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Export Decision under Risk∗
José De Sousa, Anne-Célia Disdier and Carl Gaigné†
February 19, 2015
Very preliminary and incomplete
Abstract
Does demand volatility matter for exports? How do exporting firms deal withskewed demand? A simple model of risk aversion shows that exporters react toan increase in demand volatility in destination markets by increasing their exportprices and decreasing their export volumes. In sharp contrast, exporters decreaseprices and increase volumes when demand skewness rises. We also show that themoments of the demand distribution affect the extensive margin of trade. Thesetheoretical predictions are put to the test by using French firm-level exports acrossdestination markets with different levels of demand volatility and skewness. Thefirm-level results, over the period 2000-2009, are consistent with our predictions.
JEL classification: D81, F12, L25Keywords: Uncertainty, Demand volatility, Firm exports, Skewness
1 Introduction
Does demand volatility matter for exporters supply decisions? According to the Capgem-
ini 2011 survey of large companies, 40% of respondents say that demand volatility is their
number one business driver. Indeed, in numerous industries, sellers need to decide pro-
duction levels or input procurements before the output is marketed or the market price
for output is known. Demand cannot be known for certain at the time the contracts with∗We thank Holger Breinlich, Gabriel Felbermayr, Keith Head, Nuno Limão, Gianmarco Ottaviano,
John Morrow, Denis Novy, Mathieu Parenti, Veronica Rappoport, Daniel Sturm and seminar and con-ference participants at CEPII, U. of Caen, London School of Economics, U. of Munich (ETSG), U. ofStrathclyde, and U. of Tours for their helpful comments. This work is supported by the French NationalResearch Agency, through the program Investissements d’Avenir, ANR-10-LBX-93-01.†José De Sousa: Université Paris-Sud, RITM and Sciences-Po Paris, LIEPP, [email protected].
Anne-Célia Disdier: Paris School of Economics-INRA, [email protected]. Carl Gaigné: INRA,UMR1302 SMART, Rennes (France) and Université Laval, CREATE, Québec (Canada). Email:[email protected].
1
the importers are signed as expenditures for an industry are subject to random shocks. In
this paper, we analyze how demand volatility affect the export decision at the firm level.
In most of the trade literature, the firm knows the state of demand function with cer-
tainty and only the level of foreign market size plays a key role in firm export performance.
However, for a same level of demand (apparent absorption), we observe a significant de-
mand volatility in destination markets. This point is illustrated in Figure (1) from data
on world sectoral-level data.
Figure 1: Demand (apparent consumption) level and demand volatility
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PANPAN
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PER
PERPERPERPER
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PERPER
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PERPER
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POLPOL
POLPOLPOL
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POLPOL
POL POLPOL
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POLPOL
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POLPOLPOL POL
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POLPOL POL
POL
POL POL
PRTPRT
PRT
PRTPRT
PRT
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PRTPRT PRTPRT
PRT
PRT PRT
PRT
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PSE
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QAT
ROMROM
ROM
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ROM
ROM
ROM
ROM
ROM
ROM
ROM
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ROM ROM
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RUSRUS
RUSRUS RUS
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UKRUKR
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USAUSA
USA
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USA
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USA USA
USA
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USAUSA
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VNM
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YEMYEM
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ZAF
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05
1015
20Lo
g of
App
aren
t Con
sum
ptio
n (M
ean)
- 3
dig
it
-4 -2 0 2Log of Apparent Consumption Volatility (sd) - 3 digit
Note: 82 countries, 62 sectors, 2005.
Under demand or price uncertainty, risk-averse firms have to manage their risk expo-
sure. Indeed, there exists some delay between the time an export decision is made and the
time the corresponding output reaches the market. Hence, during this delay, the foreign
demand or market price can change so that there is an uncertainty that the decision-maker
has to handle. The literature on production decision under risk shows that an increase
in risk (as measured by a higher volatility) has a negative effect on output size when the
decision maker is averse to risk.
As a result, if firms are risk-averse, they should export less to a country with more
volatile demand, ceteris paribus. In other words, a country with higher demand volatil-
ity should import less. Using the same database of world sectoral-level data, we find
2
prima facie evidence of a negative relationship between demand volatility and imports,
as depicted in Figure (2).1
Figure 2: Relationship between imports and demand volatility
AFGAFG
AFGAFGAFG
AFG
AFGAFGAFGAFG
AFGAFG
AFGAFG
AFG
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AFG
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AFGAFG
ALBALBALB
ALBALBALBALBALBALBALB
ALBALBALBALB ALB
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ALBALBARGARG
ARG
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ARM
ARMARMARMARMARMARM
ARMARM
ARM
ARM
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BLXBLXBLXBLXBLX BLX
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BOLBOL BOLBOL
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BOLBOL BOLBOL
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DNKDNKDNKDNKDNKDNK DNKDNKDNKDNK
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DNKDNKDNKDNK DNKDNKDNK
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DNKDNKDNKDNKDNKDNKDNKDNKDNK DNK
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JPNJPNJPNJPNJPNJPN JPNJPN
JPN JPNJPNJPNJPNJPNJPN JPN
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JPNJPNJPNJPNJPNJPNJPNJPNJPN JPN JPNJPN JPN
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JPNJPNJPNJPNJPNJPNJPN JPN
JPNJPNJPNJPNJPNJPNJPN JPN
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JPNJPNJPNJPNJPNJPNJPN JPNJPNJPN
JPNJPNJPN JPNJPNJPNJPNJPNJPN JPNJPNJPNJPN
JPNJPNJPNJPNJPN
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KORKORKORKORKORKORKORKOR KOR
KORKORKORKORKORKORKOR
KORKORKORKORKORKOR
KOR KORKOR KORKORKORKORKORKORKORKOR
KOR
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KOR KOR
KORKORKOR
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KORKORKORKORKORKORKORKORKOR
KORKORKOR
KOR KORKORKORKORKORKORKORKORKOR
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KORKORKORKORKOR
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KORKORKORKORKORKORKORKORKOR
KORKORKOR
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KORKORKORKORKORKOR KOR
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KORKORKORKORKORKORKORKORKOR
KORKORKORKORKORKORKOR
KORKORKORKORKORKORKORKORKOR
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KORKORKORKORKORKORKOR
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KORKORKORKORKORKORKORKORKOR
KORKORKORKORKORKORKORKORKOR
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KORKORKORKORKORKORKORKORKOR KORKORKORKOR
KORKORKOR
KORKORKORKORKORKOR
KOR
KORKORKORKORKORKOR KORKORKOR
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KORKORKOR
KORKORKORKOR
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KORKORKOR
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KORKORKORKORKORKORKORKOR KOR
KORKORKORKORKORKORKOR KORKOR
KORKORKOR
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KORKORKORKORKORKORKOR
KORKORKORKOR
KORKORKORKORKORKORKOR
KORKORKORKORKORKORKOR
KORKORKORKORKOR
KORKOR KORKORKORKORKORKORKOR
KORKORKORKORKORKOR
KORKORKOR
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KORKOR
KORKOR
KORKOR
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KORKORKOR
KORKORKORKOR
KORKORKOR
KORKOR KORKOR
KORKORKORKORKORKORKOR
KWT
KWTKWT
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LKA LKA
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LTULTULTULTULTU
LTULTULTU
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LTULTU
LTULTU
LTULTULTULTU
LTULTULTULTULTULTU
LTULTULTULTU
LTULTULTULTULTULTU
LTULTULTULTULTULTU
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LTULTULTULTU
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LTULTULTULTULTULTULTU LTULTULTU LTU
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LTULTULTULTULTULTULTULTU LTU LTU
LTULTULTULTU LTU LTULTULTULTU
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LTULTU LTU
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LTULTU
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LTULTULTULTU
LTULTULTU LTU
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LTULTULTULTULTULTU
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LTULTULTU
LTULTULTULTU LTU
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LTU LTULTULTULTU
LTU LTULTULTU
LTULTU
LTULTULTU
LTULTULTULTULTU
LTULTULTULTULTULTU
LTULTULTULTU
LVALVALVALVALVA
LVALVALVA
LVA LVA
LVA LVALVALVALVALVA
LVALVA
LVALVALVALVA
LVALVALVALVA
LVA LVALVALVALVALVALVALVA
LVALVA
LVA LVA
LVA
LVALVALVA
LVALVA
LVALVA LVA
LVALVALVALVA
LVALVALVALVALVA
LVALVA
LVALVALVALVA
LVALVALVALVALVA
LVA
LVALVA LVALVA
LVALVALVALVALVA
LVALVA
LVALVA
LVALVA
LVA
LVALVALVA LVA
LVALVALVALVA
LVALVALVALVA LVALVALVA
LVA
