacturing multiplant firms >20 employees
38.70%
32.60%
4.72%
Manufacturing single plant firms >20 employees
11.75%
9.41%
8.94%
Other exporting firms
49.56%
57.98%
86.33%
Total French exports
100.00100.00100.00
Manufacturing single plants firms in all manu23.29%
22.41%
65.44%
facturing exporting firms > 20 employees

0
1
2
3-5
6-10
>10
Nb of observations

Std. Dev.
Min
170.9
2.5
283560.84630.75
12196.5
219.1
14745110.6
3451.3
262.4
13.8
1
12.9
1
72.9
0
30.1
0
6.6
0
1.7
0
8071

same product-same destination
84.8%
9.4%
2.7%
2.2%
0.7%
0.2%

all products-all destinations
0.1%
0.2%
0.3%
2.1%
6.8%
90.5%

Max
6166
1689989575363
4.62×107
19263.9
277
116
350
223.3
62
35.5

# other firms in the area
all products-same destination same product-all destinations
12.1%
43.1%
10.1%
18.7%
8.2%
9.9%
17.3%
13.3%
16.9%
7.9%
35.4%
7.1%
645268

Table 3: Distribution statistics of spillovers in terms of firms

Mean
77.1
181556.8
3751.1351897.5
3107.2
11
10.5
58.8
18
3
0.47

Table 2: Descriptive statistics (sample used in Tables 3 to 5)

Variable
Firm employees
Total employment in the area
Firm value added
Destination country’s demand, product specificDistance# of exported products# of destination countries# other firms in the area, all products-all destinations# other firms in the area, all products-same destination# other firms in the area, same product-all destinations# other firms in the area, same product-same destination
Nb of firms

Table 4: Logit on the decision to start exporting / Same product-same destination spillovers
Model :ln (firm’s employees)ln (destination country’s imports, product specific)(1)
0.572a(0.077)0.177a(0.013)(2)
0.571a(0.077)0.175a(0.013)0.051a(0.009)(3)0.579a(0.075)0.175a(0.013)0.051a(0.009)0.120a(0.034)645268
yes
yes
0.09

645268
yes
yes
0.09

645268
yes
yes
0.09

# other firms in the area, same product-same destinationln (firm’s TFP)ln (total employment in area)
Observations
Year fixed-effects
Firm-country-product fixed-effects
R2(4)
0.570a(0.075)0.174a(0.013)0.051a(0.009)0.118a(0.035)0.884(0.585)645268
yes
yes
0.09

(5)
0.311
(0.202)
0.586c(0.321)0.033a(0.010)0.241c(0.138)-1.416(1.664)
9007
yes
yes
0.14

Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and 10% levels. Regressions
are corrected for clustering at area level. All explanatory variables are time specific and lagged one year with respect
to the explained variable. In column 5 the sample is restricted to observations for which the number of firms in thearea, same product-same destination is greater than 3.

Table 5: Logit on the decision to start exporting / Different product-destination spillovers
Model :ln (firm’s employees)ln (firm’s TFP)ln (total employment in the area)ln (destination country’s imports, product specific)# other firms in the area, all products-all destinations(1)
0.570a(0.074)0.118a(0.035)0.869(0.582)0.176a(0.013)0.001(0.001)(2)0.570a(0.075)0.119a(0.035)0.842(0.586)0.172a(0.013)(3)
0.568a(0.075)0.119a(0.035)0.874(0.586)0.175a(0.013)0.008a(0.003)# other firms in the area, all products-same destination

0.012b(0.005)# other firms in the area, same product-all destinations# other firms in the area, same product-same destination
Observations
Year fixed-effects
Firm-country-product fixed-effects
R2(4)
0.570a(0.075)0.118a(0.035)0.884(0.585)0.174a(0.013)645268
yes
yes
0.09

645268
yes
yes
0.09

645268
yes
yes
0.09

0.051a(0.009)645268
yes
yes
0.09

All regressions are conditional logit estimations. Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and 10% levels. Regressions are corrected for clustering
at area level. All explanatory variables are time specific and lagged one year with respect to the
explained variable.

