Sandrine Levasseur
email: sandrine.levasseur@sciences-po.fr

" Updating empirical evidence on business cycles synchronization between CEECs and the euro area : How

Keywords: business cycles, Central and Eastern European Countries. JEL codes : E32, C22, P20, F15

important is the recent period "

Introduction

Upon joining the European Union (EU), adoption of the euro constitutes another important step for Central and Eastern European countries (CEECs), as they have no option to opt-out of monetary union. In that purpose, CEECs have to fulfil the so-called Maastricht criteria whereas participating for at least two years in the "new" European exchange rate mechanism (ERM-II, hereafter)1 . Note that Slovenia has already adopted the euro, entering European and Monetary Union (EMU) on the 1 st January 2007 while Lithuania was refused as not fulfilling the inflation criteria. Slovakia is targeting entry in the euro area for 2009 which may be effective if the country goes on successfully in curbing inflation and containing public deficit. All the three Baltic countries and Bulgaria are currently participants in the ERM-II whereas Hungary, Poland and the Czech republic have pushed back their timetables. The government of Romania has not yet specified a date to participate in the ERM-II.

While the EU officials put emphasis on the respect of nominal criteria for entering EMU, empirical researchers have rather focused on the business cycles synchronization across countries for evaluating the desirability of sharing a common currency. Especially, following the theory of optimal currency area (OCA), a strong degree of business cycles synchronization across EMU members reduces the cost of giving up an independent exchange rate and monetary policy. This view finds its roots in prices and wages rigidities as well as insufficient labour mobility to absorb the impact of (temporary) asymmetrical shocks across countries, as put first by Mundell fifty years ago. Some authors, among others [START_REF] Frankel | The Endogeneity of the Optimum Currency Area Criteria[END_REF], have nevertheless challenged this view, arguing that OCA criteria may be "endogenous" to the adoption of a common currency or, put differently, that countries which do not satisfy OCA criteria ex-ante may yet satisfy them ex-post. Their argument is that sharing a common currency stimulates trade flows across countries which, in turn, induce higher business cycles synchronization.

With the prospect of future EMU membership for the CEECs, a lot of empirical studies have been devoted to evaluate the degree of business cycles between CEECs and the euro area (or Germany)2 . From this literature using either simple correlations of GDP co-movements or more sophisticated methodologies, two main findings emerge. First, CEECs exhibit a quite different degree of business cycles synchronization with the euro area. More precisely, Poland, Hungary and Slovenia were considered as more suitable EMU members than other CEECs, especially Lithuania or Slovakia. Second, as soon as time spans are not too short and cover at least the very beginning of the new millennium, the business cycles of CEECs and the euro area are found increasingly synchronized over time, which is interpreted as a consequence of deeper trade integration within the EU (i.e. as a proof of the endogeneity of OCA) although most of CEECs are not yet EMU members.

To date, however, there is no systematic empirical evidence on which GDP components may drive the co-movements between CEECs and the euro area. Especially, are external components much more important than internal ones? How does the contribution of each component evolve over time? Does EU membership in 2004 for most of CEECs change significantly the degree of co-movements compared to the previous period? [START_REF] Darvas | Business cycles synchronization in the enlarged EU[END_REF] and, to some extent, [START_REF] Carmignani | The characteristics of Business Cycles in Selected European Emerging Market Economies[END_REF], [START_REF] Benczur | Economic Fluctuations in Central and Eastern Europe-the facts[END_REF] make a first step in this direction by analysing the co-movements of some GDP components.

In this paper, we begin with an extension of [START_REF] Darvas | Business cycles synchronization in the enlarged EU[END_REF] in three directions. First, we use a longer time span, adding most of four years of quarterly data over the recent period. By this way, we cover the first years of EU membership for most of CEECs. Second, we include data for Bulgaria, Romania and Croatia, which have not been extensively investigated in previous studies as noted by Fidrmuc and Korhonen (2004). We then offer empirical evidence on these countries which is interesting per se. Third, we use all demand components, including public consumption. We are then able to assess if -and to which extent -GDP co-movements are policy-driven versus trade-driven, for instance.

While useful, simple (cross-country) correlations based on raw data have the main drawback to conflate shocks and the responses of the economy to those shocks. The paper goes on with a structural VAR analysis (SVAR), allowing for a distinction between demand and supply shocks affecting CEECs and the euro area. The SVAR methodology was pioneered by [START_REF] Bayoumi | Shocking aspects of European Monetary Unification[END_REF] in the context of assessing OCA theory for actual EMU members, then was extensively applied for CEECs and the euro area3 . Our updated SVAR analysis allows us to assess if supply shocks continue to be the main source of business cycles synchronization between CEECs and the euro area, as found in previous empirical works.

The paper concludes on the desirability of EMU membership based on "new" GDP comovements and estimates of shocks affecting CEECs and the euro area.