LVALVALVA
LVALVA
LVALVA
LVA
LVALVA
LVALVALVALVALVALVALVALVA
LVALVA
LVA
LVA LVALVALVALVA
LVALVA
LVALVALVALVALVA LVALVA
LVALVALVA
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LVALVALVALVALVA
LVALVA
LVALVA
LVALVALVA
LVALVALVALVA
LVA
LVALVALVA
LVALVALVALVALVALVA
LVALVALVA
LVA
LVALVALVALVA
LVALVA
LVALVALVA
LVALVALVALVALVALVALVA
LVALVA
LVALVALVA
LVALVA
LVALVA
LVA
LVALVALVALVALVALVALVA
LVALVA
LVA
LVALVALVALVALVA
LVALVA
LVALVA
LVALVA
LVALVALVALVALVALVALVA
LVALVA
LVA LVALVALVALVALVALVA
LVALVA
LVALVALVALVALVALVA
LVALVA
LVALVA
LVA
LVALVALVA
LVALVA
LVALVALVALVALVALVALVA
LVA
LVA
LVALVALVA
LVALVALVA
LVA
LVALVALVALVALVA
LVALVALVA
LVA LVALVA
LVALVALVALVALVA
LVALVALVALVALVALVA
LVALVALVALVALVALVA
LVALVALVA
LVA
LVALVALVA
LVALVA
LVALVA LVA
LVALVALVALVALVALVA
LVALVALVA
LVA
LVALVALVALVALVALVA
LVALVA
LVA LVALVALVA
LVALVA
LVA
LVALVA
LVALVALVALVALVALVA
LVALVA
LVA
LVA
LVALVA
LVA
LVALVA
LVA
LVA LVA
LVA
LVA
LVALVA
LVA
LVA
LVA
LVA
LVA
LVALVA
LVALVA
LVALVA LVA
LVA
LVALVALVALVALVA
LVALVALVA
LVALVALVALVALVA
LVALVALVALVA
LVA
MACMAC
MAC
MACMAC
MACMAC
MACMACMAC
MACMAC
MACMACMACMAC
MACMAC
MACMAC
MAR MARMARMARMARMAR
MARMARMAR
MARMARMARMAR
MARMAR
MAR
MARMARMARMARMARMAR
MARMAR MAR
MAR
MAR MARMARMARMARMAR MAR
MAR
MAR MARMARMARMARMAR
MARMARMAR
MAR MARMARMAR MARMAR
MAR
MAR MARMARMARMARMAR MARMAR
MARMARMARMARMARMAR
MARMARMAR
MARMARMAR MAR MARMARMARMARMARMARMAR MARMARMAR MAR
MARMAR
MARMARMARMARMARMARMAR
MARMARMARMARMARMAR
MARMARMAR
MARMAR
MARMARMARMAR
MARMARMAR
MARMAR
MARMARMARMAR
MARMAR
MARMAR
MARMARMAR
MAR
MAR
MAR MARMAR
MARMAR
MARMAR
MAR MARMARMAR MAR
MARMARMAR
MARMARMARMAR
MARMARMARMARMAR
MARMARMARMARMARMAR MAR
MARMAR
MARMAR
MARMAR
MARMARMARMARMARMARMARMAR MAR
MAR MARMARMARMAR
MAR MAR
MARMARMARMARMAR
MARMARMARMAR
MARMARMARMAR
MARMAR
MAR
MARMAR
MARMARMAR MAR
MAR
MARMAR
MAR
MARMARMAR
MARMAR
MARMAR
MARMARMARMARMAR
MARMARMARMARMAR
MARMARMARMAR
MARMARMAR
MARMARMAR MARMARMARMARMARMAR
MARMARMARMAR
MARMARMARMARMARMARMARMARMAR MAR
MARMARMAR
MARMAR
MAR
MARMARMARMARMARMARMARMAR
MARMAR MARMARMAR
MARMARMARMARMARMAR
MARMARMARMAR MARMARMAR
MARMAR
MARMARMARMAR
MARMARMAR
MARMARMARMARMARMARMAR
MARMARMARMARMARMAR
MARMAR
MARMARMARMAR MAR
MAR
MARMAR
MARMARMAR
MARMAR
MAR
MARMAR MARMAR
MARMAR
MAR MAR MARMARMAR
MARMAR
MARMAR
MARMARMAR
MARMAR
MAR
MAR
MARMARMAR
MARMAR
MARMARMARMAR
MAR MARMARMARMARMAR
MARMAR
MARMARMARMARMAR
MARMARMAR
MAR MAR MARMARMAR MAR
MARMARMAR
MDAMDA
MDAMDA
MDAMDA
MDAMDA
MDA
MDA
MDAMDA
MDAMDA
MDAMDAMDA
MDAMDA MDA
MDA MDAMDA
MDAMDA
MDA
MDA
MDA
MDA
MDAMDA
MDAMDAMDAMDA
MDAMDAMDA
MDAMDAMDA
MDAMDAMDA MDA
MDAMDA
MDAMDAMDA
MDA
MDAMDAMDA
MDAMDAMDAMDA
MDAMDAMDAMDAMDAMDAMDA MDA MDAMDA
MDAMDAMDAMDAMDAMDAMDA
MDAMDAMDA
MDAMDAMDAMDA
MDA
MDAMDA
MDA
MDAMDA
MDAMDAMDAMDA
MDAMDA
MDAMDA
MDA
MDAMDA
MDAMDAMDA
MDAMDAMDAMDA
MDAMDA
MDAMDAMDAMDA
MDAMDA MDA
MDAMDA
MDAMDAMDAMDAMDA
MDAMDAMDAMDAMDAMDAMDAMDAMDA
MDA
MDAMDAMDAMDA
MDAMDA
MDAMDAMDAMDA
MDAMDAMDAMDA
MDAMDAMDAMDA
MDAMDA
MDAMDA
MDA
MDAMDAMDA
MDA
MDA
MDA
MDA
MDA MDAMDA
MDAMDAMDAMDA
MDA MDAMDAMDAMDAMDA
MDAMDAMDA
MDAMDA
MDAMDAMDAMDAMDAMDA
MDAMDAMDA
MDA
MDAMDA
MDAMDA
MDAMDA
MDA
MDAMDA
MDA
MDAMDAMDA
MDAMDA
MDAMDAMDAMDA
MDA
MDA
MDAMDA
MDAMDA
MDAMDAMDA MDA
MDA
MDAMDAMDAMDAMDA
MDAMDAMDAMDA
MDA
MDAMDA
MDA
MDAMDA
MDAMDA
MDAMDAMDA
MDAMDA
MDA
MDAMDAMDA
MDAMDA
MDAMDAMDAMDAMDA
MDA
MDAMDAMDAMDAMDA
MDAMDA
MDA
MDA
MDAMDAMDA
MDAMDAMDAMDA
MDAMDA
MDA
MDAMDAMDA
MDAMDAMDAMDA
MDAMDAMDA
MDA
MDAMDAMDA
MDAMDAMDAMDA
MDAMDA
MDAMDAMDA
MDAMDA
MDAMDA
MDA
MDAMDAMDA
MDAMDAMDA
MDAMDA
MDA MDA
MDAMDA
MDA
MDA
MDA
MDA
MDA MDA MDA
MDAMDAMDA
MDA
MDAMDAMDA
MDA
MDAMDAMDAMDAMDA
MDA MDAMDA MDAMDAMDA
MDAMDAMDA
MDAMDA
MDA
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MDAMDAMDA
MDAMDAMDA
MDAMDA
MDA
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MDAMDAMDAMDA
MDAMDA
MDAMDA
MDAMDA
MDA MDAMDA
MDA
MDA
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MDAMDAMDA
MDAMDA
MDAMDAMDA
MDAMDA
MDA
MDAMDAMDA
MDAMDAMDA
MDAMDA
MDAMDAMDA
MDA
MDAMDAMDAMDA
MDAMDAMDA
MDAMDA MDA
MDGMDGMDG
MDGMDGMDG
MDGMDGMDG
MDGMDGMDG
MDGMDGMDG
MDGMDGMDG
MDGMDGMDGMDGMDG
MDG
MEXMEXMEXMEXMEXMEXMEXMEXMEXMEX
MEXMEX MEXMEXMEX
MEXMEXMEX
MEXMEXMEX MEXMEX
MEX
MEXMEX
MEXMEXMEXMEXMEX MEX
MEXMEXMEXMEX
MEXMEX
MEXMEX MEX MEXMEXMEXMEX
MEXMEXMEX
MEXMEXMEX MEXMEXMEX
MEXMEX
MEXMEX
MEXMEXMEX MEXMEXMEXMEXMEX MEXMEXMEXMEXMEXMEXMEXMEXMEXMEX
MEXMEX
MEX MEX MEXMEXMEXMEXMEX
MEX MEXMEXMEXMEXMEXMEXMEX
MEXMEXMEX MEXMEXMEXMEXMEXMEXMEX
MEX
MEXMEXMEXMEXMEXMEX MEXMEXMEXMEXMEX
MEXMEX
MEX MEXMEX MEX MEXMEXMEXMEXMEXMEX
MEXMEXMEX
MEX
MEXMEXMEX MEXMEXMEXMEXMEX
MEXMEXMEX
MEX
MEXMEXMEX MEX
MEX
MEXMEXMEXMEX
MEXMEXMEXMEX MEXMEXMEXMEXMEX
MEX
MEX
MEXMEXMEX MEXMEXMEXMEXMEXMEXMEX
MEXMEXMEX MEX MEXMEXMEXMEX MEX
MEX
MEXMEXMEXMEX
MEXMEXMEXMEX
MEXMEX
MEX MEXMEX MEXMEXMEXMEXMEXMEXMEX
MEXMEXMEX MEX MEXMEXMEXMEXMEX
MEXMEX MEXMEX
MEX MEXMEXMEXMEXMEXMEX
MEXMEXMEXMEXMEXMEXMEXMEXMEX
MEX
MEX MEXMEXMEXMEXMEX
MEXMEXMEXMEX
MEXMEX MEX MEXMEXMEXMEXMEXMEX
MEX
MEXMEXMEX MEXMEXMEXMEXMEXMEXMEXMEXMEX MEX
MEXMEXMEX MEXMEXMEX
MEXMEXMEXMEXMEX MEXMEXMEXMEX
MEXMEX
MEXMEXMEX MEXMEX MEX
MEXMEX
MEXMEXMEXMEXMEX
MEXMEX
MEXMEX MEXMEX
MEXMEXMEXMEX MEXMEX MEX MEX MEXMEX
MEXMEXMEXMEXMEXMEXMEXMEX
MEX MEXMEXMEXMEXMEXMEX
MEXMEXMEX
MEXMEXMEXMEXMEXMEX
MEXMEXMEXMEX
MEXMEX
MEXMEXMEX
MEX
MEXMEXMEXMEXMEXMEXMEXMEX MEX
MEX
MEXMEXMEXMEXMEX
MEXMEXMEXMEXMEX
MEXMEXMEX MEXMEXMEXMEXMEX MEXMEX
MEX
MEX
MEX
MEX
MEXMEX
MEXMEX
MEXMEXMEX
MEX
MEXMEXMEX
MEX
MEXMEXMEXMEXMEX
MEX
MEXMEXMEX
MEXMEXMEXMEX
MEXMEXMEX
MKDMKD MKDMKDMKDMKD
MKDMKD MKDMKD
MKDMKD
MKDMKDMKD
MKDMKDMKDMKDMKDMKD
MKD MKD
MKDMKD
MKD MKD
MKDMKDMKDMKD
MKDMKDMKDMKDMKD
MKDMKD MKD
MKDMKDMKDMKDMKD
MKD
MKDMKDMKD
MKDMKDMKDMKDMKD
MKD MKD MKD MKDMKDMKDMKD
MKDMKDMKD
MKDMKDMKDMKDMKD MKD
MKDMKDMKD
MKDMKDMKD
MKD MKDMKDMKDMKD
MKDMKD
MKD
MKD MKD MKD MKDMKDMKDMKD
MKDMKDMKDMKDMKD MKD MKDMKDMKD
MKDMKDMKDMKDMKD
MKDMKD
MKD MKD
MKDMKDMKDMKD
MKD MKDMKDMKDMKDMKD MKDMKD MKD
MKD
MKDMKDMKDMKD
MKDMKDMKDMKDMKD
MKDMKDMKD MKDMKDMKD
MKD
MKDMKD
MKDMKDMKD
MKDMKDMKD
MKDMKDMKD MKDMKD
MKDMKDMKD
MKD
MKD
MKD
MKDMKD
MKDMKD MKD
MKDMKDMKDMKD
MKDMKD
MKDMKDMKDMKD
MKD MKDMKDMKD MKDMKD
MKD
MKDMKD
MKDMKD
MKDMKDMKD
MKD MKDMKDMKDMKD MKD MKD
MKDMKDMKD
MKDMKD
MKD
MKD
MKDMKDMKD
MKDMKD
MKDMKD
MKD
MKDMKDMKDMKDMKD
MKD
MKD
MKD
MKDMKDMKD
MKDMKD
MKDMKD
MKDMKD
MKD MKD
MKD
MKDMKDMKD
MKDMKDMLTMLTMLT MLTMLTMLTMLTMLT
MLT
MLT MLTMLTMLTMLTMLTMLT
MLTMLT MLTMLT MLTMLTMLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT
MLT
MLTMLTMLT
MLTMLT
MLTMLT
MLT
MLTMLTMLT MLT
MLTMLTMLTMLT
MLTMLTMLT
MLTMLT
MLTMLTMLT
MLTMLTMLTMLT
MLT
MLTMLT
MLT
MLT
MLTMLT
MLT
MLTMLT
MLTMLTMLTMLTMLTMLTMLTMLT MLT
MLT MLTMLTMLTMLTMLTMLTMLTMLTMLTMLTMLTMLT
MLTMLTMLT
MLTMLT
MLTMLT MLT MLT
MLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT
MLT
MLTMLTMLTMLT
MLT
MLTMLTMLTMLTMLTMLT MLTMLT MLT
MLTMLTMLT MLTMLT MLTMLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT MLTMLTMLT MLTMLTMLTMLTMLTMLTMLTMLTMLTMLT
MLTMLT
MLT
MLTMLTMLTMLT
MLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT MLTMLTMLTMLTMLT
MLTMLTMLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLT
MLTMLTMLTMLT MLTMLT
MLT MLT MLTMLTMLTMLTMLTMLTMLT
MLTMLTMLTMLT MLTMLT
MLT
MLT
MLTMLTMLTMLT
MLTMLTMLTMLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT
MLTMLT
MLT
MLTMLT MLTMLT
MLT
MLTMLT
MLTMLT MLT
MLTMLTMLTMLTMLTMLTMLTMLTMLT
MLT
MLTMLTMLTMLTMLT
MLTMLTMLT
MLTMLTMLT MLT MLTMLTMLT
MLTMLT
MLT MLT MLTMLT
MLT
MLTMLTMLTMLT
MLT
MLT
MLTMLTMLT
MLTMLTMLTMLTMLTMLTMLT MLTMLT
MLT
MLTMLTMLT
MLTMLTMLT MLT
MLT
MLT
MLT
MLT
MLT
MLT
MLTMLTMLT
MLT MLTMLTMLTMLTMLT
MLTMLT
MLT
MLTMLTMLT
MLTMLT MLT
MNGMNG
MNGMNGMNG MNG
MNGMNG
MNGMNG
MNGMNG
MNGMNGMNG
MNGMNGMNG
MNG
MNG
MNG
MNGMNG
MNGMNG MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNGMNG
MNG
MNG
MNG
MNG
MNG
MNGMNGMNG
MNGMNGMNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNG
MNGMNG
MNG
MNG
MNGMNG
MNG
MNG
MNGMNGMNGMNGMNG
MNG
MNG
MNG
MNG
MUSMUSMUSMUSMUSMUSMUS MUSMUSMUS
MUSMUS
MUSMUSMUS
MUSMUSMUSMUS
MUS
MWIMWI
MWIMWIMWIMWIMWI MWI
MWI MWIMWIMWIMWIMWI MWI MWI
MWIMWI
MWI
MWIMWIMWI
MWIMWI
MWIMWIMWI
MWIMWIMWI
MWI
MWI
MWIMWI
MWI
MWI
MWIMWI
MWIMWI
MWI
MWI
MWI
MWIMWIMWIMWIMWI
MWI MWIMWI
MWIMWIMWI
MWIMWI
MWIMWIMWIMWI MWIMWI
MWI
MWIMWIMWI
MWI MWI
MWI MWIMWIMWIMWIMWI
MWIMWI
MWI
MWI
MWIMWI MWIMWI
MWIMWI
MWI
MWI
MWIMWI
MWI
MWIMWI
MWIMWI
MWIMWI
MWI
MWI
MWIMWI
MWIMWI
MWIMWIMWIMWI
MWIMWI
MWIMWIMWI MWI
MWIMWI
MWI
MWIMWIMWIMWI
MWIMWIMWI MWIMWIMWI
MWIMWI
MWIMWIMWIMWI
MWI MWI
MWI MWIMWI
MWI
MWIMWI
MWIMWI
MWIMWI
MWIMWIMWI MWIMWI
MWI
MWIMWIMWIMWIMWIMWI
MWIMWI
MWIMWIMWIMWI
MWIMWI
MWI MWIMWIMWI
MWIMWI
MWI
MWIMWIMWIMWI
MWI
MWI MWIMWIMWIMWI
MWI
MWI
MWI
MYS MYSMYSMYSMYSMYS
MYSMYSMYS
MYSMYS MYSMYS
MYSMYS
MYS MYSMYSMYSMYSMYS MYS
MYSMYS
MYS MYSMYSMYSMYSMYSMYSMYSMYS
MYSMYS MYS
MYS MYSMYSMYSMYS
MYS
MYSMYSMYS
MYSMYSMYS
MYSMYS MYSMYSMYSMYSMYS MYS
MYSMYSMYSMYSMYS MYSMYSMYSMYSMYSMYS MYS MYSMYSMYS
MYSMYSMYS
MYS
MYSMYSMYSMYS
MYSMYSMYS
MYSMYSMYS
MYSMYSMYS
MYSMYS MYSMYSMYS
MYSMYSMYSMYS
MYS MYSMYSMYSMYSMYSMYSMYSMYS
MYSMYSMYSMYS
MYSMYS
MYSMYS
MYSMYSMYS MYSMYSMYSMYSMYS
MYSMYSMYS
MYS
MYSMYS
MYSMYSMYSMYS
MYSMYSMYSMYSMYS
MYS MYSMYSMYSMYSMYSMYSMYS
MYS
MYS MYSMYS MYSMYSMYSMYSMYSMYS
MYS MYSMYSMYSMYSMYS
MYSMYSMYSMYSMYS
MYSMYSMYS
MYSMYSMYSMYSMYSMYS
MYSMYSMYSMYS MYS
MYSMYSMYSMYS
MYSMYSMYSMYS
MYSMYS
MYSMYS
MYSMYSMYSMYS
MYSMYSMYSMYS
MYS
MYSMYSMYS
MYSMYSMYSMYSMYSMYSMYS
MYSMYSMYSMYSMYSMYSMYSMYSMYSMYS
MYSMYS MYSMYSMYSMYSMYS
MYSMYSMYS
MYS MYSMYSMYS MYSMYS
MYSMYSMYSMYSMYSMYS
MYSMYSMYSMYSMYSMYS
MYSMYSMYS
MYSMYSMYS
MYSMYSMYS
MYSMYSMYS
MYSMYSMYSMYSMYSMYS
MYS MYS MYSMYSMYSMYS
MYSMYS MYS MYSMYS MYS
MYSMYSMYSMYS MYSMYS
MYSMYSMYSMYS
MYSMYS
MYSMYSMYSMYSMYSMYS
MYS
MYS
MYS
MYSMYSMYSMYSMYS
MYSMYSMYSMYSMYS
MYS
MYSMYSMYS
MYSMYS
MYS
MYSMYS
MYSMYSMYSMYS
MYS MYS
MYSMYSMYS
MYSMYS
MYS
MYSMYSMYS
MYSMYS
MYS
MYS
MYSMYSMYSMYSMYS
MYSMYS
MYSMYS MYSMYSMYSMYSMYS
MYSMYSMYSMYSMYS
MYSMYS MYSMYSMYS MYS
MYSMYS
NGA
NGA
NGANGANGANGA
NGANGA
NGA
NGANGANGA
NGANGA
NGANGANGA
NGA
NGA
NGANGA
NGA
NGANGA
NGA
NGA
NGA
NGANGA
NGA
NGA
NGANGA
NGANGANGA
NGA
NGANGA
NGANGA
NGANGA
NGA
NGANGA
NGANGA
NGANGANGA
NGANGANGANGANGA
NGA NGANGA
NGA
NGA
NGA
NGANGA
NGANGA
NLDNLDNLDNLDNLD
NLD
NLDNLDNLD
NLD NLDNLDNLDNLDNLDNLD
NLDNLD
NLD NLDNLD
NLDNLDNLD
NLDNLD
NLD
NLDNLDNLDNLDNLDNLDNLDNLDNLD
NLDNLDNLD
NLDNLDNLDNLD
NLDNLDNLD NLDNLDNLD NLDNLDNLDNLDNLDNLDNLDNLDNLD
