Granular Comovement∗
Julian di Giovanni
Universitat Pompeu Fabra
Barcelona GSE
CREI and CEPR

Andrei A. Levchenko
University of Michigan
NBER and CEPR

Isabelle Méjean
Ecole Polytechniqueand CEPR

December 4, 2014PRELIMINARY AND INCOMPLETE

Abstract
This paper investigates the role of individual firms in international business cycle comovement using data covering the universe of French firm-level sales, bilateral imports
and exports, and cross-border ownership over the period 1991-2007. Because internationally connected firms are systematically larger than non-internationally connected
firms, the firms directly linked to foreign countries represent 10% of all firms, but 45%
of all value added. After controlling for firm and country effects, trade in goods with aparticular foreign country is associated with a significantly higher correlation between afirm and that foreign country. By contrast, there is no significant relationship between
multinational linkages at the firm level and comovement with foreign GDP. The impact
of direct trade linkages has significant aggregate implications. Directly connected firms
account for two-thirds of the observed aggregate comovement. Without those linkages
the correlation between France and foreign countries would fall by about 0.1. These results are evidence of transmission of business cycle shocks through direct trade linkages
at the firm level, and of the important role of granularity in international business cycle
comovement.
JEL Classifications: F44, F61, F62
Keywords: Comovement, International Trade, Firm-Level Shocks, Large Firms∗
We would like to thank Eda Gulsen for expert research assistance.
E-mail (URL): julian.digiovanni@upf.edu (http://julian.digiovanni.ca), alev@umich.edu (http://www.alevchenko.com), isabelle.mejean@polytechnique.edu (http://www.isabellemejean.com).

1

Introduction

The international business cycle exhibits two prominent features. First, there is significant
positive business cycle comovement between countries. And second, country pairs thatare more closely linked through goods trade or multinational production exhibit greater
comovement. Taken at face value, these two facts coupled with ever greater economic integration appear to suggest that shocks are transmitted across countries through trade andmultinational linkages. Working predominantly with aggregate models and data, the literature has struggled to provide convincing empirical evidence of cross-country transmission ofshocks, and to develop quantitative frameworks that can successfully replicate the featuresof business cycle comovement observed in the data.
Until now the literature has by and large not studied the properties of cross-border
comovement at the firm level, or its aggregate implications. By contrast, an emerging
research agenda in closed-economy macro has argued convincingly that modeling and measuring shocks at the micro level (to firms and sectors) is essential for understanding aggregate fluctuations. Gabaix (2011), di Giovanni and Levchenko (2012), and Carvalho andGrassi (2013) develop models in which aggregate fluctuations arise from shocks to individual firms, because the firm-size distribution is extremely fat-tailed (Zipf’s Law). Acemoglu
et al. (2012) and Carvalho and Gabaix (2013) argue that sectoral shocks lead to aggregate
fluctuations through interconnections between sectors. Di Giovanni et al. (2014) and Atalay(2014) provide corresponding empirical evidence on the role of shocks to firms and sectors
in aggregate fluctuations.
If large firms and firm-to-firm linkages matter for aggregate fluctuations, a natural conjecture is that they will matter as much if not more for cross-border comovement. Larger
firms are disproportionately more likely to trade internationally and own affiliates in foreign countries. Indeed, in most countries international trade flows are dominated by only
a handful large firms. If transmission of shocks through trade and multinational linkages
leads to positive aggregate comovement, it should first be detectable at the firm level.
This paper provides a forensic account of international comovement at the firm andaggregate level using data covering the universe of French firm-level production, destinationspecific imports and exports, and cross-border ownership over the period 1991-2007. To
set the stage, we start with the observation that the aggregate business cycle correlation
between France and another country is simply an appropriately weighted sum of the firmlevel correlations with that country. Some French firms will exhibit direct linkages to the1foreign country, through importing from it, exporting to it, or cross-border ownership ofaffiliates. The aggregate business cycle correlation between France and each country canthus be written as a sum of two terms: the part due to the directly connected firms, and thepart due to the not directly connected firms. Working with a set of 10 large trading partnersof France, we show that the large directly connected firms are important in accounting for
aggregate comovement. For a typical foreign partner country, the directly connected firms
represent only about 10% of all firms in our dataset. However, they account for 45% of
total value added.
This decomposition is evidence that the combined firms with direct linkages to a particular foreign country are large enough to affect aggregate outcomes in France. Next, weestablish that the direct linkages indeed serve as a conduit for the transmission of foreign
shocks. We estimate a specification inspired by Frankel and Rose (1998), that relates comovement with foreign GDP at the firm level to firm-level direct linkages. Because thesample includes many firms and countries, estimation controls for firm and country effects,
which absorbs the common aggregate shocks affecting France and each foreign country, andallows us to identify the effects from the differences in correlation across countries for thesame firm. Trade linkages at the firm level are significantly associated with increased comovement between an individual firm and the country with which it trades. An importlink increases the correlation by 0.01, and an export link by 0.002. This is large relative
to the average correlation between an individual firm and foreign GDP, which is 0.02 for
directly connected firms, and zero for non directly connected ones. By contrast, after controlling for country effects, multinational links do not have a robust positive association
with comovement at the firm level.
Finally, we build up to the aggregate implications of these micro-level estimates. The
directly connected firms are unconditionally more correlated with the foreign country. Together with the fact that they account for almost half of the aggregate value added, this
implies that the directly connected firms account for two-thirds of the aggregate businesscycle correlation observed in the data. We then use the conditional relationship between
direct connections and firm-level correlations to compute the change in the aggregate correlation between France and each foreign country that would occur if direct linkages at thefirm level disappeared. The aggregation exercise combines information on the change inthe correlation at the firm level with firm-level weights. On average, if direct linkages at
the firm level were severed, the aggregate correlation would fall by 0.1. Since the observed
correlations between France and its main trading partners are at most 0.35 over this period,2