Key evidence on co-movements of GDP and its main components

In what follows, we report the correlation of real GDP growth rates -and of its components -for each CEEC with the euro area4 . Quarterly data are taken from Eurostat and available for most of countries and variables since 1995. For each variable, growth rates are computed as fourth differences of the natural logarithm (except for changes in inventories). For most of CEECs, we consider a standardized full period running from 1996 to 2006 and two sub-periods (i.e. 1996-2001 and 2002-2006) to get some insights on the developments of co-movements or, put differently, on the postulated endogeneity of OCA. Notable exceptions are Croatia and Romania for which data are respectively available since 1997 and 1999. Consequently, the full period is shorter for these two countries and, their first sub-period runs from [START_REF] Frankel | The Endogeneity of the Optimum Currency Area Criteria[END_REF]-2001for Croatia and 2000-2001 for Romania.

In our commentaries, we grant a particular attention to the significance of coefficients to avoid an arbitrary split of CEECs according to their degree of co-movements with the euro area5 . In that respect, considering the Hodrick-Prescott filter as an extracting measure of business cycles does not change the qualitative results, albeit affecting the numerical values of coefficients6 .

2.1.GDP co-movements

Results reported in Table 1 (in the Appendix) confirm the picture of heterogeneity between CEECs in terms of GDP co-movements with the euro area, as already found in previous studies. Over the full period, only four CEECs present a positive (and significant) correlation of GDP growth rates with the euro area (Poland, Hungary, Slovenia and, to a lesser extent, Bulgaria). At the other extreme, Lithuania and Slovakia present a negative (and significant) correlation of their GDP growth rates with the euro area. For the remaining CEECs, correlations are either positive or negative, but not significantly different from 0 at the 10 % level.

Comparing the correlations over 1996-2001 with those over 2002-2006, we observe that the GDP co-movements with the euro area has increased over time for most of CEECs. The increase of GDP co-movements is particularly impressive for Slovakia which ranges among the highest correlations over the 2002-2006 period. Increases in GDP co-movements are also quite large for countries like the Czech republic, Slovenia and Latvia. In this general picture, notable exceptions are Hungary for which the decrease in GDP co-movements with the euro area is very sharp and, to a lesser extent, Bulgaria and Croatia.

Private consumption co-movements

The private consumption of CEECs and the euro area is by far less synchronized than their GDP (Table 2)7 . Over the full period, most of CEECs report negative (and significant) private consumption correlations with the euro area. A notable exception is Bulgaria for which the correlation of private consumption with the euro area is significantly positive.

Interestingly, over the time, the correlation of private consumption growth rates with the euro area has substantially increased for most CEECs, turning significantly positive in the case of Romania, Latvia and Slovenia for the 2002-2006 period. A notable exception is Hungary for which the correlation of private consumption with the euro area falls from a (significant) positive value to a (significant) negative value between 1996-2001 and 2002-2006. The increase in private consumption synchronization over the recent period -except for Hungary -is an important point to notice, as previous empirical studies based on earlier data cannot uncover this feature. It is important to note however that it cannot be explained by a higher cross-country insurance against the consumption risk, as capital outflows of CEECs towards more advanced EU countries remain small. Rather, more "normal" consumption fluctuations may account for this feature, after painful adjustment due to the transition from planned to market economy [START_REF] Darvas | Business cycles synchronization in the enlarged EU[END_REF].

Gross fixed capital formation co-movements

As for the GDP variable, there is a large heterogeneity between CEECs in terms of gross fixed capital formation (GFCF) co-movements with the euro area (Table 3). Over the full period, four of them (Poland, Slovenia, Bulgaria and Romania) exhibit positive (and significant) correlations with the euro area while, at the other extreme, three others (Croatia, the Czech republic and Lithuania) exhibit negative (and significant) correlations. For the remaining CEECs, correlations either negative or positive are no longer significant.

Comparing the recent period to the previous one, we observe increases in GFCF correlations with the euro area for most of CEECs. In particular, both Slovakia and Romania are reporting a very large increase in GFCF correlation, turning significantly positive over the recent period as for Poland and Slovenia. Hungary is again a notable exception, exhibiting a negative (albeit insignificant) correlation.

Changes in inventories co-movements

Changes in inventories of CEECs are poorly correlated with those of the Euro area, with no substantial modification over the recent period (Table 4). Only Romania was reporting a negative (and significant) correlation for the 2000-2001 period. But this negative correlation does no longer characterize the recent period.