NLDNLDNLD
NLDNLDNLD NLDNLDNLD NLDNLD
NLDNLDNLD
NLDNLDNLD NLDNLDNLD
NLD
NLDNLDNLDNLDNLD NLDNLD NLDNLDNLDNLD
NLDNLDNLDNLDNLDNLD
NLD
NLDNLDNLD
NLDNLDNLDNLDNLD
NLDNLD
NLD
NLDNLDNLDNLDNLDNLDNLDNLD
NLD
NLDNLDNLDNLD NLDNLDNLD NLD
NLD
NLDNLDNLDNLDNLDNLDNLD
NLDNLD
NLDNLDNLDNLDNLDNLDNLDNLD
NLD
NLDNLDNLDNLDNLDNLD
NLDNLD NLD
NLD
NLDNLDNLDNLD NLD
NLDNLDNLD
NLDNLDNLDNLD NLDNLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLD NLDNLDNLDNLD
NLDNLDNLDNLDNLD NLDNLD NLD
NLD NLDNLDNLD
NLD NLDNLDNLDNLDNLDNLDNLDNLD
NLDNLD NLDNLDNLDNLDNLDNLDNLD NLDNLDNLDNLDNLDNLDNLD
NLDNLD
NLDNLDNLDNLDNLDNLD
NLD
NLDNLD
NLDNLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLD NLDNLDNLD
NLDNLDNLD NLD NLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLD
NLDNLDNLD
NLDNLDNLDNLDNLD NLDNLD
NLDNLD NLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLD
NLD NLDNLDNLD
NLD NLD NLD
NLDNLD
NLD
NLDNLD
NLDNLDNLDNLD
NLD
NLD
NLDNLD NLD
NLDNLDNLDNLDNLDNLDNLDNLD
NLDNLDNLDNLDNLDNLDNLD
NLDNLD NLDNLDNLDNLDNLDNLDNLD NLD
NLDNLD
NLDNLD
NLD
NLDNLD
NLDNLD
NLD
NLD
NLDNLD
NLDNLDNLD
NLD NLDNLDNLDNLD
NLD
NLDNLDNLD
NLDNLDNLD
NLDNLDNLD
NLD
NLDNLDNLDNLDNLDNLDNLD NLDNLDNLDNLD
NLD
NLDNLDNLDNLDNLD
NLD
NORNOR NORNOR
NORNORNORNOR
NOR
NORNORNORNORNORNORNORNORNOR
NORNORNORNOR NOR NOR
NOR
NORNORNORNORNORNOR
NORNORNOR
NORNOR
NOR
NORNORNOR
NORNOR
NORNOR
NORNOR NORNORNORNORNORNOR
NORNOR NOR
NOR
NORNORNORNORNOR
NORNORNOR
NORNOR NOR NORNORNORNORNORNOR
NORNORNORNORNORNOR
NORNORNOR
NORNORNORNORNORNORNOR NORNORNOR NORNORNOR
NORNORNORNOR NORNORNORNORNOR
NORNORNORNORNORNORNOR
NOR NOR NORNORNORNORNORNORNORNORNOR
NORNORNORNOR NORNORNORNORNOR
NORNOR NORNOR NORNORNORNORNORNOR NOR
NORNORNORNOR
NOR
NORNORNOR
NORNORNORNORNORNOR
NORNORNORNOR
NORNORNORNORNOR
NORNORNORNOR
NORNORNORNOR NOR NORNORNORNORNORNOR
NORNORNORNORNORNORNORNOR NOR
NORNORNORNOR
NOR
NORNORNORNOR
NORNOR
NORNORNORNORNOR
NORNORNORNOR
NOR NORNORNOR NOR
NORNORNORNOR
NORNORNOR
NORNOR
NORNORNORNORNORNORNOR
NORNORNORNORNORNORNOR
NORNORNORNOR
NORNORNORNORNOR NORNORNORNOR
NORNORNORNORNORNORNORNOR NOR
NORNORNORNORNORNORNOR
NORNORNORNORNORNORNOR
NORNOR
NORNORNORNORNOR
NOR NOR
NORNORNOR NORNORNORNOR
NORNOR
NOR
NORNORNORNOR
NORNOR
NORNORNORNOR
NORNORNORNORNORNOR
NORNORNOR
NORNORNORNORNORNORNOR
NOR NOR
NORNORNOR
NORNORNORNOR
NOR NORNORNORNORNORNORNORNOR
NOR NORNORNORNORNOR
NORNORNORNOR
NORNORNORNORNORNOR
NOR NOR
NOR NORNORNORNORNOR
NOR NORNORNOR NORNORNORNORNORNORNOR
NORNORNOR
NOR NORNORNOR
NOR
NOR
NOR
NORNOR NOR
NORNOR
NORNOR
NORNORNORNORNORNOR
NORNOR
NORNOR
NOR
NOR
NOR NOR
NORNORNORNORNORNORNORNORNOR
NORNORNORNORNORNORNORNORNOR
NZLNZL
NZLNZL
NZL NZLNZLOMNOMNOMN OMN OMNOMNOMN
OMNOMNOMN
OMNOMN OMN
OMN OMNOMNOMNOMN
OMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMN
OMN
OMN
OMNOMNOMNOMN
OMNOMNOMNOMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMNOMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMNOMNOMN
OMN
OMNOMNOMN
OMN
OMNOMNOMN
OMN
OMN
OMNOMN
OMNOMNOMN
OMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMNOMNOMN
OMN
OMN
OMN
OMN
OMN
OMNOMNOMN
OMNOMN
OMN
OMNOMNOMN
OMN
OMNOMN
OMNOMNOMNOMN
OMN
OMN
OMNOMN
OMNOMN
OMN
OMNOMN
OMN
OMN
OMNOMNOMN OMN
OMN
OMN
OMNOMNOMN
OMNOMNOMNOMNOMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMNOMNOMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMNOMNOMNOMNOMNOMN
OMNOMN
OMNOMNOMNOMNOMNOMN OMN
OMN
OMN
OMN
OMNOMNOMN
OMNOMNOMNOMN
OMNOMN
OMNOMNOMN OMN
OMN
OMNOMNOMN
OMN
OMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMNOMN
OMN
OMNOMNOMNOMNOMN
OMNOMN
OMNOMN
OMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMNOMNOMNOMN
OMNOMNOMN
OMNOMN
OMN OMNOMNOMNOMN
OMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMNOMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMNOMN
OMNOMNOMNOMN
OMN
OMNOMNOMN
OMNOMN
OMN
OMN
OMNOMN
OMN
OMNOMNOMN
OMN
OMNOMNOMNOMNOMNOMN
OMNOMN
OMN
OMN
OMNOMN
OMN
OMN
OMN
OMN
OMNOMNOMN
OMNOMNOMNOMN
OMNOMN
OMNOMN
OMN OMNOMN
OMNOMNOMNOMN
OMNOMN
OMNOMNOMNOMNOMNOMN
OMN
OMN
OMNOMNOMNOMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMN
OMN OMNOMNOMNOMN
OMNOMNOMNOMNOMNOMNOMN
OMNOMNOMNOMN
OMNOMNOMNOMNOMN
OMNOMNOMN
OMNOMN
OMNOMN OMN OMNOMNOMN
OMNOMN
OMNOMN
OMNOMNOMN
OMN
OMN
OMNOMN
OMNOMN
OMN
OMN
OMNOMNOMN
OMNOMN
OMN
OMNOMN
OMNOMN
OMN
OMN
OMN
OMNOMN
OMNOMN
OMN
OMNOMN
OMN
OMNOMN
OMNOMNOMN
OMN
OMNOMNOMN
OMNOMNOMNOMNOMNOMN
OMNOMN
OMNOMN
OMNOMNOMNOMN
OMNOMN
OMNOMNOMN
OMNPAN PAN
PAN
PANPANPANPANPAN
PAN
PAN PAN PANPAN PAN
PAN
PAN
PAN PAN
PANPAN
PAN
PAN
PAN
PAN
PANPAN
PAN PANPAN
PAN
PANPAN
PAN
PAN
PAN PANPAN
PANPANPAN
PAN
PANPAN
PAN
PANPAN PAN
PANPAN
PANPANPAN
PANPANPAN
PANPAN PAN
PAN
PANPAN
PAN
PAN
PAN
PAN
PANPANPAN
PERPER PERPERPERPERPERPER
PERPERPERPERPER
PER
PERPERPER
PERPERPER
PERPERPERPER
PERPERPERPERPERPER
PER PERPER PERPERPER
PER PER
PER
PER
PERPER
PERPERPERPER
PER
PERPERPERPERPERPER
PER PER
PERPERPERPER
PERPERPERPERPER
PERPER PER
PERPER PER
PERPERPER
PERPERPERPERPERPERPER
PERPER
PER PERPERPER
PERPER PER
PERPER PERPER
PERPERPERPER
PER
PERPER
PERPER PERPERPERPERPER PER
PERPERPERPERPER PER
PERPER
PERPERPERPERPER
PER PER
PERPERPERPER
PER
PERPER PER
PERPERPERPERPER
PER
PERPER PER PERPERPERPERPER
PERPERPERPERPERPER
PERPERPERPER
PERPER PER
PERPERPERPER
PERPER
PERPER
PER PER PERPER
PERPERPERPERPERPER
PERPER
PERPER
PERPER
PER
PER
PERPERPER
PERPERPER
PERPERPERPERPERPER
PERPER
PER PERPERPERPER
PERPERPER
PERPER
PER PERPERPERPER
PERPER
PER
PERPERPERPERPER PER
PERPERPERPERPERPER
PERPER
PERPER
PERPER
PERPERPER
PERPERPER
PERPER
PERPER
PERPER
PERPERPER
PERPER PERPERPER
PERPERPERPER
PERPERPERPER
PERPER
PERPERPER
PER
PERPER
PERPERPER
PERPER
PER
PERPERPERPER PER PER
PERPERPERPER
PERPER
PERPER
PERPER
PERPER
PERPER
PERPERPER
PERPERPER PERPER PER
PER
PER
PER
PER
PERPER
PER
PER
PERPERPERPER
PER
PER
PER
PER
PER
PERPERPERPERPERPER
PERPERPER
PERPER
PERPERPERPER
PERPERPERPERPER PER
PERPER
PHLPOLPOL
POL
POLPOL
POLPOLPOL
POL POL
POLPOLPOLPOL
POLPOL
POL
POLPOL
POL
POL POLPOLPOL
POLPOLPOLPOL
POLPOLPOLPOLPOLPOLPOL
POLPOLPOL
POLPOL
POL
POLPOL
POLPOLPOL
POL
POLPOL
POLPOLPOL POL
POLPOLPOLPOLPOLPOLPOLPOLPOL
POL
POL
POLPOL POLPOLPOLPOLPOL
POL
POLPOL POLPOLPOLPOLPOLPOLPOL
POLPOLPOLPOLPOLPOLPOLPOL
POL
POLPOLPOL
POLPOLPOLPOL POLPOLPOL
POL POLPOLPOLPOLPOLPOL
POLPOL
POLPOLPOL POLPOLPOLPOL
POLPOL
POLPOLPOLPOL
POLPOLPOLPOLPOLPOL
POLPOLPOLPOLPOLPOLPOL
POLPOL POL
POLPOLPOLPOL
POLPOLPOLPOLPOL
POLPOLPOLPOLPOLPOLPOL POLPOL
POL
POL
POL
POLPOLPOL
POL
POLPOLPOLPOL
POLPOL
POLPOL
POLPOL
POL POLPOLPOL
POL POLPOLPOL
POL
POL
POL POLPOL
POLPOLPOLPOL POLPOL POL
POLPOLPOL
POLPOL
POLPOLPOLPOLPOLPOL
POLPOLPOLPOLPOLPOL
POLPOLPOLPOL POLPOLPOL
POLPOL
POLPOLPOL
POLPOLPOLPOLPOL
POL
POLPOLPOLPOL
POLPOL
POLPOL
POLPOL
POLPOLPOLPOL
POLPOL
POLPOLPOL
POLPOLPOLPOLPOLPOLPOL
POLPOL
POLPOL POLPOL
POLPOLPOLPOLPOLPOL
POLPOLPOLPOLPOL
POLPOLPOLPOLPOL
POL
POL POLPOLPOLPOLPOLPOL
POLPOLPOL
POLPOLPOL POLPOLPOLPOL
POLPOLPOLPOLPOLPOLPOL POLPOLPOLPOL
POLPOLPOLPOL
POLPOL
POL
POLPOLPOLPOL POLPOL
POLPOLPOL
POLPOLPOLPOL
POLPOLPOLPOL POL
POLPOL POLPOLPOL POLPOL
POLPOL
POLPOL
POLPOL
POLPOL
POL
POL POLPOLPOL
POLPOLPOLPOL
POL
POLPOLPOLPOLPOLPOLPOL POLPOLPOL POLPOL
POLPOL
POLPOL
POLPOLPOL
POL
POLPOLPOL
POL
POL
POL POLPOL POLPOL
POLPOL
POL
POLPOLPOLPOLPOL
POLPOL
POLPOL
POLPOL POLPOL
POLPOL
POLPOLPOL
POLPOLPOLPOL
POLPOL
POL
POLPOLPOL
POLPOLPOL
POLPOLPOL POLPOL
POLPOLPOLPOLPOL
POL
POL
POL
POLPOL
POLPOLPOLPOLPOLPOL
POLPOLPOLPOL
POLPOLPOL
POLPOL
POL
POLPOLPOL
POLPOLPOLPOL
POL
POL
POL
POL
POLPOLPOLPOLPOLPOLPOL
POLPOL
POLPOLPOLPOL POLPOLPOLPOL
POL POLPRTPRTPRTPRTPRTPRTPRTPRT
PRTPRT
PRTPRTPRT PRT
PRTPRTPRTPRTPRT
PRTPRT
PRTPRTPRTPRTPRT PRT
PRT PRTPRTPRTPRT PRT
PRT
PRT
PRTPRT
PRT PRT
PRTPRT
PRTPRT
PRTPRTPRTPRT
PRT PRTPRTPRT
PRT PRTPRT PRTPRTPRTPRT
PRT PRTPRT
PRT
PRTPRT
PRT
PRT
PRTPRTPRT PRT PRT
PRTPRTPRT PRTPRTPRTPRTPRTPRT
PRTPRT PRTPRT
PRT PRTPRTPRTPRT PRTPRTPRTPRTPRT
PRTPRT PRTPRTPRTPRT
PRTPRT PRTPRT PRTPRTPRTPRT
PRT
PRT
PRTPRT
PRTPRT PRTPRTPRTPRTPRT
PRTPRT
PRT
PRTPRTPRTPRTPRTPRT
PRT
PRT
PRT
PRT PRTPRTPRTPRTPRTPRTPRTPRT
PRT
PRT
PRT
PRTPRT
PRT
PRT
PRTPRTPRTPRT
PRT
PRTPRT
PRT PRTPRT
PRTPRT
PRTPRT PRT PRTPRT
PRTPRTPRT
PRT
PRT
PRTPRTPRT PRTPRT PRT
PRTPRT PRTPRTPRTPRT
PRTPRT
PRTPRTPRTPRTPRT PRTPRTPRT
PRTPRT PRTPRTPRT
PRTPRTPRTPRTPRT
PRTPRTPRTPRT
PRT
PRTPRTPRT PRTPRTPRTPRTPRT
PRTPRTPRT PRTPRTPRTPRTPRT
PRTPRT
PRT PRTPRTPRTPRTPRTPRTPRTPRTPRTPRTPRTPRT PRT
PRT
PRTPRT
PRTPRT
PRTPRTPRTPRTPRT
PRT
PRT PRTPRTPRT
PRT
PRTPRTPRT PRTPRTPRTPRTPRTPRT
PRTPRTPRT PRT
PRTPRT PRT
PRTPRT
PRTPRTPRT PRTPRTPRTPRT
PRTPRTPRTPRT
PRT PRTPRT
PRTPRTPRT PRT
PRTPRTPRTPRT
PRTPRTPRTPRTPRTPRTPRTPRT
PRTPRTPRTPRT
PRT
PRT
PRT PRT
PRTPRT
PRTPRTPRT PRTPRTPRTPRT PRTPRT
PRT
PRTPRTPRT PRTPRTPRTPRT PRTPRT
PRTPRTPRT PRTPRTPRT
PRTPRTPRTPRTPRTPRT
PRTPRT
PRTPRT
PRT
PRT
PRTPRT
PRT
PRT
PRTPRT PRT
PRT
PRTPRT
PRTPRT
PRTPRT
PRTPRTPRT PRT
PRT
PRTPRTPRTPRT
PRTPRTPRT
PRTPRTPRTPRTPRTPRT
PRTPRTPRTPRT
PRT
PRT
PRT
PRT
PRTPRT
PSE
PSEPSE PSE
PSEPSEPSE
PSE
PSEPSEPSE
PSEPSE
PSEPSEPSE
PSE
PSEPSEPSE
PSE
PSEPSEPSE
PSE
PSEPSEPSE
PSE
PSEPSE
PSE
PSE
PSEPSE
PSEPSE
PSEPSE
PSEPSEPSE
PSE
PSE
PSEPSE
PSEPSEPSE
PSE
PSE
PSEPSE
PSE
PSEPSE
PSEPSE
PSEPSE
PSE
PSEPSEPSE
PSE
PSEPSEPSE
PSEPSEPSEPSE
PSEPSE
PSEPSEPSE
PSEPSEPSE
PSE
PSEPSE
PSEPSEPSE
PSEPSE
PSEPSEPSE
PSEPSE
PSEPSEPSE
PSE
PSEPSE
PSE
PSEPSE
PSEPSEPSE
PSEPSE
PSEPSEPSE
PSEPSE
PSEPSE
PSEPSE
PSEPSE
PSE
PSE
PSE
PSEPSEPSE
PSEPSE
PSE
PSE
PSEPSE
PSEPSEPSE
PSEPSE
PSEPSEPSE
PSE
PSEPSE
PSE
PSEPSE
PSEPSEPSE
PSE
PSEPSE
PSE
QATQAT QATQATQATQAT
QAT QATQATQATQAT
QAT QATQAT
QATQATQAT
QATQAT QAT
QAT
QAT
QATQATQATQAT
QAT
QATQAT QATQAT
QAT QATQATQATQATQAT
QAT QAT
QATQAT
QATQAT
QAT
QATQATQATQAT
QATQAT
QATQAT
QATQATQAT QAT
QAT
QAT
QATQAT QAT
QATQAT
QAT
QAT
QATQAT
QAT
QAT
QAT QAT
ROMROMROMROM
ROMROMROMROM
ROMROM
ROMROMROMROMROMROMROM
ROMROM
ROMROMROMROMROMROM
ROMROM
ROMROMROM ROM
ROMROM
ROMROM
ROMROM
ROM
ROMROM
ROM
ROMROMROMROM
ROMROMROM
ROMROM
ROMROMROMROMROM
ROMROM ROMROMROMROM
ROMROM
ROMROMROM
ROMROMROMROM
ROMROMROMROMROM
ROM
ROMROM
ROMROMROM
ROMROMROM
ROMROMROM ROMROM ROM
ROMROM
ROMROMROM
ROMROMROMROM
ROM
ROMROMROM
ROMROMROM
ROM ROMROMROM
ROM
ROMROM
ROMROM
ROMROMROM
ROMROMROM
ROMROMROM
ROMROM
ROM ROM
ROMROMROMROMROM
ROMROMROMROM
ROMROM
ROMROMROM
ROMROM
ROMROMROM
ROMROMROM ROM
ROMROM
ROMROM
ROMROM
ROMROMROM ROM
ROMROMROM
ROM