Table 6: Explanatory power - Decision to start exporting
Within variation
Variable

Mean

Std-dev.

Firm’s employees
Firm’s TFP
Destination country’s demand, product specific# of other exporters in the area, same product-same destination# of other exporters in the area, same product-all destinations# of other exporters in the area, all products-same destination
Between variation
Variable

126.1
67.6
426157
0.31
3.03
14.51

19.07
26.8
248572
0.35
1.07
2.11

Mean

Std-dev.

Expl. power(% point)
2.57
1.24
2.56
0.55
0.39
0.53

Expl. power(% point)
Firm’s employees
126.1
209.46
22.92
Firm’s TFP
67.6
48.95
2.04
Destination country’s demand, product specific426157 2109558
11.19# of other exporters in the area, same product-same destination
0.31
1.16
1.87
# of other exporters in the area, same product-all destinations
3.03
6.54
2.45
# of other exporters in the area, all products-same destination
14.51
25.53
7.01
The table must be read as follows: a standard within deviation of the number of employees with respect toits mean generates an increase of probability to start exporting of 2.57% based on within variation and of
22.92% based on between variation.

644740
0.09
yes
yes
no

(1)
0.634a(0.081)0.085b(0.033)0.292a(0.082)0.908(0.581)0.174a(0.013)0.051a(0.009)645268
0.09
yes
yes
no

0.812
(0.582)0.174a(0.013)0.049a(0.009)0.375a(0.086)(2)
0.563a(0.074)0.119a(0.034)645268
0.09
yes
yes
no

0.009b(0.004)0.871(0.586)0.174a(0.013)0.054a(0.010)(3)
0.569a(0.075)0.118a(0.035)645268
0.09
yes
yes
no

0.0002a(0.00005)

0.889(0.585)0.175a(0.013)(4)0.570a(0.075)
0. 118a(0.035)645268
0.09
yes
yes
no

0.0001c(0.00006)

0.884(0.585)0.174a(0.013)0.048a(0.010)(5)0.570a(0.075)0.118a(0.035)645268
0.09
yes
yes
no

0.014a(0.004)0.008a(0.001)0.868(0.588)0.166a(0.013)0.042a(0.009)(6)0.566a(0.075)0.119a(0.035)0.008c(0.005)0.003a(0.001)0.0007a(0.0002)645268
0.09
yes
yes
no

0.831
(0.588)0.171a(0.013)(7)0.564a(0.075)0.119a(0.035)645268
0.08
yes
yes
yes

0.003a(0.001)0.002a(0.0002)0.288c(0.143)0.039a(0.004)0.010a(0.002)(8)0.122a(0.020)0.027a(0.011)

0.002(0.001)0.0008b(0.0003)0.0003a(0.00007)
645268
0.08
yes
yes
yes

0.282c(0.148)0.040a(0.004)(9)0.122a(0.020)0.027a(0.011)

All regressions are conditional logit estimations except columns (8) and (9) which are linear probability estimations. Standard errors in parentheses, a . b and crespectively denoting significance at the 1%. 5% and 10% levels. Regressions are corrected for clustering at area level. All explanatory variables are time specificand lagged one year with respect to the explained variable. Estimations in columns (8) and (9) are done with product (SH2)-time fixed effects.

Observations
R2
Year fixed-effects
Firm-country-product fixed-effects
Product-year fixed-effects# other firms in France other than the region, same prod.-all dest.

# other firms in region other than the area, same prod.-all dest.

# other firms in the area, same prod.-all dest.

# other firms in France other than the region, same prod./dest.

# other firms in region other than the area, same prod./dest.

Mean size of other exporting firms, same prod./dest.

# employees in the area, other firms, same prod./dest.

# other firms in the area, same product-other destinationsln (1+# other exported products in the area)# other firms in the area, same product-same destinationln (destination country’s imports, product specific)

ln (total employment in the area)ln (firm’s mean wage)ln (firm’s TFP)

Model :ln (firm’s employees)

Table 7: Logit on the decision to start exporting / Different product-destination spillovers / Robustness checks

Table 8: Are local externalities really local?
Within variation
Variable

Mean

Std-dev.