Final consumption of government co-movements

The final consumption of government of CEECs is also in general poorly correlated with the one of the euro area (Table 5). Over the full period, only the Czech republic has a significant (and positive) correlation with the euro area. For the recent period, i.e. 2002-2006, while we detect an increase in the governmental final consumption correlations, only Croatia is reporting a positive (and significant) correlation. This result is particularly striking as EU membership would have to constraint -and then to synchronize -the fiscal policy via the Stability and Growth Pact (SGP) and the Maastricht criteria. Restraining further the time span to assess more precisely the impact of the SGP on CEECs government behaviour does not change importantly the correlations 8 . Data of final consumption of government do not confirm the view according to which the SGP would push toward greater fiscal policy synchronization within the EU. However, as soon as we consider data of fiscal position as a percent of GDP, the positive role of SGP (and fiscal Maastricht criteria) in synchronizing fiscal positions becomes evident for most of CEECs. Exceptions are Hungary and Slovakia (see Table 5

bis).

In Hungary, this is evidently due to the public finances' mistakes of the Socialist-led coalition while, in Slovakia, the implementation of tax flat may account for that feature.

Exports and imports co-movements

Exports and imports of CEECs and the euro area are by far the most correlated variables among demand components (Tables 6 and7). Moreover, looking at Table 1, there is a strong evidence of a highly positive relationship between GDP and trade variables co-movements, suggesting that exports and imports are important factors in synchronizing business cycles of CEECs and the euro area. This feature is even better illustrated by considering the crosscorrelations of CEECs imports and euro area exports and, symmetrically, of CEECs exports and euro area imports (Tables 6 bis and 7 bis). Higher (and very often significant) crosscorrelations of trade variables over 2002-2006 than over 1996-2001 show that trade integration within the EU is becoming deeper, thus contributing to higher GDP comovements 9 . 8 In the case of CEECs joining EU in May 2004, the SGP applies to fiscal position of 2003. As specified in the EU legislation, a procedure for "excessive deficit" is applied as soon as the fiscal deficit is reaching the limit of 3 % of GDP. In May 2004, this was the case in 4 out of 8 CEECs new EU members (the Czech Republic, Hungary, Poland and Slovakia). See [START_REF] Levasseur | Fiscal Policies of the CEECs After Joining the EU[END_REF]. 9

We take as granted that strong trade links across countries are conducive to high business cycles synchronization, as shown by a large empirical literature beginning with [START_REF] Frankel | The Endogeneity of the Optimum Currency Area Criteria[END_REF]. See [START_REF] Fidrmuc | Meta-Analysis of the Business Cycle Correlation between the Euro Area and the CEECs[END_REF] for references on this point.

Synthesis on the evolution of GDP co-movements and contributing components

Updating empirical evidence on business cycles synchronization of CEECs with the euro area reveals interesting features. First, there is a general trend towards greater GDP comovements with the euro area. The increase in GDP co-movements is particularly large for Slovakia, which ranges among the highest GDP co-movements with the euro area over the recent period. Nevertheless, in this general trend towards higher synchronization, Hungary is a notable exception, exhibiting a very sharp decline of its GDP co-movements with the euro area. Those two latter features were uncovered by previous empirical studies based on earlier data. Finally, Lithuania is yet much lagging behind other CEECs in terms of GDP comovements with the euro area, especially compared to Slovenia which is placed first in this respect.

Considering the correlations of main GDP components -and their developments over time -reveals other interesting features. Especially, internal variables such as private consumption and GFCF have played a growing role in explaining the GDP co-movements over the recent period. This point is particularly made clear in Graph 1 which reports the aggregated contribution of consumption and GFCF components versus exports and imports components to changes in GDP co-movements between the two sub-periods. As evidenced, internal factors have been predominant in explaining changes in GDP co-movements. External factors have also contributed -generally in a positive manner -to changes in GDP co-movements with the euro area, but to a lesser extent. Notable exceptions are Estonia and Romania where external factors correlations have decreased in context of higher GDP comovements. Exports rather than imports account for a large share of this finding (see Tables 6 and7).

How to date changes in business cycles synchronization? The case of Hungary, Slovakia and Lithuania based on GDP variables.

Are previous findings due to our specific time span choices? Or, are they robust to different time spans? In that purpose, we have run "rolling" correlations. Box A presents briefly the three methods of rolling we used. However, to save space, we report and comment only the results for the reverse recursive analysis and for four CEECs (Hungary, Lithuania, Slovakia and Slovenia) 10 . More precisely, in the cases of Hungary and Slovakia, we are interested in the (approximated) dating of changes in co-movements with the euro area, those two countries representing opposite cases. We also focus on Lithuania which does not appear to participate in the general trend of higher co-movements over time, remaining very poorly correlated with the euro area. Is it due to specific time choices we made? In a benchmark purpose, we report the results for Slovenia which is the frontrunner in terms of business cycles synchronization with the euro area.