ROMROMROMROM
ROMROM
ROMROMROM
ROM
ROMROMROMROM
ROMROM
ROMROMROM
ROMROMROMROMROMROMROM
ROMROM
ROMROMROMROMROM
ROMROMROM
ROM
ROMROMROM
ROMROMROM
ROMROMROM
ROM
ROMROM
ROM
ROMROMROMROMROMROM
ROMROMROMROM
ROMROMROMROM ROM
ROMROMROMROMROM
ROMROMROMROM
ROMROM ROM
ROMROM ROM
ROMROM
ROM ROMROMROM
ROMROM
ROM
ROMROMROMROM
ROMROMROM ROM
ROMROM
ROMROMROM
ROMROMROMROMROM
ROM
ROMROMROM
ROM
ROMROM
ROMROM
ROMROM
ROMROM
ROMROMROM
ROMROMROM
ROMROMROM
ROM
ROMROM
ROM
ROM
ROMROM
ROM
ROMROMROM
ROMROMROM
ROM
ROM
ROM
ROM
ROM
ROM
ROM
ROMROM
ROMROM
ROM
ROM
ROMROMROMROM
ROMROMROM
ROMROM
ROM
ROMROMROM
ROMROM
RUSRUS
RUSRUSRUS
RUS
RUSRUS
RUSRUSRUS
RUSRUS
RUSRUS
RUS RUS
RUSRUSRUSRUS RUS
RUS RUSRUSRUSRUSRUS
RUS
RUSRUS RUS
RUSRUSRUSRUS
RUSRUSRUS
RUS
RUSRUS
RUS
RUSRUS
RUS
RUSRUS
RUSRUS
RUSRUSRUS RUS
RUS RUS
RUSRUSRUSRUSRUSRUSRUSRUS
RUSRUSRUS
RUSRUS
RUS
RUS
RUS
RUS
RUS
RUSRUS
RUSRUS
RUSRUS
RUSRUS
RUSRUS
RUSRUS
RUS RUSRUSRUSRUS
RUS
RUSRUS
RUSRUS RUSRUS
RUS
RUSRUS
RUSRUS
RUSRUSRUS
RUS
RUSRUSRUS
RUSRUS
RUS
RUS
RUS
RUSRUS
RUS
RUSRUS
RUSRUS
RUSRUS
RUS
RUS
RUSRUSRUS
RUSRUS
RUSRUSRUSRUSRUSRUS
RUSRUS
RUSRUS
RUS
RUSSAU
SAU
SAU
SAU SAU
SAU
SENSEN SEN
SENSENSEN
SENSEN
SENSEN
SENSENSENSEN SEN
SENSENSEN
SEN
SEN
SENSEN
SEN
SENSENSEN
SENSENSENSENSEN
SENSEN
SENSENSENSENSENSEN
SENSEN
SEN
SEN
SENSEN
SEN
SENSEN SEN
SENSENSENSENSEN
SENSENSENSENSEN
SENSEN
SEN
SEN SEN
SEN SEN
SENSEN
SENSENSEN
SEN
SENSEN
SENSENSENSEN
SEN
SENSEN
SEN SENSENSENSENSENSEN
SENSENSEN SENSEN
SENSENSENSEN
SENSEN
SENSENSENSEN SEN
SENSENSEN
SENSENSENSENSEN
SENSENSENSENSEN SEN SENSEN
SEN
SEN
SENSEN
SEN SENSEN
SEN
SENSENSENSEN SEN
SEN
SENSEN
SEN SENSENSEN
SENSENSENSEN
SENSEN
SENSEN SENSENSENSEN
SENSENSENSEN
SEN SENSENSEN SEN
SENSENSENSENSENSENSEN
SENSENSENSENSENSENSEN
SENSENSEN
SENSENSEN
SEN SENSEN
SEN
SEN
SEN
SENSEN
SENSEN SEN
SEN
SEN
SENSEN
SENSENSEN
SEN
SENSEN
SENSENSEN
SEN SENSENSENSEN
SENSENSENSEN
SEN SENSENSENSEN
SENSEN
SEN
SEN
SENSENSEN
SEN SEN
SENSEN
SENSEN
SENSENSEN
SEN SEN
SENSEN
SENSEN
SENSEN
SEN
SENSENSENSEN
SENSENSENSENSEN
SENSEN
SENSENSEN
SENSEN
SEN SEN
SEN SENSENSEN
SEN
SENSENSEN
SENSEN
SENSENSEN
SENSEN
SENSEN
SENSEN SEN
SENSEN
SENSEN
SEN
SEN
SEN
SENSEN
SEN
SEN SEN
SEN
SENSEN
SEN SENSEN
SEN
SENSENSENSENSEN SENSENSEN
SGPSGP SGPSGPSGPSGPSGPSGPSGPSGP
SGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGPSGP
SGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGP
SGPSGPSGPSGPSGP
SGPSGP
SGPSGP
SGPSGPSGPSGP
SGPSGP SGPSGP
SGP
SGP
SGP
SGP
SGPSGPSGPSGPSGPSGPSGPSGPSGP
SGPSGPSGPSGPSGP SGPSGPSGPSGP
SGPSGPSGPSGP
SGP
SGPSGPSGP
SGPSGPSGPSGP
SGPSGPSGP
SGP
SGPSGPSGPSGPSGPSGPSGPSGPSGPSGP
SGP
SGP
SGPSGPSGPSGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGP
SGPSGPSGP
SGPSGP
SGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGPSGPSGPSGPSGPSGPSGP
SGP
SGP
SGPSGPSGPSGPSGPSGPSGPSGP SGP
SGPSGPSGP SGP
SGP SGPSGP
SGPSGPSGPSGP SGP
SGPSGP
SGPSGP
SGPSGPSGP
SGPSGP SGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGPSGP
SGPSGPSGP
SGPSGPSGPSGP
SGPSGP
SGPSGPSGP
SGPSGPSGPSGP
SGPSGP
SGPSGPSGPSGP
SGPSGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGP
SGP SGPSGPSGPSGPSGP
SGPSGPSGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGPSGPSGP
SGP
SGPSGPSGPSGP
SGPSGPSGPSGPSGP
SGP
SGP
SGPSGP SGP
SGPSGPSGPSGPSGP
SGPSGPSGP
SGPSGPSGPSGPSGPSGPSGPSGP SGPSGP
SGPSGPSGP
SGP
SGPSGP
SLE
SLE
SLE
SLESLE
SLE
SLESLE
SLESLE
SLESLESLE
SLE
SLE
SLE
SLE
SVKSVKSVKSVK
SVKSVKSVK
SVKSVK
SVK
SVKSVK SVKSVK
SVK
SVKSVK
SVKSVK
SVKSVK SVK SVKSVKSVKSVK
SVKSVK
SVKSVKSVKSVK SVK
SVKSVKSVK
SVK
SVKSVK
SVKSVK
SVKSVKSVK
SVK
SVKSVKSVK
SVKSVKSVKSVKSVKSVK
SVK
SVKSVK
SVK
SVKSVK
SVKSVK
SVKSVKSVKSVK
SVKSVK SVK
SVKSVKSVKSVKSVKSVKSVKSVKSVK SVK
SVKSVK
SVKSVK
SVKSVKSVKSVK
SVKSVK
SVK SVKSVKSVKSVK
SVKSVK
SVKSVKSVKSVKSVKSVK
SVKSVK
SVKSVKSVK
SVK
SVKSVKSVK
SVKSVK SVKSVKSVKSVK SVK
SVKSVKSVKSVKSVKSVK
SVK
SVKSVKSVK
SVKSVKSVK
SVK
SVKSVK
SVKSVKSVKSVK
SVK
SVK
SVKSVKSVKSVKSVKSVK
SVKSVKSVK
SVK
SVKSVKSVK
SVKSVKSVKSVK
SVKSVK
SVKSVKSVKSVKSVKSVKSVK
SVKSVK
SVKSVKSVK SVK
SVKSVK
SVKSVK
SVK
SVKSVK SVKSVK
SVKSVK
SVK
SVK SVKSVKSVKSVKSVK
SVKSVKSVK
SVKSVKSVK
SVKSVK
SVKSVKSVK
SVKSVKSVKSVK
SVKSVK
SVKSVKSVKSVKSVK
SVK
SVKSVKSVKSVK
SVKSVKSVKSVKSVK
SVK
SVKSVKSVKSVK
SVKSVKSVKSVKSVK
SVK
SVKSVKSVKSVK
SVKSVK
SVKSVKSVK
SVKSVKSVKSVKSVK
SVK SVKSVKSVK
SVKSVKSVK
SVK SVKSVKSVK
SVK
SVKSVKSVK
SVKSVKSVKSVKSVKSVK
SVKSVKSVKSVK
SVKSVKSVK
SVK SVK
SVKSVKSVK
SVKSVK
SVKSVK
SVK
SVKSVKSVK
SVKSVKSVK
SVKSVK
SVKSVK SVK SVK
SVKSVKSVKSVKSVK
SVKSVKSVK SVK
SVK
SVKSVKSVKSVKSVKSVK
SVKSVKSVK
SVK SVKSVKSVK
SVKSVKSVKSVKSVKSVK
SVKSVK
SVKSVKSVK
SVK
SVK
SVKSVK
SVK
SVK SVKSVKSVK
SVKSVKSVK
SVKSVKSVK
SVKSVKSVKSVK
SVKSVKSVKSVK
SVK
SVK
SVNSVNSVNSVNSVN
SVNSVN
SVN SVNSVN
SVNSVN SVNSVN
SVNSVN
SVNSVN
SVNSVN
SVNSVNSVNSVNSVN
SVNSVN
SVNSVNSVN SVNSVNSVN
SVNSVNSVNSVNSVN
SVN SVNSVNSVN
SVN
SVNSVNSVNSVNSVN
SVN
SVNSVNSVNSVNSVNSVNSVNSVNSVN
SVNSVNSVNSVNSVNSVNSVNSVNSVN SVNSVN
SVNSVNSVNSVNSVNSVNSVNSVN
SVNSVNSVNSVNSVNSVNSVNSVN
SVN SVNSVNSVNSVN
SVNSVN
SVN
SVN
SVN
SVN
SVNSVN
SVNSVN
SVNSVNSVNSVNSVNSVNSVNSVN
SVNSVN SVNSVNSVNSVNSVN
SVNSVNSVNSVNSVNSVN SVNSVNSVN SVN
SVN
SVNSVNSVNSVN SVNSVNSVNSVNSVNSVN
SVNSVNSVNSVN SVNSVNSVN
SVN
SVNSVN SVNSVNSVNSVNSVN SVNSVNSVN
SVNSVNSVN
SVNSVNSVN
SVN
SVNSVNSVN
SVNSVNSVNSVNSVNSVNSVN
SVN
SVNSVNSVNSVNSVNSVNSVN
SVN
SVN SVN
SVNSVNSVNSVN SVNSVNSVNSVNSVNSVNSVN
SVN
SVNSVNSVN SVNSVNSVNSVN SVN
SVNSVNSVN
SVNSVNSVNSVNSVNSVNSVN
SVN
SVNSVNSVNSVNSVN
SVNSVNSVN SVN
SVNSVNSVNSVN
SVN
SVNSVN
SVNSVNSVNSVN
SVNSVN
SVNSVNSVNSVNSVN
SVNSVN
SVNSVNSVN
SVNSVNSVNSVNSVNSVNSVN SVN SVN
SVN
SVNSVNSVNSVNSVNSVN SVN
SVN
SVNSVN
SVNSVNSVNSVN
SVN
SVNSVNSVNSVN
SVN
SVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVNSVNSVNSVN SVN
SVNSVNSVNSVNSVNSVNSVN
SVN
SVNSVN SVNSVN SVNSVNSVNSVN
SVN SVNSVN SVNSVNSVN
SVNSVN
SVN SVNSVN SVNSVNSVNSVN
SVN
SVNSVNSVNSVN SVNSVNSVNSVNSVNSVN SVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVNSVN SVN SVN
SVN SVNSVNSVNSVNSVN
SVNSVNSVN
SVNSVNSVN SVN
SVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVN SVN
SVN
SVNSVNSVNSVNSVNSVNSVNSVN
SVNSVNSVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVNSVNSVN
SVNSVN
SVNSVNSVNSVNSVN
SVNSVNSVN
SVNSVN
SVNSVN
SVNSVNSVN
SVNSVN
SVN
SVNSVNSVN
SVNSVNSVN
SVN
SVN
SVN
SVN
SVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVNSVN
SVNSVNSVN
SWESWESWESWESWESWE
SWESWESWE
SWE
SWESWESWE
SWESWESWE
SWESWESWE
SWESWESWE SWESWESWE
SWE
SWESWE
SWE
SWE
SWE SWE SWESWESWESWESWESWE SWE
SWESWESWE SWESWESWESWESWESWESWE
SWESWESWESWESWESWESWESWE
SWE
SWESWESWESWESWE SWESWESWESWESWESWESWESWE SWESWESWESWESWESWE
SWESWE
SWESWESWESWESWESWESWE
SWESWE SWESWESWESWE
SWESWESWESWESWESWESWE
SWESWESWESWESWESWESWESWESWE
SWESWE
SWE
SWESWESWESWESWESWESWESWE SWESWE
SWE SWESWESWE
SWESWESWE
SWESWE
SWESWESWE SWESWESWESWESWESWESWE
SWE
SWESWESWE
SWESWESWESWESWESWESWESWE SWESWE
SWESWE SWESWESWESWESWESWESWE
SWESWESWESWE SWESWESWESWESWESWE
SWESWE SWE SWESWESWESWESWESWE
SWESWESWE
SWE
SWESWESWE
SWESWE
SWE
SWESWESWESWESWESWESWE
SWE SWE
SWESWESWE SWESWESWE
SWESWESWE
SWESWESWESWE SWE SWE
SWESWE
SWE
SWE
SWESWESWE SWESWESWESWESWESWESWE
SWESWESWE
SWE SWESWESWESWESWE
SWE
SWESWESWESWESWESWESWESWESWE
SWE
SWESWE SWESWESWESWESWE
SWESWESWESWESWESWESWESWESWE SWESWE
SWESWESWESWESWESWESWE
SWESWESWESWESWESWESWESWESWE
SWESWE
SWESWE SWESWESWE
SWE SWE
SWESWE
SWESWESWE
SWESWESWESWESWESWE
SWE
SWESWESWESWE SWE SWE
SWESWESWESWESWESWESWESWESWESWESWESWE
SWESWESWESWESWESWESWESWESWE
SWESWESWE SWESWE SWESWESWESWE
SWESWESWE
SWESWESWESWESWESWE
SWESWESWE
SWESWESWESWESWESWESWESWESWE
SWESWE
SWE
SWESWE
SWESWESWESWE
SWE
SWESWESWESWE
SWESWESWESWE
SWESWESWE SWE SWE
SWESWE SWESWE
SWE
SWESWESWESWE
SWESWESWE
SWESWE SWESWESWESWESWE
SWESWE
SWESWESWE
SWESWESWESWESWESWESWE
TJK
TJKTJKTJK TJKTJK
TJK
TJKTJK
TJK
TJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJKTJK
TJK
TJKTJK
TJKTJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJK
TJK
TJK
TJKTJK
TJKTJK
TJK
TJKTJKTJK
TJK
TJK
TJK
TJK
TJKTJK
TJK
TJKTJK
TJK
TJKTJKTJKTJKTJKTJK
TJK TJKTJK
TJKTJK
TJKTJK
TJK
TJK TJKTJKTJKTJK
TJKTJK
TJK
TJK
TJK
TJKTJK
TJK
TJKTJK
TJKTJKTJKTJK
TJK
TJKTJKTJK
TJK
TJK
TJK
TJK
TJK TJK
TJKTJKTJK
TJK
TJKTJK TJKTJKTJKTJK
TJK
TJK
TJK
TJK
TJK
TJK
TJKTJK
TJKTJK
TJK
TJKTJK
TJKTJKTJK
TJK
TJKTJKTJK
TJKTJK
TJK
TJKTJK
TJK
TJK
TJKTJKTJKTJK
TJKTJK
TJK
TJK
TJKTJK
TJKTJKTJK
TJKTJKTJK
TJK TJKTJKTJK
TJKTJK
TJK
TJK
TJKTJKTJK
TJKTJKTJK
TJKTJK
TJKTJK
TJK
TJKTJK
TJK
TJKTJK
TJKTJK
TJK
TJK
TJKTJK
TJKTJKTJK
TJK
TJK
TJK
TJKTJK
TJKTJK
TJKTJK
TJKTJK
TJK
TJKTJK
TJKTJK
TJKTJKTJK
TJK
TJK
TJK
TJK
TKM
TKMTKM
TKM
TKM TONTONTONTON
TON TONTONTONTON
TONTONTON
TONTON
TONTON
TONTON
TON
TONTON
TONTON
TONTON
TONTONTON
TON
TONTON
TONTONTONTON
TONTON
TON
TON
TONTONTONTON
TON
TON
TONTONTON
TONTONTONTON
TONTON
TONTONTON
TON
TON
TON
TON
TON
TONTONTONTONTON
TONTON
TONTON
TON
TON TONTONTON
TONTONTONTON TON
TON
TONTON
TON
TONTONTON
TON
TON
TON
TON
TON
TON
TONTON
TONTONTONTONTON
TONTON
TONTONTONTON
TON
TON
TONTON
TON
TON
TONTONTONTON
TONTON
TONTON
TONTON
TON
TONTONTON
TON
TON
TON
TON
TON
TONTON
TON
TON
TON
TON
TTOTTOTTOTTO TTOTTOTTO
TTOTTOTTOTTOTTOTTO
TTOTTOTTOTTO
TTOTTOTTO TTOTTOTTOTTOTTO
TTOTTO
TTOTTOTTOTTO TTO
TTO
TTO
TTOTTO
TTOTTOTTO
TTO
TTOTTO
TTOTTO TTOTTOTTOTTO TTO
TTOTTOTTOTTO TTOTTO TTOTTOTTOTTO
TTO
TTOTTOTTOTTOTTO
TTOTTOTTOTTOTTO
TTO TTO
TTO
TTOTTOTTO
TTOTTOTTO
TTO
TTOTTO
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TTO
TTO
TTO
TTOTTOTTO
TTO TTO
TTOTTO TTOTTOTTOTTOTTO
TTOTTOTTOTTOTTOTTOTTO
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TTOTTOTTOTTO TTOTTOTTOTTOTTO
TTOTTO
TTOTTOTTO
TTOTTO TTO
TTOTTO
TTOTTOTTOTTOTTOTTOTTO
TTOTTO TTOTTOTTO
TTOTTO
TTOTTOTTO
TTO
TTOTTOTTO TTOTTO TTO
TTO TTO
TTOTTO
TTO
TTOTTO
TTOTTO TTO
TTOTTOTTOTTO
TTOTTOTTOTTOTTOTTO
TTOTTOTTOTTO
TTOTTOTTO
TUNTUN
TUN
TUN
TUN
TUN
TUN TURTUR
TUR
TURTURTURTUR
TUR
TUR
TUR
TUR
TUR TUR
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However, risk-averse firms react differently to uncertainty according to their charac-
teristics. Indeed, demand fluctuation may force some producers to not enter the export
market or to cease exporting and, in turn, the demand for the incumbent exporting firms
may rise. Additionally, change in uncertainty may modify the relative prices among vari-
eties supplied leading to a reallocation of market shares among the incumbent exporters.