# of other exporters in the area, same product-same destination# of other exporters in region but the area, same product-same destination# of other exporters in France but the region, same product-same destination
Between variation
Variable

0.31
1.76
17.10

0.35
0.93
3.95

Mean

Std-dev.

Expl. power(% point)
0.45
0.41
1.02

Expl. power(% point)# of other exporters in the area, same product-same destination
0.31
1.16
1.51
# of other exporters in region but the area, same product-same destination
1.76
4.86
2.21
# of other exporters in France but the region, same product-same destination 17.10
31.98
9.33
The table must be read as follows: a standard within deviation of the product and destination specificspillover variable with respect to its mean generates an increase of probability to start exporting by 0.45%
based on within variation and of 1.51% based on between variation.

645268
0.10
yes
yes

0.503a(0.070)0.110a(0.033)0.928c(0.532)0.165a(0.013)0.050a(0.010)0.470a(0.067)
0.093a(0.033)0.778(0.542)0.173a(0.013)0.051a(0.010)0.103a(0.005)645268
0.10
yes
yes

0.113a(0.010)

(2)

(1)

0.002
(0.008)645268
0.09
yes
yes

0.569a(0.075)0.118a(0.035)0.884(0.585)0.174a(0.013)0.041(0.031)(3)165671
0.10
yes
yes

<39
0.410a(0.110)0.116(0.071)2.241(1.564)0.195a(0.025)0.039a(0.010)(4)479597
0.09
yes
yes

336464
0.09
yes
yes

(5)
(6)
Employment≥39<69
0.657a0.423a(0.096) (0.084)
0.120a0.137a(0.045) (0.045)
0.400
1.705c(0.674) (0.964)
0.168a0.172a(0.015) (0.018)
0.059a0.049a(0.012) (0.011)

308804
0.09
yes
yes

All
0.388a(0.112)
0.094b(0.037)2.472a(0.929)0.167a(0.014)0.022a(0.008)≥69
0.829a(0.119)0.083c(0.049)-0.185(0.943)0.179a(0.017)0.054a(0.017)0.001(0.001)889193
0.09
yes
yes

(8)

(7)

363134
0.10
yes
yes

469388
0.09
yes
yes

(9)
(10)
Firm type
Single-plant Multi-plant
0.540a0.316b
(0.095)(0.135)0.034
0.167a(0.050)(0.052)
1.921a2.950b(0.717)(1.397)0.177a0.158a(0.017)(0.017)0.033a0.023a(0.010)(0.002)All regressions are conditional logit estimations. Standard errors in parentheses, a . b and c respectively denoting significance at the 1%. 5% and
10% levels. Regressions are corrected for clustering at area level. All explanatory variables are time specific and lagged one year with respect
to the explained variable.

# other firms in the area, same prod./dest.
interacted with ln (firm’s employees)
Observations
R2
Year fixed-effects
Firm-country-product fixed-effects# other products, same firm-same dest.

# other destinations, same firm-same dest.

# other firms in the area, same prod./dest.

ln (destination country’s imports, product specific)

ln (total employment in the area)ln (firm’s TFP)ln (firm’s employees)

Model :

Table 9: Logit on the decision to start exporting / Different product-destination spillovers / Robustness checks