this is a sizeable change.
Our paper contributes to the literature on international business cycle comovement. An
important research agenda, going back to Backus et al. (1995), attempts to understandthe positive GDP correlation across countries using representative firm models in which
all shocks are aggregate. Later developments in this literature explore the role of the
production structure, such as input-output linkages (Burstein et al., 2008; Arkolakis andRamanarayanan, 2009), or firm heterogeneity (Ghironi and Melitz, 2005; Alessandria andChoi, 2007) but have similarly been confined to considering only the role of aggregate
productivity shocks in generating cross-country business cycle comovement.
The landmark paper by Frankel and Rose (1998) establishes that country pairs that
trade more with each other exhibit more correlated business cycles. While this robust
empirical regularity has been confirmed repeatedly in subsequent studies, the literature
has struggled to understand it. Kose and Yi (2006) and Johnson (2014) show that evenquite sophisticated IRBC models fail to capture this relationship, dubbing it the “tradecomovement puzzle.” The literature on multinationals and international business cycle
comovement is more limited, but shares a similar feature. Kleinert et al. (2012) showthat French regions that contain more multinationals from a particular foreign country are
more correlated with that country. However, Cravino and Levchenko (2014) show that theobserved multinational presence alone cannot generate the level of positive comovementfound in the data.
While the existing empirical literature has focused almost exclusively on aggregate GDP
correlations, this paper explores the transmission of shocks internationally at the firm level,and derives the aggregate implications based on micro-level estimates. In this respect, it
shares some features with recent papers by Kurz and Senses (2013), Boehm et al. (2014),
and Cravino and Levchenko (2014), who perform related exercises.
The rest of the paper is organized as follows. Section 2 lays out the conceptual frameworkand the empirical exercises performed in the paper. Section 3 describes the data, andSection 4 the results. Section 5 concludes.

2

Conceptual Framework

Total value added Xt by all French firms in year t is by definition given by: Xt ≡P

f ∈It xf t ,where xf t is defined as the value added of firm f in year t, and It is the set of firms
f operating at t. The growth rate of aggregate value added is then defined simply as
γAt = Xt /Xt−1 − 1, where we assume that Xt−1 and Xt are the aggregate value added of3all firms that exist both at t − 1 and t, i.e. we restrict attention to the intensive margin of
aggregate value added growth.1
The growth rate of aggregate sales can be written as a function of individual firm growth
rates and firm shares:
γAt =

X

wf t−1 γf t ,(1)

fwhere γf t is the growth rate of value added of firm f , and wf t−1 is the share of f ’s value
added in total French value added.
The object of interest is the correlation between French aggregate growth and foreign
GDP growth. Let γCt be the GDP growth of a foreign country C between t − 1 and t. This
correlation is given by:
Cov (γAt , γCt ),σA σCwhere σC is the standard deviation of country C growth.
ρ (γAt , γCt ) =(2)

Unfortunately, working with equation (1) directly to produce a decomposition of the aggregate correlation (2) is impractical because time-varying weights wf t−1 make the stochastic process (1) difficult to analyze.
Instead, we work with a closely related set of stochastic processes:
γAt|τ =

X

wf τ −1 γf t .

(3)

f

For a given τ , γAt|τ is a stochastic process in which weights wf τ −1 are fixed over time attheir τ − 1 values, and combined with growth rates from period t. Naturally, when τ = t,
the “synthetic” aggregate growth rate γAt|τ coincides with the actual aggregate growth rate
γAt . The correlation between γAt|τ and foreign GDP growth can be written as:
Cov γAt|τ , γCt
ρ γAt|τ , γCt =σAτ σCP
Cov
f wf τ −1 γf t , γCt=σAτ σCP
f wf τ −1 Cov (γf t , γCt )=σAτ σCPw
f f τ −1 σf σC ρ (γf t , γCt )=σAτ σCX
σf=wf τ −1
ρ (γf t , γCt ) ,
σAτ

(4)

f

1

In earlier work using these data (di Giovanni et al., 2014) we show that the extensive margin is notquantitatively important for fluctuations at the business cycle frequency.

4where σf is the standard deviation of γf t , and σAτ is the standard deviation of γAt|τ .2
While simply an identity, Equation (4) states the key premise of the paper: the aggregate
correlation between the French economy and another country is a weighted sum of the firmlevel correlations, times the relative standard deviation of the firm to the aggregate. We
proceed by analyzing first the properties of the individual firm-level correlations ρ (γf t , γCt ),and then the consequences of aggregation across firms.

2.1

Micro Evidence

Equation (4) emphasizes that the aggregate business cycle correlation between the Frencheconomy and foreign countries is a function of the individual firm-level correlations andthese firms’ weights in the total French economy. We start by establishing whether the
direct trade and multinational linkages at the firm level to a particular foreign country areassociated with a higher correlation between the firm and that foreign country. To that
end, we estimate the following specification:
ρ (γf t , γCt ) = α + β1 EXf,C + β2 IMf,C + β3 AF Ff,C + β4 HQf,C + δf + δC + ηf,C .

(5)

In equation (5), the correlation between a firm and a foreign market C is related to binary
indicators of whether the firm exports there (EXf,C ), imports from there (IMf,C ), is a
French multinational with affiliates in C (AF Ff,C ), or is an affiliate of a foreign multinational
headquartered in C (HQf,C ). Importantly, the specification admits both firm and country
effects, allowing for a precise identification of transmission of shocks through direct linkages.
The specification is inspired by Frankel and Rose (1998), but leads to qualitatively new
insights relative to the traditional cross-country empirical model. First, estimation at thefirm level reveals the micro underpinnings of the aggregate relationship. Observing crossborder links at the firm level allows us to establish with forensic precision the role of tradeand multinational links in international comovement. Second, comparing firms within thesame country and including country and firm fixed effects addresses a common critique ofFrankel-Rose style regressions: inability to control for common shocks (Imbs, 2004). Since
all firms in this specification are in France, country effects will absorb the common shocks
affecting France and country C. As a result, we can establish convincingly that trade andmultinational linkages are indeed a source of transmission of shocks, rather than simply astand-in for the presence of common shocks.
2
The approach of constructing aggregate moments under weights that are fixed period-by-period follows
Carvalho and Gabaix (2013) and di Giovanni et al. (2014), who apply it to variance decompositions.

5

2.2

From Micro to Macro

Next, we investigate the macroeconomic implications of these micro findings. The aggregate correlation as written in (4) is additive in the individual firm-level correlations withforeign GDP, and thus can be decomposed easily into the various components. Since we areinterested in the impact of individual firms on aggregate correlations, we can decompose
the summation of idiosyncratic shocks into two sets of firms: the directly connected andthe not directly connected to a particular country:
XX

wf τ −1 γf t =wf τ −1 γf t +wf τ −1 γf t

f ∈ICc

f ∈IC

f

X

where IC is the set of firms that satisfy at least one of the four criteria included in estimating
equation (5): (i) export to C; (ii) import from C; (iii) is a French affiliate of a multinational
based in C; or (iv) is part of a French multinational that has affiliates in C. Correspondingly,
ICc is the complement of that set of firms. Then, the aggregate comovement decomposes
additively into two components, one due to the directly connected firms, and the other due
to the rest: σI τ
ρ γAt|τ , γCt = C ρ 
σAτ