Hence, the effects of industry-specific uncertain demand on export performance at the
firm level are not a priori clear.
Further, managers can be sensitive to downside losses, relatively to upside gains. It
has been shown that macroeconomic fluctuations are skewed rather than symmetric (see
e.g., Popov 2011). Ceteris paribus, managers might prefer to serve a country exhibiting
a high probability of an extreme event associated with a high level of demand than a
country with a high probability of an extreme event associated with a very low demand.
Yet, the variance does not distinguish between upside risk versus downside risk. Skewness1This evidence is supported by an adjusted partial residual plot, controlling for year and country
fixed effects. The corresponding elasticity of demand volatility is −0.17 (p<0.01) suggesting that an 1%increase in volatility in a destination country is associated with a 0.17% decrease in sectoral imports.
3
(the third central moment of a distribution) can be used as a measure of downside risk.
Indeed, the sign of the skewness provides information on the asymmetry of the demand
distribution, and thus on downside risk exposure. For a same mean and variance, countries
with a demand distribution which is more skewed to the right can be viewed as providing
better downside protection or smaller downside risk. A basic intuition is that an increase in
skewness involves a smaller probability for low (or large negative) returns. A distribution
exhibiting more downside risk than another is less skewed to the right.2 For a same mean
and variance, a decision-maker therefore prefer a distribution with the highest skewness.
To illustrate our point, consider the demand pattern of two hypothetical countries A
and B. Each country exhibits a demand distribution with the same mean (6000 ke) and
same variance ((6000)2/3 ke). Assume that, in country A, the level of demand is either
4000 ke with a probability 3/4 or 12000 ke with a probability 1/4 while, in country B,
the level of demand is either 0 ke with a probability 1/4 or 8000 ke with a probability
3/4.3 Despite an equal demand variance in each country, the manager may prefer to
serve the country with a higher skewness (country A) because of a lower exposure to
downside risk. In this case, the decision-maker is averse to downside risk. As a result,
a manager prefers to serve a country with a large mean demand, a small variance and a
large (unweighted) skewness.
In this paper, we study theoretically and empirically how firms adjust their intensive
and extensive margins to a change in the variance and skewness of foreign demand. To
achieve our goal, we first build a trade model where heterogeneous firms producing under
imperfect competition face same industry-wide uncertainty over foreign demand. We
adopt a mean-variance-skewness analysis so that firms are assumed to be averse both to
risk and to downside losses. A comment is in order. By adopting this decomposition
analysis, we consider a “small” risk. Indeed, export sales in a foreign country represent a
low share in total sales of firms.
Our theory leads to the following predictions. A higher variance of foreign demand in a
market raises the unit prices set by firms in this market through a change in their markup.2However, the converse is not necessarily true (Menezes, Geiss, and Tressler, 1980).3A similar example can be found in Eeckhoudt, Gollier, and Schlesinger (2005) in a different context.
4
The magnitude of this effect increases with the productivity of firms but decreases with
trade costs. We also show that, even though a higher foreign demand volatility reduces
the volume of industry export sales, its effect on the probability of exporting (extensive
margin) and on the level of export sales at the firm level (intensive margin) is ambiguous.
Indeed, a rise in demand volatility induces a reallocation of market share from most
productive (and largest) firms to smaller firms. As a result, the probability of exporting
may increase when fixed trade costs are not too high. Under this configuration, the export
sales of medium-sized firms (or the smallest exporters) may grow. However, this effect is
weakened when the skewness of foreign income increases.
To test our predictions, we use firm-level data from the French customs over the period
2000-2009. This database reports the volume (in tons) and value (in euros) of exports
for each product (combined nomenclature) and destination, for each firm located on the
French metropolitan territory. Our different estimations confirm our main predictions.
Hence, decision-makers seem to act as if they were risk-averse and averse to downside
losses. Our results are robust over different sized panels and to the inclusion of a plethora
of fixed effects, and additional controls.
Our paper proceeds as follows. We present the related literature in Section 2. We
then develop our multi-country model of trade with heteregeneous firms under imperfect
competition in Section 3. Section 4 exposes the empirical strategy and the results. We
conclude in Section 5.
2 Related literature
Although macroeconomic volatility plays a key role in a wide variety of economic out-
comes (in long-run growth, investment and production decisions, welfare), rather little
consideration has been given to its impact on export decision.
The determinants of macroeconomic volatility has received much attention. In partic-
ular, it has been argued that freer trade increases volatility at the country level. Industries
more open to international trade are more volatile even though the industries exporting in
foreign countries or importing from different countries depend less on the domestic shocks
5
(Giovanni and Levchenko 2009). On the one side, the industries exporting (or import-
ing) a large fraction of their production (or their intermediate products) are vulnerable
to world demand shocks (or supply shocks). On the other side, trade openness leads
to a less diversified production structure, implying a greater specialization and, in turn,
an increase in volatility. In addition, trade openness can exacerbate domestic volatility
through a decline in the number of firms (Gabaix, 2011). Indeed, since lowering trade
barriers induces an exit of small firms and boosts the size of large firms (Melitz, 2003),
the economy becomes more and more dependent on the shocks at the firm level.
Besides, the macroeconomic volatility is a source of uncertainty which potentially
influences the export decision. Indeed, trade decision are often made before the resolution
of uncertainty. When an export commitment is made, the price received is unknown. The
expected utility theory has been used to analyze international trade under risk and perfect
competition (see Helpman and Razin 1978). More recently, the framework supplied by
real option theory (Dixit and Pindyck 1994) has been introduced in trade model under
imperfect competition. Given export sunk costs, the decision whether and when to export
is very similar to an investment decision. For example, Baldwin and Krugman (1989)
explain trade hysteresis despite large exchange rate fluctuations. In addition, Handley
and Limao (2013) model trade policy uncertainty as a rare event to explain world trade
patterns. A standard assumption in new trade theory is that firms are risk-neutral.
A recent literature emphasizes that uncertainty shocks may affect the choice to serve
a foreign market through exports or affiliate sales. Lewis (2014) considers nominal un-
certainty. He finds that when multinational sales are priced in the local currency while
exports are priced in the producer currency, destination volatility benefits exporters: dur-
ing a foreign nominal contraction, the foreign exchange rate appreciates, causing exports
to be relatively cheaper. Exporters gain non-linearly through demand, making profit
convex in prices. Ramondo, Rappoport, and Ruhl (2013) consider real uncertainty and
changes in relative costs between exporters and multinationals. Exporters hire labor
at home compared to affiliates. Exporters’ expected profits are thus increasing in the
volatility of production costs in the destination country.
6
Our approach contributes to the trade literature in a number ways. First, we consider
that when a firm chooses a certain quantity to serve a foreign market, the price that will
obtain in the market is uncertain. The real value taken by foreign demand for each product
is not known ahead of time. Second, we consider that firms are averse to risk and more
particularly to downside risk. Third, we develop a model where firms are heterogeneous in
productivity supplying a differentiated product (horizontal and vertical differentiation).
Our approach differs from the current trade literature since we consider that managers
know the productivity of their firm but do not have perfect information about the level
of foreign demand.
3 Theory
In this section, we develop a multi-country model of trade with heterogeneous firms un-
der imperfect competition. Firms differ in productivity, but unlike Melitz (2003), they
know with certainty their production technology. Firms face instead an industry-wide
uncertainty over foreign demand. Industry-specific demand is uncertain because indus-
try level expenditures in destination country j, denoted by Rj, are subject to random
shocks. Factors beyond the firm’s control that influence the demand realization are cli-
matic conditions, changes in consumer tastes, opinion leaders’ attitudes, popularity of
competing products, etc. We assume that Rj is independently distributed with a mean, a
variance, and a skewness given by E(Rj), V(Rj), and S(Rj) respectively. Note that E(Rj)
and V(Rj) are always positive while S(Rj) can be positive or negative. The economic
interpretation of these three moments of Rj distribution is discussed below.
Hence, each firm located in country i and producing a variety v faces a downward
sloping demand curve in destination country j given by pij(v) = f [qij(v), Rj, .], where
pij(v) and qij(v) are the price and the quantity of variety v respectively. We assume that
the demand is not known for certain at the time the contracts with the importers are
signed. As above-mentioned Rj is subject to random shocks that cannot be observed
at the time strategic variables (pij or qij) are chosen. The actual demand realization
is therefore uncertain, and can be either higher or lower than the average expenditures,
7
E(Rj). As a result, the dependence of price on quantity (and vice-versa) is given for every
state of nature. In other words, the marginal revenue of each firm is volatile and not
known with certainty at the time the contracts are signed.
Our objective is to analyze how risk-averse firms react to industry-level uncertainty
in export decisions (both at the intensive and extensive margins), according to their
characteristics and the features of destination country. On the one hand, the level of
output may decrease for all firms due to demand fluctuations in accordance with the
standard theory of production under uncertainty. On the other hand, some producers
may not enter the export market or cease to export because of these fluctuations so that
the demand for the remaining exporters may rise (due to a reallocation of demand). In
addition, even if the demand shocks are common for all firms within an industry, it may
modify the relative prices among varieties supplied by the surviving firms in this industry,
leading to a reallocation of market shares. Hence, the effects of industry-specific uncertain
demand on export performance at the firm level are not a priori clear.
Market structure, technology, and firm behavior
Varieties are provided by heterogeneous monopolistically competitive firms. Each vari-
ety is produced by a single firm and each firm supplies a single variety. This means that
producers are negligible to the market, behave as monopolists on their market and their
decisions do not account for the impact of their choice on aggregate statistics.
Labor is the only production factor and is assumed to be supplied inelastically. The
production of qij(v) units of variety v requires a quantity of labor equals to `ij(v) =
τijqij(v)/ϕ, where ϕ is the labor productivity and τij > 1 is an iceberg trade cost. We
assume that the labor productivity is known a priori but differs among firms. Thus, the
marginal requirement in labor is specific to each firm and to each destination, but does
not vary with production.
Under imperfect competition, the choice of the action variables (quantity or price)
merits a discussion. We know from Leland (1972) and Klemperer and Meyer (1986) that,
if the choice of behavioral mode by a monopolistic firm is unimportant under certainty,
this is no longer the case under uncertainty. The firm has two options: (i) set quantity
8
before demand is known and thereafter the actual demand curve yields the market clearing
price or (ii) set price before demand is known and thereafter the actual demand curve
yields the market clearing quantity. Ideally, we would determine endogenously if firms
choose either a quantity to produce or a price to charge, as in Klemperer and Meyer
(1986). In this section, we consider that firms set quantity first, before demand is known.
In Appendix (A), we report the case where price is the strategic variable. We show that
this configuration yields the same predictions than the case where firms set quantity even
if the level of prices and quantities differ according to the behavioral mode.
Hence, without loss of generality, we assume that firms determine quantity, qij, for
each destination j before knowing the value of Rj. Equilibrium prices pij are determined
ex post in accord with realized demand. We assume that firms cannot adjust ex post
quantity with respect to the demand shock. The decision to produce for exports has been
taken ex ante and thus ex post adjustments of quantity are not feasible. The producer
cannot refuse the deal ex post once the price is realized. This implies that products cannot
be sent back. They are sold once exported.
As the shocks to market demand are unobservable, the impact of quantity on price is
uncertain. The expected export profit in a given market is
E [πij(v)] = E [pij(v)] qij(v)− wiτijϕ
qij(v)− wifij, (1)
where wi is the wage rate prevailing in the exporting country i. Firms located in country
i serving the destination country j have to pay a fixed cost fij to serve foreign markets,
which are the costs to maintain a presence, (i.e., maintaining a distribution and service
network, minimum freight and insurance charges, costs of monitoring foreign customs
procedures and product standards, etc.).
We assume that markets are segmented and that shocks are not correlated across coun-
tries, such that the covariance between πij and πik, with k 6= j, is zero as Cov(Rk, Rj) = 0.
The uncertain terminal profit of a firm producing variety v and located in country i, πi,
can be decomposed into two parts: the profit of domestic sales πii and the profit of to-
tal exporting sales Σjπij, such that πi = πii + Σjπij. Throughout, we assume that the
9
domestic profit πii is known with certainty.
Uncertainty and firm behavior
We consider an utility function of a manager Π(πi) representing his/her risk preferences
with Π′(πi) > 0. The manager is made better off by an increase in his/her terminal profit.