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Exports at least 5% of total sales
Exclusion of bottom half of productsln (firm’s employees)
0.575a0.576a0.574a0.576a0.540a0.540a0.538a0.540a(0.084) (0.084) (0.084) (0.084) (0.078) (0.078) (0.078)(0.079)ln (firm’s TFP)
0.117a0.117a0.117a0.116a0.100a0.100a0.100a0.100a(0.037) (0.037) (0.037) (0.037) (0.035) (0.035) (0.035)(0.035)ln (total employment in area)
0.872
0.840
0.873
0.879
1.000
0.974
1.005
1.015
(0.611) (0.614) (0.613) (0.612) (0.830) (0.826) (0.834)(0.832)ln (destination country’s imports, product specific)
0.174a0.171a0.173a0.173a0.190a0.187a0.189a0.189a(0.014) (0.013) (0.013) (0.013) (0.014) (0.014) (0.014)(0.014)# other firms in the area, all products-all destinations
0.001
0.001
(0.001)(0.002)# other firms in the area, all products-same destination
0.008b0.008b(0.003)(0.003)# other firms in the area, same product-all destinations
0.009c0.011b(0.005)(0.005)# other firms in the area, same product-same destination
0.050a0.052a(0.010)(0.012)
Observations
574158 574158 574158 574158 484152 484152 484152
484152
R2
0.09
0.09
0.09
0.09
0.10
0.10
0.10
0.10
Year fixed-effects
yes
yes
yes
yes
yes
yes
yes
yes
Firm-country-product fixed-effects
yes
yes
yes
yes
yes
yes
yes
yes
All regressions are conditional logit estimations. The sample is restricted to “intensive exporters”. They are defined as firms exporting at least
5% of total sales in columns 1 to 4 and as products representing at least the sample median of the share of products in the firm’s total sales, incolumns 5 to 8. Standard errors in parentheses, a . b and c respectively denoting significance at the 1%. 5% and 10% levels. Regressions arecorrected for clustering at area level. All explanatory variables are time specific and lagged one year with respect to the explained variable.

Table 10: Logit on the decision to start exporting / Different product-destination spillovers / Intensive exporters

Model :

(1)
(2)
(3)
(4)
SH4
SH2
SH4
SH2
Single-plant firms
All firms
Decision to start exporting
0.566a0.551a0.385a0.504a(0.075)(0.073)(0.109)(0.096)0.119a0.126a0.095a0.110a(0.035)(0.038)(0.037)(0.030)0.868
1.065c2.471a0.425(0.588)(0.628)(0.933)(0.528)0.166a0.346a0.157a0.275a(0.013)(0.029)(0.013)(0.025)0.042a0.017c0.020a0.015a(0.009)(0.009)(0.003)(0.002)0.014a0.005b0.010a0.003a(0.004)(0.002)(0.002)(0.001)0.008a0.002a0.005a0.002a(0.001) (0.0005) (0.001) (0.0003)
645268322903889193419564
0.09
0.10
0.09
0.10
yes
yes
yes
yes
yes
yes
yes
yes

(5)
(6)
(7)
(8)
SH4
SH2
SH4
SH2
Single-plant firms
All firms
export status
0.557a0.587a0.432a0.534a(0.068)(0.061)(0.068)(0.066)0.138a0.162a0.106a0.144a(0.029)(0.032)(0.026)(0.031)0.132
0.183
1.445b-0.086(0.486)(0.443)(0.734)(0.546)0.134a0.315a0.122a0.259a(0.010)(0.020)(0.009)(0.020)0.033a0.017a0.017a0.012a(0.007)(0.005)(0.001)(0.002)0.008a0.004a0.005a0.002b(0.002)(0.001)(0.001)(0.001)0.006a0.001a0.004a0.001a(0.001)(0.0002) (0.0003) (0.0002)
126211167425617777369002920.00430.00550.00370.0045
yes
yes
yes
yes
yes
yes
yes
yes

All regressions are conditional logit estimations. Standard errors in parentheses, a . b and c respectively denoting significance at the 1%. 5% and
10% levels. Regressions are corrected for clustering at area level. All explanatory variables are time specific and lagged one year with respect
to the explained variable.

Observations
R2
Year fixed-effects
Firm-country-product fixed-effects# other firms in France other than the region, same prod./dest.