X

wf τ −1 γf t , γCt  +Pf ∈IC wf τ −1 γf t

σ
ρ
σAτ


X

wf τ −1 γf t , γCt  ,(6)

f ∈ICcf ∈ICwhere σI2C τ = Var



ICc τ



is the variance of the combined sales of the directly

connected terms, and similarly for σI2c τ .
C

By bringing in information on firm weights wf τ −1 , this additive decomposition will
provide the first glimpse of whether the directly connected firms combined are a large
enough segment of the economy to play an appreciable role in aggregate comovement. Of
course, this decomposition yields only part of the answer: the direct component can be large
either because the directly connected firms are large, or because they are more correlated.
Estimates of equation (5) provide the means of separating the two.
For each directly connected firm, we can compute the predicted change in its correlation
with country C if it were no longer connected with C:c (γf t , γCt ) = −βb1 1 (EXf,C = 1)− βb2 1 (IMf,C = 1)− βb3 1 (AF Ff,C = 1)− βb4 1 (HQf,C = 1) .
∆ρ
(7)As an example, if firm f only exported to C and had no other links, the predicted change
in the correlation between f and C is simply −βb1 . The formulation (7) allows for every

6combination of different types of direct links, and turns off all the existing ones at the sametime.
Combining (7) with (4), the predicted change in the aggregate business cycle correlation
between France and C if all cross-border links were severed is: X
σf cc γAt|τ , γCt =∆ρ (γf t , γCt ) .
∆ρ
wf τ −1
σAτ

(8)

f

For simplicity, this calculation assumes that the severing of the direct links does not havean impact on volatilities either at the firm or the aggregate level, or on firm weights.

3

Data and Basic Patterns

The empirical analysis conducted in this paper relies on several firm-level databases. The
main object of interest is the correlation between French and foreign GDP growth. At
the most disaggregated level, it is measured using a database that covers the universe ofFrench firm sales and value added over the period 1991-2007. The dataset is describedin detail in di Giovanni et al. (2014). Importantly, it covers the entire French economy,
manufacturing and non-manufacturing sectors included. We augment it with information
on each firm’s direct trade and multinational linkages, disaggregated by foreign partnercountry. Namely, we use Customs data to recover bilateral export and import flows, at thelevel of each individual firm. Finally, information on the firm ownership linkages is takenfrom the LIaisons FInancieres (LIFI) database, an administrative dataset that provides
information about the ownership and nationality of the parent company of firms located in
France. Together, these two datasets provide firm-level information on all possible direct
links to each individual foreign country, whether through cross-border trade or multinational
production. Finally, note that we do not have any information at the plant level.
The value added data, as well as additional variables, come from the balance sheet
information collected from firms’ tax forms. The original dataset is quasi-exhaustive. However, the amount of information that has to be provided to the fiscal administration differs
according to the firm’s size. Namely, the French tax system distinguishes three different
regimes, the “normal” regime (called BRN for Bénéfice Réel Normal ), the “simplified”
regime (called RSI for Régime Simplifié d’Imposition) that is restricted to smaller firms,
and the “micro-BIC” regime for entrepreneurs.3 Throughout the exercise, “micro-BIC”
3

Under some conditions, firms can choose their tax regime. In 2010, an individual entrepreneur can
decide to enroll in the “micro-BIC” regime if its annual sales are below 80,300 euros. Likewise, a firm canchoose to participate in the RSI rather than the BRN regime if its annual sales are below 766,000 euros(231,000 euros in services). Those thresholds are adjusted over time, but marginally so.

7and “RSI” firms are excluded. We do not have enough information for “micro-BIC” firms.
We also exclude “RSI” firms, both because their weight in annual sales is negligible andbecause it is difficult to harmonize these data with the rest of the sample. In 2007, thosefirms represented less than 4% of total sales and about 11% of total employment. Thus, oursample represents the bulk of the aggregate French economy. In this sample, it is possible
to classify firms according to the sector in which they operate, using information on their
NAF code.4
The information collected by the tax authorities is combined with the firm-level export
and import data from the French customs authorities. The datasets can be merged using aunique firm identifier, called SIREN. On top of the firm dimension, the customs data alsodetail trade flows by their country of destination (for exports) or the country of origin (forimports). This is what makes it possible to investigate the heterogeneity of trade linkages
within firm across different foreign countries. The customs data are also quasi-exhaustive.
There is a declaration threshold of 1,000 euros for annual exports to and annual imports
from any given destination outside the European Union. Below the threshold, the customs
declaration is not compulsory. Since 1993, intra-EU trade is no longer liable for any tariff,
and as a consequence firms are no longer required to submit the regular customs form. A newform has however been created that tracks intra-EU trade. Unfortunately, the declarationthreshold for this kind of trade flows in much higher, around 150,000 euros per year in 2010.
A number of firms continue declaring intra-EU trade flows below the threshold however,
either because they don’t know ex ante that they will not reach the 150,000 Euro limit in agiven fiscal year, because they apply the same customs procedure for all export markets they
serve, or because they delegate the customs-related tasks to a third party (e.g., a transportfirm) that systematically fills out the customs form. Below-cutoff trade flows missing from
customs data imply that we might underestimate the contribution of direct trade linkages
as a driver of aggregate comovements.5
4“NAF”, Nomenclature d’Activités Française, is the French industrial classification. Our baseline analysis considers the level of aggregation with 60 sectors. This corresponds to the 2-digit ISIC (Revision 3)
nomenclature. We drop NAF sectors 95 (domestic services), and 99 (activities outside France). We also
have to neglect the banking sector due to important restructuring at the beginning of the 2000s that makes
it difficult to follow individual firms. The NAF nomenclature has been created in 1993, as a replacement
for the “NES” (Nomenclature Economique de Synthèse). Data for 1990–92 are converted into the NAF
classification using a correspondence table.
5
We can judge how many exports we are missing by comparing exports declared on tax forms to exports
declared to customs. It appears that the problem is relatively minor. In 10% of firm-year observations, thetax form reports exports but the customs data do not. These observations account for 7% of overall exports.
On average, the total exports reported in the tax form but missing from customs (413,000 euros per year)
are an order of magnitude smaller than average exports in the whole sample, which are 3,056,000.