Assume that the utility function Π(πi) is continuously differentiable up to order 3. We
follow the methodology developed in Eeckhoudt, Gollier, and Schlesinger (2005). A third
order Taylor series expansion of Π(πi) evaluated at E(πi) gives
Π(πi) ≈ Π(E(πi)) + Π′ [πi − E(πi)] + 12Π′′ [πi − E(πi)]2 + 1
6Π′′′ [πi − E(πi)]3 .
Taking the expectation and assuming that the moments exist lead to
EΠ(πi) ≈ Π[E(πi)] + 12Π′′E[πi − E(πi)]2 + 1
6Π′′′E[πi − E(πi)]3.
According to the expected utility theory, in the neighborhood of E(x) where x is
a random variable, we have EΠ(x) = Π(E(x) − Γ) where Γ is the risk premium. In
our case, the risk premium Γ is defined as the sure amount of money a manager would
be willing to receive to become indifferent between receiving the risky return πi versus
receiving the sure amount E(πi)−Γ. Because EΠ(πi) can also be approximated as follows
EΠ(πi) = Π[E(πi)]− ΓΠ′, we have
Γ ≈ −12
Π′′
Π′ V(πi)−16
Π′′′
Π′ S(πi), (2)
where −Π′′/2Π′ and −Π′′′/6Π′ are the marginal contributions of variance (V(πi) = E[πi−
E(πi)]2) and skewness (S(πi) = E[πi − E(πi)]3) of πi to the risk premium Γ, respectively.
The term −Π′′/2Π′ is known as the so-called Arrow–Pratt absolute risk aversion coef-
ficient. The term −Π′′′/6Π′ captures a preference for positive skewness when Π′′′ > 0,
because it implies a low probability of obtaining a large negative return. Note that skew-
ness to the left (S(π) < 0) is associated with “downside risk” exposure, while skewness to
the right (S(π) > 0) is associated with “upside risk” exposure.
10
Two remarks are in order. First, risk aversion can decrease with wealth of the individ-
uals. Under this configuration, decision makers exhibit decreasing absolute risk aversion
(DARA preferences) implying Π′′′ > 0. This entails that the risk aversion of the exporter
decreases with its level of domestic sales. Second, Π′′′ > 0 corresponds to “downside risk
aversion”, implying that a rise in downside risk (a decrease in S(π)) would tend to increase
the willingness to pay to avoid risk (Menezes, Geiss, and Tressler, 1980).
Finally, we know from expected utility theory that maximizing EΠ(x) is equivalent
to maximizing the certainty equivalent E(x) − Γ. Since expression (2) provides a local
approximation to the risk premium Γ, it follows that the objective function of a decision-
maker can always be approximated by
Πv(πi) ≈ E(πi)− ρvV(πi) + ηvS(πi), (3)
with ρv ≡ −Π′′/2Π′ and ηv ≡ Π′′′/6Π′ . Remember that ρv > 0 expresses the absolute
degree of the firm’s risk aversion. If ρv = 0, firms are risk neutral. This general formulation
does not require a full specification of the utility function Πv(π) and it allows us going
beyond a simple mean-variance analysis in the investigation of export decision under
demand uncertainty. This may be particularly useful in the analysis of “downside risk”
exposure. However, the reader should keep in mind that expression Πv(π) is valid only in
the neighborhood of the point E(πi). In other words, we only consider small risk. Given
that the share of export sales in one country to total sales is low, this assumption is not
too restrictive.
Preferences and demand
To study the effect of uncertainty on export decision, we use a simple and tractable
utility function yielding a specific demand curve.4 The consumer preferences are identical4We could use the general setting in which the demand curve is pij = f(qij , Rj). However, this
increases the complexity of the formal developments while the gains in results are limited. What mattersfor most of our results is that export prices (1) decrease in export quantities (∂pij/∂qij < 0), (2) increasein destination demand (∂pij/∂Rj > 0), (3) with a diminishing marginal effect as export quantities increase∂2pij/(∂Rij∂qij) < 0.
11
and the utility of the consumption of the differentiated good is given by
Uj =∫v∈Ωj
uj(v)dv, (4)
where Ωj is the set of available varieties v in country j. Hence, as in Dixit and Stiglitz
(1977), Krugman (1979) and Zhelobodko et al. (2012), we assume that preferences over
the differentiated product are additively separable across varieties. To keep things simple,
we consider uj(.) = θvqj(v)1/2 where θv is the quality of variety v and q(v) is the quantity
consumed. Given that ∂u(v)/∂q(v) > 0 and ∂2u(v)/∂q(v)2 < 0, consumers exhibit a
preference for variety.
The budget constraint faced by a consumer is given by
∫Ωj
pkj(v)qkj(v)dv = Rj, (5)
where Rj represents the income of the consumer and its total expenses, while pkj(v) is
the price of variety v produced in country k with k = i, j, ..K.5 Notice that deviations
from expected demand in individual foreign markets, due to random shocks, may lead to
potential gains and losses. By neglecting their effect on total income, which may cancel
out, we adopt a partial equilibrium approach.
Maximizing (4) under the budget constraint (5) leads to the inverse demand:
pij(v) = θv2λqij(v)
−12 , (6)
where λ is the Lagrange multiplier. Plugging (6) into (5) implies λ = Ψj/2Rj with
Ψj ≡∫
Ωj
θvqkj(v) 12 dv. (7)
The expression Ψj can be interpreted as a measure of industry supply. As a consequence,5Note that we could extend easily this framework by considering the case where U is embodied in a
Cobb-Douglas upper-tier utility.
12
the inverse demand for each variety is now
pij(v) = Rjθvqij(v)−12 Ψ−1
j . (8)
As expected, the price of a variety increases with its quality (θv) and the market size (Rj)
but decreases with its quantity (qij) and rivals’ quantity and quality of products (Ψj).
Intensive margin of exports with no uncertainty
We start by analyzing briefly the export decision with no uncertainty before introduc-
tion uncertainty. Each firm maximizes its profit (1) by setting its output qij(v) to serve
market j and by taking into account its impact on prices pij(v) given in (8). Profit-
maximizing quantity is given by
qij(v) 12 = Rj
2ϕθvwiτij
Ψ−1j , (9)
so that
pij(v) = 2wiτijϕ
.
Thus, under certainty, the equilibrium price is equal to the marginal cost wiτij/ϕ times
a constant (equals to 2), while exports (in value and quantity) increase with productivity,
quality and demand.
In what follows, we analyze how uncertain demand curve affect equilibrium prices and
quantities at the firm level.
Intensive margin of exports with uncertainty
Under uncertainty, remember that exporting firms face a downward sloping demand
curve characterized by a random shift parameter Rj (common to all firms in a given
industry). Rj is not known for certain at the time the contracts with the importers are
signed. The expected price prevailing for each firm in the foreign market is therefore given
by
E [pij(v)] = E(Rj)θvqij(v)−12 Ψ−1
j . (10)
13
For the sake of clarity, we first consider a mean-variance utility function such that
ηv = 0 (i.e., downside and upside risks are not accounted for). Then, we will consider the
role of skewness with ηv 6= 0. The payoff of each firm when ηv = 0 is as follows:
Πv(πi) = E(πi)− ρvV(πi) =∑
jE(πij)− ρv
∑jV(πij),
where E(πij) is given by (1) and ΣjV(πij) = V(πi) because Cov(Rk, Rj) = 0 for all
k 6= j. Hence, the marginal revenue is uncertain while the marginal cost is known with
certainty. The expected export sales E [pij(v)] qij(v) increases with qij but decreases with
the industry’s output size (captured through Ψj). The profit variance is
V(πij) = V(Rj)θ2vqij(v)Ψ−2
j , (11)
which increases with the firm’s output size qij and decreases with the industry’s output
size. Hence, the mass of rivals serving the same market has an ambiguous effect on the
export performance at the firm level. Indeed, a rise in Ψj decreases the marginal revenue
of the firm but reduces the variance of profits.
First order condition ∂Πv/∂qij = 0 implies
12E(Rj)θvqij(v)
−12 Ψ−1
j −wiτijϕ− ρvV(Rj)θ2
vΨ−2j = 0, (12)
and it is readily to check that ∂2Πv/∂q2ij < 0. As a result, Payoff -maximizing quantity is
given by
qij(v) 12 = E(Rj)θvϕ
2wiτijΨ−1j
[1 + ρvV(Rj)Ψ−2
j
θ2vϕ
wiτij
]−1
. (13)
In accordance with the standard literature related to producer theory under uncertainty,
the risk averse firms produce less than they would under certainty (because ρv > 0 so
that ∂qij/∂V(Rj) < 0), for a given mass of exporters. Further, we can readily check that
quantities are concave in productivity (∂qij/∂ϕ > 0 and ∂2qij/∂ϕ2 < 0). Thus, the most
productive firms are the largest in terms of labor and quantity produced. Another result
standard in the trade theory literature is that quantities are convex in trade costs, such
14
that ∂qij/∂τij < 0 and ∂2qij/∂τ2ij > 0). Hence, export sales decrease with trade costs,
while they increase with productivity. New and more original is the following proposition:
Proposition 3.1 For a given positive degree of the firm’s risk aversion (ρv > 0) and in-
dustry supply (Ψ), the negative effect of demand volatility on export quantities is strength-
ened when firm productivity increases and trade costs decrease.
As a simple proof, we can establish from equation (13)
∂2qij∂ϕ∂V(Rj)
< 0 < ∂2qij∂τ∂V(Rj)
,
when ρv > 0. Remember that the variance of profits in a given foreign market is equal
to the variance of foreign demand times the output size dedicated to that foreign country
(see equation 11). Stated differently, the variance of profits of a firm increases with its
productivity and decreases with trade costs for a given mass of firms.
Using (8) and (13), we can now determine the ex post equilibrium price of variety v
prevailing in country j:
pij(v) = wiτijϕ
2 Rj
E(Rj)
[1 + ρvV(Rj)
θ2vϕ
wiτijΨ−2j
](14)
The equilibrium price is equal to the marginal cost (wiτij/ϕ) times a markup. Remember
that, under demand certainty, the markup is equal to 2 (because of E[Rj/E(Rj)] = 1
and V(Rj) = 0). With uncertain demand and risk-averse firms, the markup is higher
on average than the markup prevailing under certain demand due to the fluctuations of
income. Hence, uncertain demand curve tends to increase prices through a higher markup.
It is worth stressing that, unlike models of monopolistic competition with perfect in-
formation, the markup is not a constant. Firms charge variable markups even under
CES preferences. In other words, demand uncertainty and risk-averse firms allow for vari-
able markups even though demand curve is iso-elastic. Markup depends on the demand
volatility, firm’s productivity and features of origin and destination countries. Note also
that the markup increases with the firm’s productivity (ϕ) and decreases with trade costs
15
(τij) and the mass of rivals (captured by Ψj). Those findings are in accordance with in-
dustrial organization theory. However, the mechanisms at work are different. Our results
are related to the existence of demand fluctuations and risk aversion. The variance of
profits being high for the most productive firms, they charge greater markups. Similarly,
low market size induces low variance of profits so that the markup is lower for destina-
tions with a low potential market. Hence, even though preferences exhibit an iso-elastic
demand, markups vary according to destinations and firms.
In addition, under demand uncertainty, the markup captures two opposite effects
associated with the destination income (Rj). On the one side, higher income raises the
markup and, in turn, prices (demand effect). On the other side, higher expected foreign
demand increases the competition across firms (which have an incentive to reduce their
level of production) and, in turn, decreases the markup. However, on average, both effects
cancel each other out (E[Rj/E(Rj)] = 1). The next proposition sums up our results on
destination prices:
Proposition 3.2 For a given positive degree of the firm’s risk aversion (ρv > 0) and
industry supply (Ψ), the markup increases more with foreign demand uncertainty, the
higher the firm productivity and the lower the trade costs.
Propositions (3.1) and (3.2) are related to the intensive margin of trade, i.e., variation
in trade of existing trade relationships. They are established without accounting for the
adjustment in the industry supply (Ψ). However, uncertainty leads to an adjustment in
the quantities produced by the competitors. This adjustment reinforces proposition (3.2)
on export prices but renders proposition (3.1) on exported quantities more ambiguous.
Before presenting the effect of the industry adjustment, we study the role of uncertainty
on the extensive margin of trade, i.e., on the emergence of new trade relationships and/or
the death of existing ones.
Extensive margin of exports with uncertainty
The mass of domestic firms in each country is exogenously given,6 while the mass of6An endogenous mass of domestic firms can be incorporated in the analysis without qualitatively
changing any of the main results.
16
exporting firms is treated as endogenous. There is a large supply of potential entrants in
the international market. However, firms located in country i serving destination country
j have to pay a fixed cost fij to serve foreign markets (see equation 1). The decision to
exit or enter a foreign market is taken on the basis of the expected payoff. A firm exports
to destination j if and only if ex ante payoff Πv is positive, i.e., E(πij)− ρvV(πij) > wifij.
It is straightforward to check that Πv = sij/2 where sij ≡ E(pij)qij with
sij = E(Rj)2
2
[wiτijθ2vϕ
Ψ2j + ρvV(Rj)
]−1
. (15)
As a result, Πv = 0 when ϕ = 0 and ∂Πv/∂ϕ > 0. Hence, there exists a quality-adjusted
productivity cutoff θ2vϕ above which a firm serves country j. As expected, the probability
of exporting decreases with V(Rj). In addition, we have ∂2Πv/∂ϕ∂V(Rj) < 0 so the
payoffs of large firms are more impacted by a rise in demand volatility than the small
firms. Notice also that ∂Πv/∂Ψj < 0 and sign∂2Πv/∂ϕ∂Ψj = signρvV(Rj) θ2
vϕwiτij−Ψ2
j
so that a higher total supply decreases the ex ante payoff of each incumbent firm. However,
the high productivity firms are less affected by a rise in Ψj.
Until now, we have studied the impact of V(Rj) on exports and prices for a given Ψj.
However, Ψj adjusts when V(Rj) changes. Therefore, we have to take into account the
indirect effect of demand volatility on prices and quantity through Ψj. In what follows,
we determine the relationship between Ψj and V(Rj) and the total effect of V(Rj). Let
ξ ≡ 1/(θ2vϕ) ≥ 0 be an inverse measure of quality-adjusted productivity and µ(ξ) is the
distribution of ξ. The cutoff for exporting ξij is such that Πv(ξij) = wifij or, equivalently,
ξij ≡[E(Rj)2
4wifij− ρvV(Rj)
]Ψ−2j
wiτij. (16)
It follows that a firm exports as long as ξ < ξij. As expected, high productivity firms are
more likely to be exporters while high fixed and variable trade costs reduce the probability
of exporting. However, unlike trade models with heterogeneous firms, the exporting zero-
payoff cutoff conditions ξij can be non positive. No firm finds a priori profitable to serve
country j if the expected income E(Rj) is not sufficiently high relatively to its variance
17
V(Rj). Hence, we provide a rationale for the prevalence of zeros in bilateral trade without
making an ad hoc assumption on the distribution of productivity across firms. Helpman
et al. (2008) allow also for zero bilateral trade volumes as the authors assume that the
most productive firms exhibit a level of productivity which is below than the exporting
threshold.
Uncertainty and industry adjustment
Although our modeling strategy gives our framework a partial equilibrium flavor, it
does not remove the interdependence among firms within industries. We can show that
εΨj≡ −V(Rj)
Ψj
∂Ψj
∂V(Rj)> 0,
or, equivalently, ∂Ψj/∂V(Rj) < 0 (see Appendix (B)). As expected, an increase in the
variance of income reduces the aggregate supply for destination market j and, in turn,
the equilibrium prices. Hence, equilibrium prices (14) increase with demand fluctuations
through two effects: (i) a direct effect due to risk aversion (as explained above) and (ii)
an indirect effect via the exit of firms relaxing competition among surviving firms. In
contrast, the effect of demand volatility on the export sales (or profits) is ambiguous
when Ψj adjusts to a change in V(Rj). Indeed, we have
dsij(v)dV(Rj)
= ∂sij(v)∂V(Rj)
+ ∂sij(v)∂Ψj
∂Ψj
∂V(Rj)= sij
V(Rj)2wiτijξεΨj
− ρvV(Rj)Ψ−2j
wiτijξ + ρvV(Rj)Ψ−2j
,
where ∂sij(v)/∂V(Rj) < 0 while ∂sij/∂Ψj < 0 and ∂Ψj/∂V(Rj) < 0 (see above). It
follows that a rise in demand volatility induces a reallocation of market shares from larger
firms to smaller ones as dsij(v)/dV(Rj) increases with ξ. Hence, the aggregate productiv-
ity of exporters can decrease ceteris paribus with a higher uncertainty, in accordance with
empirical facts (see Bloom, 2014). In addition, as the larger firms reduce their export
sales in high proportion when demand fluctuations increase, the export sales of smaller
exporters may expand at their expense (see Figure 3).