# other firms in region other than the area, same prod./dest.

# other firms in the area, same prod./dest.

ln (destination country’s imports, product specific)

ln (total employment in area)ln (firm’s TFP)ln (firm’s employees)

Model :
product nomenclaturefirm type

Table 11: Logit on the decision to start exporting & export status / Different product-destination spillovers / Robustness checks

583440
0.07
yes
no

(1)
0.268a(0.044)0.010(0.023)1.355a(0.505)0.069a(0.008)-0.001(0.002)583440
0.07
yes
no

0.002
(0.002)(2)
0.268a(0.044)0.010(0.023)1.331a(0.504)0.069a(0.008)583440
0.07
yes
no

0.001
(0.003)(3)
0.268a(0.044)0.009(0.023)
1.343a(0.505)0.069a(0.008)0.025a(0.007)583440
0.07
yes
no

(4)
0.268a(0.044)0.009(0.023)
1.337a(0.504)0.069a(0.008)All regressions are logit estimations with right hand side variables in first difference. Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and
10% levels. Regressions are corrected for clustering at area level. All explanatory variables
are time specific and lagged one year with respect to the explained variable.

Observations
R2
Year fixed-effects
product (SH2)-time fixed-effects∆ # other firms in the area, same prod./dest.

∆ # other firms in the area, same prod.-all dest.

∆ # other firms in the area, all prod.-same dest.

∆ # other firms in the area, all prod./dest.

∆ ln Imports jkt∆ ln (Total employment zt)∆ ln (TFP it)

Model :∆ ln (firm employees)

Table 12: Logit on the decision to start exporting / Different product-destination spillovers (first differences)Table 13: OLS on the export volume / Same country-product spillovers
Model :ln (firm’s employees)ln (destination country’s imports, product specific)(1)0.231a(0.063)0.120a(0.022)(2)0.231a(0.063)0.120a(0.022)0.012c(0.007)(3)0.238a(0.060)0.118a(0.022)0.012c(0.007)0.058a(0.019)691132
0.001
yes
yes

691132
0.001
yes
yes

691132
0.001
yes
yes

# other firms in the area, same product-same destinationln (firm’s TFP)ln (total employment in area)
Observations
R2
Year fixed-effects
Firm-country-product fixed-effects(4)0.229a(0.059)0.116a(0.021)0.012c(0.007)
0.056a(0.019)0.899c(0.467)691132
0.002
yes
yes

(5)
0.383c(0.195)0.318(0.208)0.010(0.007)0.113c(0.065)
1.691a(0.525)
26618
0.010
yes
yes

Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and 10% levels.
Regressions are corrected for clustering at the area level. In column 5 the sample is restricted to observations
for which the number of firms in the area, same product-same destination is greater than 3. All explanatory
variables are time specific and lagged one year with respect to the explained variable.

Table 14: OLS on the export volume / Different product-destination spillovers
Model :ln (firm’s employees)ln (firm’s TFP)ln (total employment in area)ln (destination country’s imports, product specific)# other firms in the area, all products-all destinations(1)
0.229a(0.059)0.056a(0.019)0.865c(0.464)0.117a(0.021)
0.002(0.001)# other firms in the area, all products-same destination(2)0.229a(0.059)0.056a(0.019)0.897c(0.470)0.116a(0.021)(3)
0.229a(0.059)0.056a(0.019)0.902c(0.469)0.117a(0.021)0.001(0.001)# other firms in the area, same product-all destinations

0.002(0.003)# other firms in the area, same product-same destination
Observations
R2
Year fixed effects
Firm-Country-Product fixed effects(4)0.229a(0.059)0.056a(0.019)0.899c(0.467)0.116a(0.021)691132
0.002
yes
yes

691132
0.002
yes
yes

691132
0.001
yes
yes

Standard errors in parentheses, a , b and c respectively denoting significance at the 1%,
5% and 10% levels. Regressions are corrected for clustering at area level. All explanatory
variables are time specific and lagged one year with respect to the explained variable.