8

Finally, the LIFI data is used to recover information on i) whether each French firm is
an affiliate of a company headquartered in a particular foreign country, or ii) whether eachfirm is or belongs to a French company that owns foreign affiliates in a particular foreigncountry. The LIFI database is constructed by the French statistical institute (INSEE). It
is not exhaustive, but it has a good coverage. All firms with more than 500 employees
or a turnover above 60 millions euros, whatever their sector of activity, are included in
the survey. Moreover, the information is complemented with two additional administrative
sources that allow integrating a large number of smaller groups. According to the French
statistical institute, a firm is an affiliate of a group if the latter has the (direct or indirect)
majority of voting rights. Using this definition, INSEE identifies firms that own affiliates
abroad, together with the nationalities of those affiliates. When the French firm is identified
as affiliate of a foreign company, the nationality of the group is recorded as well.
The firm-level correlation coefficients are measured using the time dimension of the value
added data, at the firm-level. On the other hand, the empirical strategy does not require
to use the time variability of measures of each firm’s direct links with each destination
country. To construct the dummies for whether a firm exports (EXf,C ), imports (IMf,C ),has affiliates in the destination (AF Ff,C ) or is an affiliate of a foreign multinational firm
(HQf,C ) the time dimension is thus collapsed. Namely, in the baseline analysis the dummy
is set to one whenever the firm satisfies the corresponding condition over at least one year
in the period of observation.6
Figure 1 plots the growth rates of aggregate value added (and sales), exports, and imports, together with the growth rate of GDP from IMF’s International Financial Statistics,
and total exports and imports from IMF’s Direction of Trade Statistics. The aggregates inour sample of firms mimic the aggregate data from standard sources quite well.
Table 1 presents the basic stylized facts on the composition of the sample. Panel A
reports the summary statistics for the whole economy, and Panel B for the manufacturingsector only. The first two columns report the number of observations and the number offirms in the dataset. There are about 800 thousand firms in total, and nearly 7 million
firm-year observations. The rest of the panel reports the summary statistics for exporters,
importers, affiliates of foreign multinationals, and French firms with foreign affiliates. Thesefour categories are of course not mutually exclusive. The table reports the total numbersof observations and firms, the mean and median value added in each category, and the6
The results are robust to instead defining the dummy to equal 1 whenever the firm is connected for atleast 50% of the years it is present in the sample.

9percentage of total value added captured by each category of firms.
As expected, firms engaged in an international activity represent a small share of the
population of French firms. Around 20% of French firms export or import. There are anorder of magnitude more exporters and importers than multinational firms: about 160170 thousand of importers and exporters, compared to 18 thousand affiliates of foreign
multinationals, and 33 thousand French firms that have foreign affiliates. Each categoryof the internationally connected firms has larger average value added than purely domestic
firms. The largest category on average is the affiliates of foreign multinationals. More novelis the information in the last column that reports the share of total value added in France
that is taken up by each category of firms. These statistics have not, to our knowledge,
been previously reported. It turns out that exporters account for 70% of total French value
added, and importers 66%. By contrast, multinational firms account for a smaller shareof economic activity, with about 20% for each foreign affiliates and French multinational
category. Panel B reports the same statistics for the manufacturing sector. Not surprisingly,
the shares of exporters and importers are even larger, at around 90%.
Table A1 presents the average standard deviations of firm-destination growth rates
across sectors, along with the shares of each sector in total sales. The raw volatility ofsales growth varies across sectors, with the standard deviation ranging from a low of 0.1489(Health and social work) to a high of 0.3248 (Coke, refined petroleum and nuclear fuel),
and a cross-sectoral mean standard deviation of 0.2593. The wholesale and retail trade
sector has by far the highest share in aggregate sales, at nearly 37% of the total. While thestandard deviation of sales growth, at 0.2188, is quite typical of the rest of the economy,
clearly wholesale and retail trade is quite special in other ways. To establish the robustnessof the results, all of the analysis below is carried out both on the whole economy and on
the manufacturing sector.
Table 2 reports the measures of connectedness and firm-level correlations for France’s
10 major trading partner countries.7 For each country, the table presents the number of
directly connected firms, the combined share of those firms in total French value added, andthe mean correlation between an individual firm and the GDP growth of that country. The
last three columns report the same statistics for the not directly connected firms. The tablereveals the extent to which aggregate comovement is driven by large firms. On average, andfor most individual countries, there are an order of magnitude fewer directly connected firms
7
These countries are 9 of the top 10 trading partners of France plus Brazil, which we included as a major
emerging market to make the sample more diverse and less dominated by European countries.

10

than non-directly connected firms. At the mean, there are 77 thousand directly connected,
and 790 thousand not directly connected firms. However, the directly connected firms takeup on average 44% of total French value added. For every single partner country, the directly
connected firms are more correlated with the foreign GDP, with an average difference incorrelation of 0.023 in this set of countries. The variation across countries is as expected.
In the 4 countries most closely integrated with France – Belgium, Germany, the UK, andItaly – the directly connected firms account for over half of all French value added. At theother extreme, the firms directly connected to Brazil, China, and Japan account for 0.246
and 0.327, and 0.328 of aggregate French value added, respectively.
Panel B reports the same statistics for the manufacturing sector. The role of the directly
connected firms is greater in this sample. On average, the directly connected firms accountfor two-thirds of aggregate manufacturing value added, even enough they comprise less than
one-quarter of all the firms in this sample. The average correlations are slightly higher for
manufacturing firms compared to the whole economy, but the difference is not large.
Table 3 further separates the directly connected firms into importers, exporters, andforeign and domestic multinationals. Once again, the categories are not mutually exclusive.
There are large differences between the trading firms and the multinationals. Directly
connected exporters and importers account for 32 and 37 percent of aggregate French value
added for this set of foreign countries, or over three-quarters of the total value added ofconnected firms. By contrast, affiliates of foreign multinationals from an individual country
take up 1.4% of aggregate value added. French firms with foreign affiliates account for
11.4% of aggregate value added. There are also many fewer multinational firms of bothkinds than trading firms. The manufacturing sector (Panel B) yields similar results.