As a result, the effect of demand volatility on the probability of exporting is also
18
Figure 3: Productivity and reallocation of export sales when demand volatility increases
Production costs (ξij)
Export Sales (sij/2)
sij(ξ, V −)
sij(ξ, V +)A
Note: V − and V + mean low and high volatility, respectively.
ambiguous. Some standard calculations reveal that
dξijdV(Rj)
= ∂ξij∂V(Rj)
+ ∂ξij∂Ψj
∂Ψj
∂V(Rj)=[E(Rj)2
4wifij− ρvV(Rj)
(1 + 1
2εΨj
)]2εΨj
Ψ−2j
V(Rj)wiτij
where ∂ξij/∂V(Rj) < 0 while ∂ξij/∂Ψj < 0 and ∂Ψj/∂V(Rj) < 0 (see above). Hence,
the probability of serving a country decreases with the volatility of its demand provided
that fixed trade costs or demand volatility are not too high. If fixed trade costs are low
enough, more medium sized firms can export when demand fluctuations rise as the export
sales of large firms decrease (see Figure 4).
When we focus the total effect of demand fluctuation on quantity, it appears
dqij(v)dV(Rj)
= qij(v)V(Rj)
wiτijξ − ρvV(Rj)Ψ−2j (1 + εΨj
)wiτijξ + ρvV(Rj)Ψ−2
j
and d2qijdϕ∂V(Rj)
< 0 < d2qijdτdV(Rj)
so that the effects of V(Rj) on qij(v) when Ψj reacts to a change in demand volatility are
qualitatively similar to the effects on export sales. It also be noted that higher uncertainty
can make trade policy (lowering trade costs) or innovation policy (rising productivity) less
effective, in accordance with Bloom (2014).
To summarize:
19
Figure 4: The impact of higher demand volatility on exporting cutoff
ξij
Payoff (Πij)
Πij(ξ, V −)
Πij(ξ, V +)
A
wifij
ξij(V −) ξij(V +)
(a) Low export fixed costs wifij
ξij
Payoff (Πij)
Πij(ξ, V −)
Πij(ξ, V +)
A
wifij
ξij(V +) ξij(V −)
(b) High export fixed costs wifij
Notes: V − and V + mean low and high volatility, respectively. ξij : production costs.
Proposition 3.3 A rise in industry-level foreign demand uncertainty decreases industry
export sales but
(i) decreases the export sales of large firms and increases the market share of medium
sized firms;
(ii) increases the probability of exporting and the export sales of medium-sized firms
when trade costs are not too high.
The bad and the good variance: the role of skewness
We have considered a mean-variance analysis. The basic intuition is that the exporters
prefer higher expected returns and lower risk. However, we should consider that, ceteris
paribus, entrepreneurs prefer a high probability of an extreme event in the positive direc-
tion over a high probability of an extreme event in the negative direction. In order to take
into account the fact that the marginal willingness of exporter to accept a risk increases
when the distribution of the risk becomes more skewed to the right, we now consider the
mean-variance-skewness utility function where ηv > 0. The payoff of each firm is given
20
by (3). Given the inverse demand of consumers, we have
S(πij) = S(Rj)θ3vq
32ij(v)Ψ−3
j . (17)
and the Payoff -maximizing quantity is implicitly given by ∂Πo/∂qij = 0, or, equivalently,
E(Rj)2 θvΨ−1
j −(wiτijϕ
+ ρvV(Rj)θ2vΨ−2
j
)qij(v) 1
2 + ηv3S(Rj)θ3
vΨ−3j
2 qij(v) = 0. (18)
Clearly, if the income distribution is positively (resp., negatively) skewed, each exporter
has an incentive to increase (resp., decrease) its level of output for a given V(Rj). The
degree of skewness modifies the desirability of risk. It is readily to check that our pre-
dictions related to the impact of V(Rj) on quantity and prices according to the level of
productivity and trade costs hold when S(Rj) 6= 0. Using envelop theorem, it is straight-
forward to verify that ∂qij/∂S(Rj) > 0 whereas ∂pij/∂S(Rj) < 0 when the second order
condition holds (E(Rj)Ψ2j − ηv3S(Rj)θ2
vqij(v) > 0). Regardless of the sign of S(Rj), an
income distribution that is more skewed to the right induces higher level of output. More
interesting, standard calculations show that
∂2qij∂ϕ∂S(Rj)
> 0 > ∂2qij∂τ∂S(Rj)
and ∂pij∂τ∂S(Rj)
> 0 > ∂pij(v)∂ϕ∂S(Rj)
as ∂qij/∂ϕ > 0 and ∂qij/∂τ > 0. The magnitude of the positive impact of a higher S(Rj)
on production is stronger for firms exhibiting a higher productivity and for destination
implying lower trade costs. The prices move in the opposite direction.
4 Empirical Analysis
4.1 Identification strategy
According to our theoretical predictions exporters react in response to volatility by de-
creasing volumes(
∂qij
∂V(Rj)< 0)
and increasing prices(
∂pij
∂V(Rj)> 0), while in response to
21
upside gains exporters increase volumes(
∂qij
∂S(Rj)> 0)and reduce prices
(∂pij
∂S(Rj)< 0).
The estimations of these predictions may be plagued by potential endogeneity at the
aggregate level between trade openness and demand volatility. To mitigate this potential
bias we exploit the following strategy of identification. We use French firm exports at the
4-digit level k in a given year as our dependent variable. Then, we regress disaggregated
French exports on three different moments of the distribution of demand R in destina-
tion j: the lagged expected value(E(RK
j,t−1)), the volatility
(V(RK
jt))and the skewness(
S(RKjt))of demand at the 3-digit sector level. We expect variation in French 4-digit
exports to be explained by 3-digit demand shifters in destination countries. However, in
turn, it is unlikely that a particular 4-digit export in a destination affects substantially
a 3-digit sector demand in that destination. The 3-digit demand in a country is made
of imports from all sources (including itself) and all 4-digit sub-sectors. To reinforce this
strategy of identification, we remove French flows to compute countries’ demand and its
moments. Moreover, we exploit the different sources of variation of our panel and use
various combinations of fixed effects to control for unobservables: firm f , destination j,
4-digit sector k and time (year) t fixed effects.
4.2 Data
Demand R in destination country j is proxied with apparent consumption such as: aKjt =
ProductionKjt + ImportsKjt − ExportsKjt , where K is 3-digit sector and t year. Our sectoral
data on production, exports and imports come from COMTRADE and UNIDO and covers
the period 1995 to 2009. Using the absorption formula and the sector data, we construct
three important regressors at the industry 3-digit level K in the destination country j.
They correspond to three different moments of apparent consumption:
1. The mean demand or absorption(E(RK
j,t−1))computed in year t as the log of the
lagged value of mean absorption over the 5 previous years;
2. The volatility of demand(V(RK
jt))computed as the standard deviation of the yearly
growth rates of aKjt over 6 years and the sub-sectors k. As an example, consider
22
the manufacture of beverages (K=155) in the United Kingdom in 2000. We first
compute the yearly growth rates of the UK’s apparent consumption over 1995 to
2000, and over the 4 sub-sectors of 155.7 Then, we compute the standard deviation of
the 20 growth rates. Figure (5) reports the volatility of demand in the manufacture
of beverages in 2005. For a same level of mean demand for beverages in 2005, we
observe different levels of demand volatility.
3. The unbiased skewness of demand(S(RK
jt))computed as the skewness of the yearly
growth rates of aKjt over 6 years and the sub-sectors k.
Figure 5: Demand level and demand volatility of a given 3-digit sector
IRNLVA
MEX ITA
SVK PAN
ERI
NLD
MDA
ETH
JPN
SVN
ROMPRT
JOR
MAR
UKR
TJK
URY
IDN
DNK
KAZ
EGY
AUS
TTO
GEO
CHN
MWI
GRC
KGZ
CAN
TUR
OMN
ARM
ECU
EST
NOR
MYS
MLT
BLX
HUNPER
COL
IND
USA
FRA
CYP
DEU
LTU
AZE
SEN
KORPOL
MKD
GBRESP
FIN
810
1214
1618
Log
of A
ppar
ent C
onsu
mpt
ion
(Mea
n) -
3 d
igit
-2.7 -.7Log Volatility (standard deviation) of destination county - 3 digit
Manufacture of beverages in 2005
To construct our dependent variable and test our theoretical predictions we use very
rich firm-destination specific export data from the French customs over the period 2000
to 2009. We observe volume (in tons) and value (in euros) of exports for each product
and destination, for each firm located on the French metropolitan territory. Unit values
are computed as the ratio of export value to export volume.
4.2.1 Descriptive Statistics
We present in this section some descriptive statistics on French exports and firms, and
on the volatility and skewness of demand in sectors and destination countries included7The 4 sub-sectors are: 1551 - Distilling, rectifying and blending of spirits; ethyl alcohol production
from fermented materials; 1552 - Manufacture of wines; 1553 - Manufacture of malt liquors and malt;1554 - Manufacture of soft drinks; production of mineral waters.
23
in our sample. In our empirical analysis, we focus on manufactured exports. French
manufactured export flows reach on average 258,062 millions USD and 94,072 millions of
tons per year. On average, our sample includes 70,239 firms per year, serving 81 countries
and 119 4-digit sectors. Table (1) provides the annual statistics. All sectors are served
every year. By contrast, we observe some changes in the number of destinations, with an
increase over the 2000-2006 period and a significant decrease during the 2008-2009 crisis.
The number of exporting firms is decreasing over time (except in 2006).
Table 1: Number of French firms and destinations per year
Year Nb. exporters Nb. destinations2000 79151 802001 77115 802002 76509 812003 74304 862004 72021 852005 70871 832006 71207 882007 68400 862008 65803 792009 47011 66Note: 119 4-digit sectors per year.
The turnover of firms in sectors and destinations is rather high in our sample over
the period 2000-2009. On average, a firm is present 2.77 years in one destination-sector
(4-digit). Firms serve on average 1.96 sectors per destination-year and 3.14 destinations
per sector-year.
Each year, exporting firms may export to one sector (4-digit) and one destination
(mono-destination and mono-sector firms), to various sectors but to one destination
(mono-destination and multi-sectors firms), to various destinations and to one sector
(multi-destinations and mono-sector firms) or to multi destinations and sectors (multi-
destinations and multi-sectors firms). Table (2) presents the share of each of the four
categories in the total of exporting firms. The two main categories of firms in our sample
are mono-destination and mono-sector firms (col. 1), and multi-destinations and multi-
sectors firms (col. 4). The shares are fairly stable over time. However, the share of
multi-destinations firms has slightly decreased in 2009. Some firms, which were present
in several destinations, seem to have reduced their international exposure in 2009 because
24
of the world crisis.
Table 2: Share of the four categories (mono- vs. multi-destinations/sectors) of firms
Year Mono-dest. Mono-dest. Multi-dest. Multi-dest.& Mono-sector & Multi-sector & Mono-sector & Multi-sector
(1) (2) (3) (4)2000 35.8 8.2 19.3 36.72001 36.1 7.9 18.9 37.12002 36.1 7.9 18.6 37.42003 35.5 7.5 19.1 37.92004 34.5 7.5 19.4 38.62005 33.3 7.8 19.7 39.22006 33.5 7.5 19.7 39.32007 32.7 7.6 20.1 39.52008 32.9 7.6 20.5 39.02009 37.7 8.2 17.7 36.4Dest. means destination.
Figure (6) reports the distribution of demand volatility (in logs) and demand skewness
across 2-digit sectors. The ranking of sectors is not similar for both moments and only
one sector (Tobacco) has a negative median skewness.
Figure 6: Distribution of volatility and skewness demand
−4 −3 −2 −1 0 1
Other transport equipmentRadio, tv, comm. equipment
Machinery and equipmentcoke, petroleum, nuclear fuel
Publishing, printingMedical, optical instruments
Fabricated metal productsFurniture, manufacturing n.e.c
Electrical machineryBasic metals
Motor vehiclesChemicals
LeatherWood
Wearing apparelOffice, accounting
TextilesFood & beverages
Other mineral productsTobacco
PaperRubber and plastics
Outside values excluded
Volatility, by industry
−4 −2 0 2 4
Machinery and equipmentOther transport equipment
Furniture, manufacturing n.e.cPublishing, printingFood & beverages
Other mineral productsTextiles
ChemicalsWearing apparel
Basic metalsLeather
coke, petroleum, nuclear fuelFabricated metal products
Motor vehiclesWood
Medical, optical instrumentsRadio, tv, comm. equipment
Electrical machineryPaper
Rubber and plasticsOffice, accounting
Tobacco
Outside values excluded
Skewness, by industry
Figure (7) presents the distribution volatility across the destinations of French exports.
For clarity, we select the 20 top main destinations and the 20 minor destinations over the
2000-2009 period. The figure reports the median (log) volatility for each of these partners.
For each country, the median is computed using all 3-digit sectors and years for which
25
we are able to compute the absorption (at most we have 10 years * 57 three-digit sectors
= 570 observations). We can observe that the main destinations of French exports have
lower median volatilities that minor destinations. Note that there is also a composition
effect: main partners are often developed countries, which are less volatile than developing
ones. This pattern is confirmed in Figure (8), where we try to answer to the question:
which are the most/least volatile countries? The United States has very low volatility,
as well as the United Kingdom and Canada. By contrast, the most volatile countries (in
the left panel) tend to be developing countries. On average, developed countries are less
volatile than developing ones.
Figure 7: Volatility of demand across destinations
−2.5 −2 −1.5 −1 −.5 0
20−IRL19−HUN18−DNK17−SGP16−CAN15−MAR14−AUT13−TUR12−SWE11−PRT10−POL09−CHN08−JPN07−NLD06−USA05−BLX04−ITA
03−GBR02−ESP01−DEU
Outside values excluded
Volatility (median), 20 main partners
−2 −1.5 −1 −.5 0
20−LVA19−ARG18−KWT17−PER16−AZE15−ECU14−PHL13−QAT12−ETH11−VNM10−URY09−GEO08−MKD07−MDA06−ARM05−TTO04−MWI03−KGZ02−ERI
01−BOL
Outside values excluded
Volatility (median), 20 minor partners
Which are the more/least skewed countries? Results are reported in Figure (9). As
previously done, each sub-figure presents 20 countries and reports the median (log) skew-
ness for each of these countries over 2000-2009. We keep only countries for which we have
at least 10% of the 570 potential observations (we do the same for Figure 8). We can
notice that developed countries are often less skewed. The difference for skewness be-
tween developing and developed countries seems to be less pronounced than for volatility.
However, one limit of our approach is the number of observations per country: for some
(developing) countries, the number of sector-year for which we are able to compute the
volatility (and even more the skewness) is rather small and this restriction may of course
26
bias the median value that we obtain.
Figure 8: Most and least demand volatile countries
−2.5 −2 −1.5 −1 −.5 0median volat.
NLDISRITAIRL
AUTGRC
FINPOLBLX
NORJPNDEUPERPRTDNKESPCHNCANGBRUSA
20 least volatile countries
−1.5 −1 −.5 0median volat.
ERIIDN
KGZGEOARMMDAAZEKWTETH
OMNIRN
ECUKAZTURMLTUKRSENMYSSVKLTU
20 most volatile countries
Figure 9: Most and least demand skewed countries
−.1 0 .1 .2 .3median skewness
POLROMARGLVAKORURYCYPGBRMKDCOLPRTESPBLXJPNBRAIRN
CANUSACHNRUS
20 least skewed countries
0 .2 .4 .6 .8median skewness
IDNMLTGEO
ERIARMKGZKAZ
OMNUKRSVKETHMDAHUNAZEBGRMYSECUSVNEGYSWE
20 most skewed countries
4.3 Empirical results
We now present our empirical results on the intensive and the extensive margin of trade.