0.012c(0.007)691132
0.002
yes
yes

690606
0.002
yes
yes
no

(1)
0.239a(0.064)0.050b
(0.022)0.048(0.062)0.913c(0.476)0.116a(0.021)0.012c(0.007)691132
0.002
yes
yes
no

0.883c(0.462)0.116a(0.021)0.011c(0.007)0.071(0.045)(2)0.228a(0.059)0.056a(0.019)691132
0.002
yes
yes
no

0.001
(0.001)0.894c(0.469)0.116a(0.021)
0.011(0.007)(3)0.229a(0.059)0.056a(0.019)691132
0.002
yes
yes
no

0.00007c(0.00004)

0.901c(0.466)
0.117a(0.021)(4)
0.229a(0.059)0. 056a(0.019)691132
0.002
yes
yes
no

0.00006(0.00007)

0.900c(0.465)0.116a(0.021)0.010(0.007)(5)0.229a(0.059)0.056a(0.019)691132
0.002
yes
yes
no

0.004
(0.003)0.001(0.001)0.895c(0.465)0.115a(0.021)0.010(0.007)(6)0.228a(0.059)0.056a(0.019)0.001(0.001)0.0001(0.0002)0.0000006(0.000001)
691132
0.002
yes
yes
no

0.900c(0.470)0.116a(0.021)(7)0.229a(0.059)0.056a(0.019)Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and 10% levels. Regressions are corrected forclustering at area level. All explanatory variables are time specific and lagged one year with respect to the explained variable.

Observations
R2
Year fixed-effects
Firm-country-product fixed-effects
Product-year fixed-effects# other firms in France other than the region, all prod.-same dest.

# other firms in region other than the area, all prod.-same dest.

# other firms in the area, all prod.-same dest.

# other firms in France other than the region, same prod./dest.

# other firms in region other than the area, same prod./dest.

ln (mean size of other exporting firms, same prod./dest.)# employees in the area, other firms, same prod./dest.

# other firms in the area, other prod.-same dest.

ln (1+# exported products in the area)# other firms in the area, same prod./dest.

ln (destination country’s imports, product specific)

ln (total employment in the area)ln (firm’s mean wage)ln (firm’s TFP)

Model :ln (firm’s employees)

Table 15: OLS on the export volume / Robustness checks

691132
0.004
yes
yes
yes

0.003
(0.003)0.0002(0.0005)

0.930b(0.450)0.108a(0.020)0.008(0.007)(8)
0.222a(0.056)
0.059a(0.019)0.001(0.001)0.0001(0.0002)0.0000006(0.000002)
691132
0.004
yes
yes
yes

0.932b(0.452)0.109a(0.020)(9)0.222a(0.056)
0.059a(0.019)

Table A-1: Logit on the decision to start exporting / Specification test
Model :ln (firm’s employees)ln (firm’s TFP)ln (total employment in area)ln (destination country’s imports, product specific)# other firms in the area, same product-same destination(1)
0.570a(0.075)0.118a(0.035)0.884(0.585)0.174a(0.013)0.051a(0.009)(2)0.570a(0.075)0.118a(0.035)0.887(0.585)0.174a(0.013)(4)
0.311(0.202)0.241c(0.138)-1.416(1.664)
0.586c(0.321)0.033a(0.010)0.072a(0.018)0.127a(0.028)0.184a(0.045)0.201a(0.060)0.234a(0.073)0.315b(0.135)0.528a(0.196)

1 firm in the area, same product-same destination
2 firms in the area, same product-same destination
3 firms in the area, same product-same destination
4 firms in the area, same product-same destination
5 firms in the area, same product-same destination
6-10 firms in the area, same product-same destination
More than 10 firms in the area, same product-same destination
Strictly positive # firms in the area, same product-same destination
Observations
Year fixed-effects
Firm-country-product fixed-effects
R2(3)
0.571a(0.075)0.118a(0.035)0.891(0.585)0.175a(0.013)645268
yes
yes
0.09

645268
yes
yes
0.09

0.080a(0.018)645268
yes
yes
0.09

9007
yes
yes
0.14

Standard errors in parentheses, a , b and c respectively denoting significance at the 1%, 5% and 10% levels.
Regressions are corrected for clustering at the area level. In Column 4, the sample is restricted to observations
for which the number of firms in the area, same product-same destination is greater than 3. All explanatory
variables are time specific and lagged one year with respect to the explained variable.

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