4

Main Results

4.1

Firm-Level Linkages and Correlations

Table 4 reports the results of estimating Equation (5). The baseline sample includes all
firms, and performs the analysis on the growth rates of value added. The standard errors are
clustered at the firm level. The first column presents the basic estimation without any fixed
effects. All four forms connectedness are positive and strongly significant. The coefficientmagnitudes are sizeable as well. Importing or exporting is associated with increases in
the correlation of 0.027, being a foreign affiliate of 0.032, and having foreign affiliates of
0.015. The next column adds firm fixed effects. In this specification, the coefficients are11

estimated from the variation within the same firm across the 10 partner countries. Some ofthe point estimates fall somewhat, but overall all 4 forms of connectedness remain positiveand strongly significant.
The next column adds country effects. Given that this specification adds only 10 dummy
variables to a regression with more than 8 million observations, it is remarkable how dramatically the coefficients change. The importer coefficient falls by a factor of 3, and theexporter coefficient by a factor of 10. Both multinational coefficients decrease and cease to
be statistically significant. This change in the coefficient is a stark illustration of the key
tension in the Frankel-Rose type estimation: disentangling transmission of shocks throughtrade from common shocks. Taken at face value, the Frankel and Rose (1998) result thatcountries that trade more are more correlated appears to suggest that trade linkages transmit shocks across countries. However, as argued forcefully by Imbs (2004), trade intensity
at the bilateral country level could simply be a proxy for a greater prevalence of common
shocks (see also the discussion in di Giovanni and Levchenko, 2010). By using firm-level
data, we can control much better for the common shocks that affect France and its trading
partners. The contrast between the specifications with and without country effects shows
why it is important to do so. Without country effects (and even after including firm effects),
it looks like directly connected firms are strongly correlated with the markets with which
they are linked. However, it is clear that a large part of these estimated coefficients is driven
by the fact that firms are more likely to establish direct links with more correlated markets.
Adding country effects controls for the average correlation between French firms and eachcountry, and reduces the estimated impact of direct connectedness considerably.
Nonetheless, column 3 shows that even after controlling for common shocks, direct trade
linkages increase comovement between a firm and the foreign country. A direct importing
link is associated with an increase in the firm-level correlation of 0.01, and an exporting linkof 0.002. Relative to the mean correlation of about 0.02 for the directly connected firms,
these coefficients are sizeable.
The rest of the table checks robustness of the results to different samples and left-handside variables. Both firm and country effects are included throughout. Column 4 checks
what happens if we only use firms for which we have at least 10 years of value added
data based on which to compute the correlation with foreign countries. The sample drops
dramatically, from about 8 million to just over 3 million. The importing coefficient ispreserved, though the point estimate falls by almost half. The exporter coefficient is nolonger positive, in fact it turns negative and significant, though small in absolute terms.

12The weakening of the results is to be expected, as including firm fixed effects is extremely
demanding, and our procedure requires a lot of observations to identify the effect. Losing
more than 60% of the sample produces less robust results.
Column 5 reports the estimates in which we use the correlation between firm idiosyncratic shocks and foreign GDP. The idiosyncratic shocks are identified following di Giovanni
et al. (2014) by extracting the sector- and destination-specific components from firm growth
rates, and retaining the residual as the idiosyncratic shock.8 The results are similar to column 3. Column 6 uses the correlation of firm sales instead of value added. The results arestronger than in the baseline. Finally, column 7 reports the results for the manufacturingsector only. The results are similar.
To summarize, direct connectedness through importing is robustly positively associatedwith greater comovement between a firm and foreign GDP. This effect is identified from thevariation across foreign countries within the firm (i.e., by comparing the firm’s correlation
with a country that it trades with to its correlation with a country that it does not), andafter controlling for common aggregate shocks. Thus, this result can be interpreted as robust
evidence for transmission of shocks through trade. On the other hand, after controlling forcommon shocks, exporting has a much smaller effect (about one-fifth that of importing),
and multinational presence is insignificant.

4.2

Aggregate Implications

Table 5 presents the decomposition in (6). For each country, it reports the average aggregatecorrelation ρ γAt|τ , γCt , as well as the shares of the aggregate correlation due to the directly
and not directly connected firms. The top panel reports the results for the whole economy,
and the bottom panel for the manufacturing sector only.
In the whole economy, on average almost 60% of the aggregate correlation is taken up
by the directly connected firms. The shares are always between zero and one, implying thatthe direct and indirect components always have the same sign as the overall correlation. In
8

Di Giovanni et al. (2014) set up a simple heterogeneous firms model to show that the growth rate offirm f ’s sales to destination market n (which could be France or a foreign country) in sector j is given by:
γf nt = δjnt + εf nt ,where δjnt is the macro-sectoral shock that is common to all French firms’ sales to market n in sector j, andεf nt is the idiosyncratic shock to firm f ’s sales to market n. After extracting the destination-specific firm
idiosyncratic shock, we compute the overall firm idiosyncratic
P shock by weighting the destination-specificshocks by the firm-level sales shares to destination: εf t ≡ n ωf nt−1 εf nt , where the ωf nt−1 is the share ofsales to market n in the firm’s total sales. If the firm only sells domestically, then trivially εf t = εf nt . We
do not have a breakdown of firm value added by destination, and thus we assume that value added shares
equal sales shares for firms selling to multiple markets.

13

the manufacturing sector, the observed correlations are on average higher, and the sharetaken up by the directly connected firms is larger, at 0.81 on average.
This decomposition is merely suggestive that direct links are responsible for the observed
aggregate comovement. Equation (6) shows that the direct component could be large bothbecause the directly connected firms account for the large share of the economy, and/or
because they exhibit larger correlations with the foreign country. Table 2 shows indeed thatboth of those things are true. However, the higher correlations reported for the directly
connected firms in the table are not necessarily evidence of transmission of shocks. To isolate
the role of the transmission of shocks, we next make use of the econometric estimation
results.
We first compute, based on each firm’s connectedness values, how much its correlation
with each country would change if it were no longer connected to that country, as in (7).
For all firms that are not connected at all to a particular country, this change is zero. We
then aggregate according to Equation (8). This equation takes into account the interaction
between relative firm sizes (wf ) and connectedness: the impact on aggregate comovement
would be greater, all else equal, if the connected firms take up a larger share of aggregate
value added.
Table 6 presents the results of computing the change in the aggregate correlation as in(8). It reports the actual correlation in the data, the predicted change in the correlation
if none of the firms were connected, and the standard error for that predicted change incorrelation. The aggregate effect is sizeable: on average correlation would decrease by about
0.1 if firms stopped being connected. By comparison, the actual observed correlations areabout 0.35 at the maximum.
As the weights wf τ −1 differ from year to year, Equation (8) yields an estimate of thechange in aggregate correlation for each year 1991-2007. Figure 2 plots the change inaggregate correlation for each country and each year, along with the 2-standard error bands.
The first page of the figure displays the European trading partners, and the second page thenon-European ones, that tend to be both less correlated and less directly connected than theEuropean ones. The y-axis has the same scale in all plots. It is clear that there is not muchdifference in the aggregate correlation change with respect to the European partners, andthe changes year to year are quite similar as well. This is not surprising, as the calculation
does not involve country-specific information on the level of the correlation between France
and these countries, and the estimated regression coefficients are not country-specific. The
variation across countries is driven by the differences in the set of connected firms. Evidently,