27
4.3.1 Intensive trade margin
Export volumes
The estimated equation for the export volumes comes directly from the theoretical
model:
ln qkfjt = lnE(RKjt) + ρV(RK
jt) + ηS(RKjt) + FE + εkfjt, (19)
where qkfjt is French firm f export volumes to j in 4-digit k in year t. This variable is
regressed on different moments of demand defined in the data section (4.2). FE represents
a vector of different combinations of fixed effects. The estimations of equation (19) is
reported in Table (3). The sample covers the period 2000 to 2009.
Table 3: Firm level estimations: export volumes
Dep. Var.: Firm export volumes: ln qkfjt(1) (2) (3)
Ln Mean Demand3Kj,t−1 0.250a 0.075a 0.060a
(0.028) (0.025) (0.21)
Ln Volatility3Kjt -0.017b -0.029b -0.022b
(0.008) (0.014) (0.011)
Skewness3Kjt 0.007b 0.012b 0.009b
(0.003) (0.006) (0.004)AdjustedR2 0.814 0.377 0.635Fixed Effects:Firm.Destination.Sectorfjk Yes - -Timet Yes - -Firm.Destination.Timefjt - Yes -Sector (4-digit)k - Yes -Firm.Sector.Timefkt - - YesDestinationj - - YesNote: Robust and industry-destination clustered standard errors inparentheses. a : p < 0.01, b : p < 0.05, c : p < 0.1. N=5,668,638.
The results are in line with our theoretical predictions. An increase in demand level
and demand skewness in destination markets increases export volumes. In contrast, an
increase in volatility reduces exports. How economically meaningful are the estimates
of volatility and skewness? Based on the first column within estimates, we find that in
2005 a one standard deviation increase in the average of volatility of Belgium, reduces
aggregate French exports to Belgium by 1.2%, while a one standard deviation increase in
the average of volatility of China, reduces aggregate French exports to China by 1.3%.
Moreover, if the United Kingdom market would be as volatile as Vietnam, French exports
28
to the UK would decrease by 2.5%. On the other hand, if the UK would be as skewed
as Vietnam, French exports to the UK would increase by 0.5%. This implies that both
absolute and downside risk matter for exporters.
Export prices
The estimated equation for the export prices comes from the theoretical model as well:
ln pkfjt = ln(
RKj,t−1
E(RKj,t−1)
)+ ρV(RK
jt) + ηS(RKjt) + FE + εkfjt, (20)
where pkfjt is French firm f export unit values to destination j in 4-digit sector k in
year t. Table (4) reports the estimates of equation (20) with the same sets of fixed effects
as for the export volumes.
Table 4: Firm level estimations: export prices
Dep. Var.: Firm export unit values: ln pkfjt(1) (2) (3)
Ln Demand3Kj,t−1 0.022b 0.001 0.001
(0.011) (0.017) (0.006)
Ln Mean Demand3Kj,t−1 -0.038c -0.003 -0.004
(0.021) (0.020) (0.008)
Ln Volatility3Kjt 0.014b 0.004 0.002
(0.007) (0.007) (0.003)
Skewness3Kjt -0.005b -0.003 -0.001
(0.002) (0.003) (0.001)R2 0.844 0.658 0.811Fixed Effects:Firm.Destination.Sectorfjk Yes - -Timet Yes - -Firm.Destination.Timefjt - Yes -Sector (4-digit)k - Yes -Firm.Sector.Timefkt - - YesDestinationj - - YesNote: Robust and industry-destination clustered standard errors inparentheses. a : p < 0.01, b : p < 0.05, c : p < 0.1. N=5,668,638.
Results on export prices are in line with our predictions but are not robust across all
specifications. The within estimates, reported in column (1), highlight the two opposite
effects associated with demand in the destination market. On the one side, higher lagged
demand raises export prices (demand effect). On the other side, higher expected foreign
demand, captured by the mean demand variable, increases the competition across firms
(which have an incentive to reduce their level of production) and, in turn, decreases prices.
29
As expected, the volatility and skewness estimates have an opposite effect compared with
their influence on export volumes.
Interaction effects of uncertainty
Proposition (3.1) establishes that the negative effect of demand volatility on export
quantities is strengthened when firm productivity increases and trade costs decrease.
Remember that the variance of profits in a given foreign market is equal to the volatility
(variance) of foreign demand times the output size dedicated to that foreign country.
Given that the most productive firms export more, they are at the margin more affected
by the increase in demand volatility. With the same logic, the lower the trade costs, the
higher the quantities sold and the larger the effect of demand volatility at the margin.
To capture the non-linearity of demand volatility related to trade costs, we use the
distance variable to destination markets, which is a usual proxy for trade costs. More
precisely, we split the distance variable into quartiles. The four distance intervals (in km)
are: [0, 440); [440, 1110); [1110, 1875); and [1875, maximum]. Then, we interact the four
categories with the volatility of demand and compute the predicted mean trade volumes
(in logs) for the deciles of volatility. Results are presented in Figure (10).
Figure 10: Volatility, distance and export volumes
-1.2
-1-.
8-.
6-.
4-.
2P
redi
cted
Mea
n T
rade
Vol
ume
(logs
)
-2.69 -2.45 -2.27 -2.11 -1.96 -1.8 -1.62 -1.4 -1.01Deciles of Demand Volatility
Distance (km) < 440440 <= Distance < 11101110 <= Distance < 1875Distance >= 1875
Predictive Margins with 95% CIs
As expected and depicted in Figure (10), the lower the distance to a destination, the
higher the exports, ceteris paribus. However, the increase in demand volatility affects
differently the quartiles of distance. In particular, export volumes to the more distant
30
markets are line with our theoretical prediction: ∂2qij/∂τij∂V(Rj) > 0.
To capture the non-linear effect of firm productivity and volatility on export volumes
(∂2qij/∂ϕ∂V(Rj) < 0), we use a similar strategy: we split the log of productivity into
quartiles and interact the four categories with demand volatility. Then, we compute the
predicted mean trade volumes (in logs) for the deciles of volatility and the quartiles of
productivity. The different predictions of trade are plotted in Figure (11). This plot shows
three interesting results: (1) the most productive firms export more than the others; (2)
the larger the demand volatility, the lower the export volumes for all levels of productivity;
and (3) the marginal decrease in exports is larger for the most productive firms as the
volatility increases.
Figure 11: Volatility, productivity and export volumes
-.85
-.8
-.75
-.7
-.65
-.6
Pre
dict
ed M
ean
Tra
de V
olum
e (lo
gs)
-2.69 -2.45 -2.27 -2.11 -1.96 -1.8 -1.62 -1.4 -1.01Deciles of Demand Volatility
Log Productivity < P(25)P(25) <= Log Productivity < P(50)P(50) <= Log Productivity < P(75)Log Productivity >= P(75)
Predictive Margins with 95% CIs
4.3.2 Extensive trade margin
Entry and exit
We now explore the impact of uncertainty on the extensive margin of trade. We dis-
tinguish between the entry of new firms on the international market and the exit of the
incumbents from that market. For the entry, our dependent variable is the probability
for a firm f to start exporting to destination j in 4-digit sector k in year t. This choice
model can be written in the latent variable representation, with y∗fjkt the latent variable
that determines whether or not a strictly positive export flow is observed from firm f to
31
destination j on sector k in year t. Our estimated equation is therefore as follows:
Pr(yfjk,t|yfjk,t−1 = 0) =
1 if y∗fjkt > 0
0 if y∗fjkt ≤ 0(21)
with
y∗fjkt = lnE(RKjt) + ρV(RK
jt) + ηS(RKjt) + FE + εkfjt.
As previously, E(RKjt), V(RK
jt) and S(RKjt) represent different moments of demand (see
section 4.2). This equation is estimated using a linear probability model. The inclusion
of fixed effects (FE) in a probit would give rise to the incidental parameter problem. The
linear probability model avoids this issue. In all regressions, we account for correlation of
errors by clustering at country-4-digit sector level. Results are reported in the first three
columns of Table (5). Estimations cover the entry on the international market over the
2000-2009 period.
In addition to the probability of entry, one can also study the exit transition. Higher
volatility or lower upside gains may indeed increase exit of firms from the export market.
In that case, our dependent variable is the probability that firm f in destination j and
sector k in year t − 1 stop exporting to this destination that product k in year t. Ex-
planatory variables are the same as for the entry estimations and results are reported in
the last three columns of Table (5)
Results are again in line with the theoretical model. The probability for a firm to enter
the export market is positively and significantly influenced by an increase in demand and
in potential upside gains (skewness) in the destination markets, while an increase in
volatility reduces the firm’s entry. Opposite effects are observed for the probability of
exit: a higher demand and skewness lower the probability of exit, while a higher volatility
increases it.
Interaction effects of uncertainty
We now investigate whether the impact of volatility on the probability of entry and
exit from the export market is affected by trade costs. To do so, we interact the volatility
32
Table 5: Firm level estimations: probability of entry and exit from the internationalmarket
Dep. Var.: Firm’s entry Firm’s exit:Prob(yfjk,t = 1|yfjk,t−1 = 0) Prob(yfjk,t = 0|yfjk,t−1 = 1)(1) (2) (3) (4) (5) (6)
Ln Mean Demand3Kj,t−1 0.005a 0.004a 0.002a -0.019a -0.003 -0.012a
(0.0002) (0.0001) (0.0002) (0.002) (0.002) (0.002)
Ln Volatility3Kjt -0.029a -0.019a -0.009a 0.020c 0.043a 0.008
(0.002) (0.001) (0.002) (0.011) (0.009) (0.010)
Skewness3Kjt 0.0003a 0.0002b 0.0002b -0.0009 -0.001b -0.001
(0.0001) (0.0001) (0.0001) (0.0007) (0.0006) (0.0005)R2 0.205 0.144 0.066 0.374 0.279 0.259Fixed Effects:Firm.Destination.Sectorfjk Yes - - Yes - -Timet Yes - - Yes - -Firm.Destination.Timefjt - Yes - - Yes -Sector (4-digit)k - Yes - - Yes -Firm.Sector.Timefkt - - Yes - - YesDestinationj - - Yes - - YesNote: Robust and clustered standard errors in parentheses.a : p < 0.01, b : p < 0.05, c : p < 0.1. N= 52,752,877 for entry and N = 4,308,691 for exit.
variable with the distance between France and the destination market j. Results are
presented in Table (6). According to our theoretical predictions, we expect a positive
estimated coefficient on the interaction term for the probability of entry and a negative
one for the probability of exit: trade costs tend to lower the impact of volatility.
Empirical estimations are in line with these theoretical predictions. The interaction
term is positive and highly significant for the probability of entry, and to a lesser extent for
the probability of exit. Furthermore, following the inclusion of the interaction term, the
coefficients on the volatility variable remain very similar to the ones obtained in Table (5).
33
Table 6: Interaction of volatility with distance: probability of entry and exit from theinternational market
Dep. Var.: Firm’s entry Firm’s exit:Prob(yfjk,t = 1|yfjk,t−1 = 0) Prob(yfjk,t = 0|yfjk,t−1 = 1)(1) (2) (3) (4) (5) (6)
Ln Mean Demand3Kj,t−1 0.005a 0.004a 0.002a -0.019a -0.003 -0.012a
(0.0002) (0.0001) (0.0002) (0.002) (0.002) (0.002)
Ln Volatility3Kjt -0.029a -0.019a -0.009a 0.020c 0.043a 0.008
(0.002) (0.001) (0.002) (0.011) (0.009) (0.010)
Ln Volatility3Kjt × Ln Distancej 0.004a 0.002a 0.001a -0.003c -0.005a -0.0002
(0.0002) (0.0002) (0.0002) (0.001) (0.001) (0.001)
Skewness3Kjt 0.0003a 0.0002b 0.0002b -0.0009 -0.001b -0.001
(0.0001) (0.0001) (0.0001) (0.0007) (0.0006) (0.0005)R2 0.205 0.145 0.066 0.374 0.279 0.259Fixed Effects:Firm.Destination.Sectorfjk Yes - - Yes - -Timet Yes - - Yes - -Firm.Destination.Timefjt - Yes - - Yes -Sector (4-digit)k - Yes - - Yes -Firm.Sector.Timefkt - - Yes - - YesDestinationj - - Yes - - YesNote: Robust and clustered standard errors in parentheses.a : p < 0.01, b : p < 0.05, c : p < 0.1. N= 52,752,877 for entry and N = 4,308,691 for exit.
5 Preliminary conclusion
Does demand volatility matter for exports? Yes and skewness as well!
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Appendices
A Price setting
The demand for a variety v is
qij(v) = R2jθ
2vΨ−2
j pij(v)−2
so thatpijqij(v)Rj
= Rjθ2vΨ−2
j pij(v)−1
Summing this expression over each variety consumed in country j yields
Ψ−2j = R−1
j
[∫Ωj
θ2vpij(v)−1dv
]−1
implying the demand for a variety can be rewritten as follows
qij(v) = Rjθ2v
[∫Ωj
θ2vpij(v)−1dv
]−1
pij(v)−2 = Rjθ2vPjp
−2ij
with
Pj ≡[∫
Ωj
θ2vpij(v)−1dv
]−1
.
36
Hence, the export profit is
πij = Rjθ2vPj/pij − cijRjθ
2vPjp
−2ij − wifij
with cij ≡ wiτij/ϕ. The payoff of each firm is as follows:
Πo(v) = E(πi)− ρvV(πi),
Given the demand of consumers, we have
E(πij) = E(Rj)θ2v
Pjpij− cijE(Rj)θ2
v
Pjp2ij
− wifi,
and
V(πij) =(p2ij − c2
ij
)p−4ij P
2j θ
4vV(Rj)
It appears that the expected profit is maximized when the price is equal 2 times the
marginal cost cij while the variance is minimized whens the price is equal to the marginal
cost.
The first order condition implies that the equilibrium price is implicitly given by
Φ(pij) = 0 with
Φ(pij) ≡ − (pij − 2cij)E(Rj) + ρv(p2ij − 2c2
ij
)2p−2
ij Pjθ2vV(Rj) (22)
while the second order condition implies
E(Rj)− ρv8c2ijp−3ij Pjθ
2vV(Rj) > 0
or, evaluated at Φ(pij) = 0,
(p2ij − 2c2
ij
)pij − 4c2
ij (pij − 2cij) > 0
Without uncertainty, the equilibrium price would be pij = 2cij, which is identical to the
37
price prevailing when firms determine strategically the level of quantity. However, under
uncertainty, pij = 2cij is not an equilibrium as long as ρv > 0. Introducing pij = 2cij into
(22) implies Φ(pij) > 0 so that the equilibrium price under uncertainty is higher than
2cij. Using the envelop theorem:
∂pij∂V(Rj)
= E(Rj)V(Rj)
pij − 2cijE(Rj)− 8ρvc2
ijPjθ2vV(Rj)p−3
ij
> 0
B Industry supply and income volatility
In this Appendix, we show that ∂Ψj/∂V(Rj) < 0. According to (7) and (13), we have
Λ[Ψj,V(Rj)] = 0 with
Λ ≡ Ψj −∑
kMk
∫ ξkj
0
E(Rj)2 Ψ−1
j
[wkτkjξ + ρvV(Rj)Ψ−2
j
]−1µ(ξ)dξ,
where ∂Λj/∂V(Rj) > 0 because both θvq1/2ij and ξij decrease with V(Rj). The envelop
theorem implies
sign ∂Ψj
∂V(Rj)= −sign∂
2Λ∂Ψj
,
as ∂Λ/∂V(Rj) > 0. Standard calculations show that
∂Λ∂Ψj
=1−Ψ−1
j
∑kMk
∫ ξkj
0 θv [qkj(ξ)]12ρvV(Rj)Ψ−2
j −wkτkjξ
ρvV(Rj)Ψ−2j +wkτkjξ
µ(ξ)dξ
−∂ξkj
∂Ψjθv[qkj(ξkj)
] 12 ,
(23)
where ∂Λ/∂Ψj > 0 as the second term on the RHS of (23) is inferior to 1 and ∂ξkj/∂Ψj < 0.
As a result,
εΨj≡ −V(Rj)
Ψj
∂Ψj
∂V(Rj)> 0.
38