14

across the top 6 European trade partners, the sets of directly connected firms are similar.
There is more heterogeneity across non-European trading partners, and greater differences
between non-European and European ones. All in all, both the relative similarity in levels
and the time variation across countries illustrates that the directly connected firms are often
the same ones across trading partners. We conjecture that this is because the set of directly
connected firms is dominated by the largest trading firms that serve multiple markets.
Figure 3 presents the same results for the manufacturing sector. The similarities acrosscountries are if anything greater in this sample. This is to be expected, as the manufacturing
sample is even more dominated by the large exporters than the whole economy.
An important assumption underlying this aggregation exercise is that there are no othergeneral equilibrium interactions that change firm-level correlations when France’s opennesschanges. In particular, the calculation assumes that (i) the change in the correlation of all
directly connected firms is given by (7); and (ii) the change in the correlation of all not
directly connected firms is zero. Thus, it ignores the possibility that a change in France’s
overall openness has feedback effects that change the firm-level correlations away from what
is predicted by the micro-level regressions. These feedbacks are potentially interesting but
there are no established intuitions or results that could even point to the direction of thoseeffects. Clearly, general equilibrium feedbacks can only be analyzed within a full generalequilibrium model structure, and are inaccessible to the regression estimation-type approach
adopted here. Nonetheless, by combining micro-level results on changes in comovement at
the firm level with information on the combined size of the connected firms, our results are
informative of the size of the likely aggregate effect.

5

Conclusion

In order to understand fluctuations at the macro level, we must understand micro-level
behavior. This paper applies this principle to international business cycle comovement byanalyzing this phenomenon at the firm level. Because the largest firms are the most likely
to exhibit direct international linkages, we show that they account for nearly half of Frenchaggregate value added. We next show that they are more correlated with the countries towhich they are directly connected through trade. Combining the two, the directly connected
firms account for two-thirds of the observed aggregate correlations between France and its
major trading partners, and if these direct linkages were severed, the aggregate correlations
would fall by 0.1.
A clear next step in this research agenda is to model the propagation of foreign shocks
15

through the domestic economy beyond the directly connected firms. Understanding the interactions between the directly connected firms and the rest of the French economy throughinput, factor, and goods markets will be crucial for developing the full picture of interdependence between France and its trading partners.

16

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and , “Country Size, International Trade, and Aggregate Fluctuations in Granular
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, , and Isabelle Méjean, “Firms, Destinations, and Aggregate Fluctuations,” Econometrica, July 2014, 82 (4), 1303–1340.
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17

Imbs, Jean, “Trade, Finance, Specialization, and Synchronization,” Review of Economics
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18

Table 1. Summary Statistics
Panel A: Whole EconomyObs.
(firm×year)

No.
firms

Value Added
Median Share in total

Mean

All firms

6964577809056

4690

650

1.00

Importers

1033739178500

22216

3103

0.70

Exporters

895269169921

23970

3312

0.66

Affiliates of foreign multinationals

180544

18374

33995

5527

0.19

Firms with foreign affiliates

331534

32615

19826

2412

0.20

Panel B: Manufacturing Sector
Obs.
(firm×year)

No.
firms

Mean

Value Added
Median Share in total

All firms

1139528

124687

10142

1060

1.00

Importers

386443

56586

27326

4420

0.91

Exporters

390770

57881

26350

3902

0.89

Affiliates of foreign multinationals

58824

5573

56623

11532

0.29

Firms with foreign affiliates

68242

6051

25713

4603

0.15

Notes: This table reports the summary statistics for the whole economy and the manufacturing sectors. It
reports the number of firm× year observations, number of distinct firms, mean and median value added,
and the share of a particular type of firm in total value added. The categories of firms are not mutually
exclusive.

19

Table 2. Directly Connected and Not Directly Connected Firms
Panel A: Whole Economy

Country

Directly Connected
No.
Combined
Mean
firms
share
ρ(γf t , γCt )Not Directly Connected
No.
Combined
Mean
firms
share
ρ(γf t , γCt )BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States

11441519770
43032
11145892859
83376
109547
38292
80859
74786

0.539
0.246
0.327
0.544
0.486
0.503
0.514
0.328
0.487
0.460

0.053
-0.007-0.047
0.052
0.040
0.006
0.060
-0.0200.055
0.036

778806807392803814777511788463788803779309803098791748793844

0.461
0.754
0.673
0.456
0.514
0.497
0.486
0.672
0.513
0.540

0.026
-0.046-0.0610.029
0.021-0.0220.032-0.0250.024
0.018

Average

76839

0.443

0.023

791279

0.557

0.000

Panel B: Manufacturing Sector
Directly Connected
CombinedMeanshareρ(γf t , γCt )Not Directly Connected
No.
Combined
Mean
firms
share
ρ(γf t , γCt )CountryNo.
firms

BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States

43846
9981
16649
43557
36677
33557
41421
16699
31826
29026

0.812
0.402
0.448
0.824
0.754
0.761
0.789
0.519
0.732
0.666

0.067
0.000
-0.036
0.059
0.042
0.008
0.065
-0.0140.067
0.036

108400123693122901107641113476113972109973121405115919117459

0.188
0.598
0.552
0.176
0.246
0.239
0.211
0.481
0.268
0.334

0.047
-0.027-0.0490.053
0.034-0.0080.063-0.0140.059
0.024

Average

30324

0.671

0.029

115484

0.329

0.018

Notes: This table reports the features of directly connected and not directly connected firms for each partnercountries. The columns report the number of firms, their combined share in aggregate value added (averaged
across years), and the mean correlation between firm value added growth and the foreign country’s GDP
growth.

20

21

No.
firms

34274
7474
8984
30269
27078
24559
26815
12207
22235
20590

21449

Country

BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States

Average

0.029

0.058
0.003
-0.034
0.055
0.043
0.013
0.062
-0.024
0.067
0.044

0.565

0.718
0.350
0.336
0.705
0.665
0.654
0.657
0.423
0.601
0.545
0.034

0.070
0.008
-0.033
0.061
0.048
0.015
0.070
-0.016
0.076
0.043

Exporters
CombinedMeanshareρ(γf t , γCt )

0.323

0.425
0.177
0.184
0.404
0.377
0.378
0.371
0.234
0.348
0.331

Exporters
CombinedMeanshareρ(γf t , γCt )

23234

33650
4423
12728
37391
27906
26194
35803
9734
24218
20288

No.
firms

60681

89942
10297
36060
95039
72970
65529
95213
23613
63621
54526

No.
firms

0.021

0.053
-0.016-0.050
0.054
0.038
0.005
0.060
-0.013
0.049
0.032
1636

2143
1
37
3267
543
2938
1271
416
2625
3114
0.014

0.008
0.000
0.000
0.024
0.002
0.021
0.007
0.004
0.022
0.050

0.574

0.729
0.213
0.328
0.786
0.640
0.687
0.735
0.400
0.637
0.584

0.028

0.066
-0.005-0.035
0.060
0.035
0.005
0.063
-0.0080.062
0.036

Importers
CombinedMeanshareρ(γf t , γCt )

492

693
0
4
1083
159
694
409
113
677
1091

0.026

0.014
0.000
0.000
0.042
0.005
0.030
0.016
0.007
0.031
0.092

-0.0040.069
0.000-0.3490.064-0.002
0.018
0.027
0.066
0.045
0.028

Affiliates of multinationals
No. Combined
Mean
firms
share
ρ(γf t , γCt )

0.037

0.036
0.198
-0.080
0.064
0.010
0.001
0.052
0.001
0.051
0.039

Affiliates of multinationals
No. Combined
Mean
firms
share
ρ(γf t , γCt )Panel B: Manufacturing Sector

0.374

0.480
0.120
0.220
0.509
0.411
0.450
0.473
0.262
0.420
0.394

Importers
CombinedMeanshareρ(γf t , γCt )

0.114

0.130
0.083
0.119
0.128
0.132
0.129
0.128
0.076
0.091
0.127

0.039

0.071
0.009
-0.052
0.075
0.053
0.035
0.058
-0.015
0.104
0.053

100

102
46
78
148
146
117
117
42
42
163

0.039

0.046
0.037
0.022
0.045
0.050
0.045
0.044
0.017
0.035
0.046

0.048

0.068
0.030
-0.056
0.067
0.081
0.086
0.063
-0.0340.100
0.078

Firms with foreign affiliates
No. Combined
Mean
firms
share
ρ(γf t , γCt )

334

438
125
228
455
552
423
409
111
149
452

Firms with foreign affiliates
No. Combined
Mean
firms
share
ρ(γf t , γCt )Notes: This table reports the features of different types of directly connected firms. The columns report the number of firms, their combined
share in aggregate value added (averaged across years), and the mean correlation between firm value added growth and the foreign country’s
GDP growth.

42122

70568
12158
15261
59923
52670
47274
54011
23443
42800
43108

BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States

AverageNo.
firms

Country

Panel A: Whole Economy

Table 3. Directly Connected Firms: by Connection Type

22
8,088,384
0.001
no
no
4212166068103342
16355

0.0267***(0.001)0.0268***(0.001)0.0318***(0.009)0.0145***(0.003)8,088,384
0.321
yes
no
4212166068103342
16355
8090560.0293***(0.001)
0.0226***(0.001)
0.0225***(0.008)0.0096***(0.003)(2)
Baseline

8,088,384
0.324
yes
yes
4212166068103342
16355
809056100.0099***(0.001)0.0028***(0.001)0.0119(0.008)0.0023(0.003)(3)
Baseline

3,188,170
0.341
yes
yes
265967375267
1996
8263
318817100.0049***(0.001)-0.0021**(0.001)0.0027(0.008)-0.0037(0.003)(4)>10 years per firm

8,088,270
0.323
yes
yes
4212086068103343
16354
809056100.0060***(0.001)0.0025***(0.001)0.0133(0.008)0.0042(0.003)(5)
Idio. Shocks

8,670,311
0.325
yes
yes
414741
616671
3822
17504
867272100.0143***(0.001)0.0095***(0.001)0.0084(0.008)0.0006(0.003)(6)
Sales

1,246,559
0.315
yes
yes
2144852323351001
4923
124687100.0067***(0.001)0.0028*(0.002)-0.0007(0.015)0.0016(0.005)(7)
Manuf.

Notes: This table reports the results of estimating equation Equation (5). The independent variables are binary indicators for whether the firm
imports from a country, exports to it, is an affiliate of a multinational firm from that country, or is a French multinational with affiliates in thatcountry.

Observations
R-squared
Firm FECountry FE# of EX-ing firms# of IM-ing firms# of affiliates# of HQs# of firm FEs# of country FEs

French Multinational

Foreign Affiliate

Exporter

Importer

Dep. Var: ρ (γf t , γCt )(1)Baseline

Table 4. Main Estimation ResultsTable 5. Aggregate Correlations: Contributions of Direct and Indirect Terms

Country
BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States
AverageCountry
BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States
Average

Panel A: Whole Economy
Average ρA|τ Direct
Indirect(observed)(share)(share)
0.340
0.611
0.389
-0.3750.602
0.398-0.515
0.541
0.459
0.200
0.589
0.411
0.353
0.474
0.526
-0.075
0.883
0.117
0.263
0.358
0.642
-0.321
0.648
0.352
0.325
0.527
0.473
0.249
0.689
0.311
0.044

0.592

0.408

Panel B: Manufacturing Sector
Average ρA|τ Direct
Indirect(observed)(share)(share)
0.584
0.917
0.083
-0.1030.432
0.568-0.362
0.705
0.295
0.235
0.860
0.140
0.424
0.866
0.134
0.222
0.984
0.016
0.391
0.837
0.163
-0.168
0.760
0.240
0.526
0.832
0.168
0.427
0.908
0.092
0.218

0.810

0.190

Notes: This table reports the results of decomposition Equation (6). The first column reports the actual
correlation in the data.

23

Table 6. Aggregate Correlations: Contributions of Direct and Indirect Terms
Country
BelgiumBrazilChina
Germany
Spain
United Kingdom
Italy
Japan
Netherlands
United States
Average

Average ρA|τ(observed)
0.340-0.375-0.5150.200
0.353-0.0750.263-0.321
0.325
0.249

∆ρA

s.e.(∆ρA )-0.111-0.058-0.083-0.113-0.103-0.106-0.108-0.075-0.098-0.104

0.015
0.009
0.013
0.015
0.015
0.015
0.015
0.009
0.012
0.015

0.044

-0.096

Notes: This table reports the results of the aggregation exercise Equation (8). The last column reports thestandard error associated with the estimated change in aggregate correlation.

24

0

.02

Growth rate
.04
.06

.08

.1

Figure 1. Comparison with Aggregates, Growth Rates

1992

1994

1996

1998

2000

2002

Total sales
GDP

2004

2006

Value added(a) Aggregate Sales, Aggregate Value Added, andGDP
0.3
IMF DOTS
Sample0.2

Growth rate of Exports

0.1

0

−0.1

−0.2

−0.3

−0.4
1992

1994

1996

1998

2000

2002

2004

2006

2008

(b) Exports
0.4
IMF DOTS
Sample0.3

Growth rate of Imports

0.2

0.1

0

−0.1

−0.2

−0.3
1992

1994

1996

1998

2000

2002

2004

2006

2008

(c) Imports
Notes: The top panel presents the time series of the growth rates of total sales, before-tax value added, in
our data and GDP sourced from the IMF International Financial Statistics. The bottom two panels present
the growth rates of total exports and imports, respectively, in our sample and sourced from IMF’s Directionof Trade Statistics.

25

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06
Predicted Change in Correlation

Predicted Change in Correlation

Figure 2. Predicted Change in Aggregate Correlation: Whole Economy

−0.08

−0.1

−0.12

−0.08

−0.1

−0.12

−0.14

−0.14

−0.16

−0.16

−0.18
1992

1994

1996

1998

2000

2002

2004

−0.18
1992

2006

1994

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

1998

2000

2002

2004

−0.18
1992

2006

1994

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

2000

1998

2000

2002

2004

2006

2004

2006

−0.12

−0.16

1998

1996

−0.1

−0.14

1996

2006

−0.08

−0.14

1994

2004

(d) United Kingdom

Predicted Change in Correlation

Predicted Change in Correlation(c) Spain

−0.18
1992

2002

−0.12

−0.16

1996

2000

−0.1

−0.14

1994

1998

−0.08

−0.14

−0.18
1992

1996

(b) Germany

Predicted Change in Correlation

Predicted Change in Correlation(a) Belgium

2002

2004

−0.18
1992

2006

(e) Italy

1994

1996

1998

2000

2002

(f ) Netherlands

Notes: This table plots the yearly estimates of the change in aggregate correlation between France and each country according tofor the whole economy, along with 2-standard error bands.

26

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06
Predicted Change in Correlation

Predicted Change in Correlation

Figure 3. (cont’d) Predicted Change in Aggregate Correlation: Whole Economy

−0.08

−0.1

−0.12

−0.08

−0.1

−0.12

−0.14

−0.14

−0.16

−0.16

−0.18
1992

1994

1996

1998

2000

2002

2004

−0.18
1992

2006

1994

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

1998

2000

2002

2004

2006

2002

2004

2006

−0.12

−0.16

1996

2000

−0.1

−0.14

1994

1998

−0.08

−0.14

−0.18
1992

1996

(h) United States

Predicted Change in Correlation

Predicted Change in Correlation(g) Japan

2002

2004

−0.18
1992

2006

(i) Brazil

1994

1996

1998

2000

(j) China

Notes: This table plots the yearly estimates of the change in aggregate correlation between France and each country according tofor the whole economy, along with 2-standard error bands.

27

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06
Predicted Change in Correlation

Predicted Change in Correlation

Figure 4. Predicted Change in Aggregate Correlation: Manufacturing Sectors

−0.08

−0.1

−0.12

−0.08

−0.1

−0.12

−0.14

−0.14

−0.16

−0.16

−0.18
1992

1994

1996

1998

2000

2002

2004

−0.18
1992

2006

1994

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

1998

2000

2002

2004

−0.18
1992

2006

1994

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

2000

1998

2000

2002

2004

2006

2004

2006

−0.12

−0.16

1998

1996

−0.1

−0.14

1996

2006

−0.08

−0.14

1994

2004

(d) United Kingdom

Predicted Change in Correlation

Predicted Change in Correlation(c) Spain

−0.18
1992

2002

−0.12

−0.16

1996

2000

−0.1

−0.14

1994

1998

−0.08

−0.14

−0.18
1992

1996

(b) Germany

Predicted Change in Correlation

Predicted Change in Correlation(a) Belgium

2002

2004

−0.18
1992

2006

(e) Italy

1994

1996

1998

2000

2002

(f ) Netherlands

Notes: This table plots the yearly estimates of the change in aggregate correlation between France and each country according tofor the manufacturing sector, along with 2-standard error bands.

28

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06
Predicted Change in Correlation

Predicted Change in Correlation

Figure 4. (cont’d) Predicted Change in Aggregate Correlation: Manufacturing Sectors

−0.08

−0.1

−0.12

−0.08

−0.1

−0.12

−0.14

−0.14

−0.16

−0.16

−0.18
1992

1994

1996

1998

2000

2002

2004

−0.18
1992

2006

1994

1996

−0.02

−0.02

−0.04

−0.04

−0.06

−0.06

−0.08

−0.1

−0.12

−0.16

1998

2000

2004

2006

2002

2004

2006

−0.12

−0.16

1996

2002

−0.1

−0.14

1994

2000

−0.08

−0.14

−0.18
1992

1998

(h) Japan

Predicted Change in Correlation

Predicted Change in Correlation(g) United States

2002

2004

−0.18
1992

2006

(i) Brazil

1994

1996

1998

2000

(j) China

Notes: This table plots the yearly estimates of the change in aggregate correlation between France and each country according tofor the manufacturing sector, along with 2-standard error bands.

29

30

SectorAgriculture, forestry and fishing
Mining and quarrying
Food and tobacco
Textile, wearing apparel and leather
Wood products
Paper products, publishing
Coke, refined petroleum, nuclear fuelChemical industry
Rubber and plastics
Mineral products
Basic metals
Metal products
Machinery and equipment
Office machinery
Electrical equipment
Radio, TV and communication
Medical and optical instruments
Motor vehicles

St. Dev.
0.23890.25330.23400.31180.26060.25580.32550.31930.3066
0.26890.31890.27150.31220.32410.3096
0.31610.30170.2950Share
0.00490.00370.06350.01500.00490.02350.0241
0.04210.01450.01140.01290.02070.02030.00510.01110.01000.00790.0332

NAF
35
36-37
40-41
45
50
55
60-63
64
70
71
72
73
74
75
80
85
90-93

SectorOther transport equipment
Manufacturing n.e.c.
Electricity, gas, water supply
Construction
Wholesale and retail trade
Hotels and restaurants
Transport
Post and telecommunications
Real estate activities
Rental without operator
Computer services
Research and development
Other business services
Public administration
Education
Health and social work
Personal services

St. Dev.
0.32320.28530.21030.23140.21880.16140.20330.24250.21020.21580.26950.29150.23840.17340.22830.14900.1986

Share
0.01130.00960.02920.04950.36890.01410.03990.02260.02350.00700.01140.00150.05780.00030.00140.00690.0164

Notes: This table presents the standard deviations of firm-destination growth rates broken down by sector over 1991–2007. “Share” is the share of thesector in total sales. The manufacturing sector covers NAF sectors 15 to 37.

NAF
01-05
10-14
15-16
17-19
20
21-22
23
24
25
26
27
28
29
30
31
32
33
34

Table A1. Firm-Level Volatility by Sector