The reverse recursive analysis of GDP co-movements -in which the ending date is held fixed, in our case 2006Q4 -shows that Hungary became poorly correlated with the euro area in the course of 2000 (Graph 2). Put differently, for Hungary, all GDP co-movements computed with data of the 2000s give rise to insignificant correlations. That contrasts with the experience of Slovenia, for which the GDP co-movement with the euro area shows quite strong increases in the 2000s, to reach impressive correlations around 0.90. Slovakia makes considerable progress toward GDP synchronization with the euro area in the very beginning of 2001. After that date, all its GDP co-movement with the euro area becomes significantly positive, reaching high values of coefficients. Finally, Lithuania does not follow a trend as positive as Slovakia, remaining poorly correlated with the euro area since the new millennium. Indeed, albeit positive, GDP co-movements of Lithuania with the euro area are insignificant for the 2004-2006 period. Thus, our main finding concerning GDP co-movements with the euro area are robust to different time spans: in the very beginning of the 2000s, Slovakia was synchronizing its GDP with the one of the euro area while Hungary was desynchronizing. Lithuania remains poorly synchronized with the euro area, contrasting in this respect with the two other Baltic States (Estonia and Latvia).

Graph 2: Reverse recursive correlations (ending date fixed: 2006Q4) -0,7 -0,5 -0,3 -0,1 0,1 0,3 0,5 0,7 0,9 1,1 1 9 9 6 q 1 1 9 9 6 q 4 1 9 9 7 q 3 1 9 9 8 q 2 1 9 9 9 q 1 1 9 9 9 q 4 2 0 0 0 q 3 2 0 0 1 q 2 2 0 0 2 q 1 2 0 0 2 q 4 2 0 0 3 q 3 2 0 0 4 q 2 2 0 0 5 q 1 Hungary Slovakia Lithuania Slovenia 10 Results for other CEECs and other "rolling correlations" are available upon request to the author.

Box A

"Rolling correlations" allow to assess the robustness of coefficient correlations to the time span. The "pure rolling method" imposes to choose arbitrarily the length of window, we set to 20 in our case. With a dataset of 44 quarters (i.e. from 1996Q1 to 2006Q4), the first correlation is based on the 20 first quarters (i.e. 1996Q1-2000Q4), the second correlation on the 20 following quarters (i.e. 1996Q2-2001Q1) and so on, finishing with a correlation based on 20 quarters from 2001Q1-2006Q4. This method is a first way to date potential changes in co-movements, with a fixed number of quarters. In our case, 20 quarters correspond to 5 years of (rolling) data, which seems to us reasonable both in terms of "cycle" length and statistically speaking.

The "recursive method" holds the starting date as fixed (i.e 1996Q1 in our case), and the window size grows as the ending date is advanced. With a dataset of 44 quarters starting in 1996Q1 and an initial window of 20 quarters, the first correlation is based on the 20 first quarters (i.e. 1996Q1-2000Q4), the second correlation on the 21 first quarters (i.e. 1996Q1-2001Q1) and so on, finishing with a correlation based on 44 quarters from 1996Q1 to 2006Q4. This method analyses how each new quarter (or, updating of the dataset) affects the coefficient correlation. Considering that most of previous empirical studies have used data from1995/1996 to 2002 to compute correlations, this method allows measuring the "bias" due to "omitting" recent data.

The "reverse recursive method" holds the ending date as fixed (i.e 2006Q4 in our case), and the window size shrinks as the starting date is advanced. With a dataset of 44 quarters, the first correlation is based on 44 quarters (i.e. 1996Q1-2006Q4), the second correlation on 43 quarters excluding the first one (i.e. 1996Q2-2006Q4) and so on. We stop the reverse recursive correlations to 2005Q1 as starting date, which corresponds to a correlation based on 8 quarters. The four or five last correlations we report in Figure A must be thus considered with caution, due to the small numbers of quarters to compute them. This method is another way to date potential changes in co-movements, allowing a focus on the more recent period and to date the "beginning" of the recent period.

Further evidence on shocks affecting CEECs and the euro area

This section is devoted to a SVAR analysis, allowing for a distinction between demand and supply shocks affecting CEECs and the euro area. It complements usefully a simple (cross-country) correlations analysis, which has the drawback to conflate shocks and the responses of the economy to those shocks.

For each CEEC and the euro area, we consider a two-variable VAR, consisting in GDP and prices (i.e. GDP deflators) 11 . Data for GDP deflators are taken from the IMF for CEECs and from Eurostat for the euro area. The data of GDP deflators are missing for Bulgaria and Croatia over a sufficient long time period, so that these two countries are excluded from the SVAR analysis. Since variables are expressed as first differences of the natural logarithm, data have been seasonally adjusted using the X12 procedure where appropriate. In most of cases, the optimal lag length for the VAR came to between 3 and 6 according to LR information criteria, except for Slovenia (1 lag) and Estonia (9 lags). As other information criteria give very different optimal lag lengths for these two countries (generally between 3 and 6), all lags were set to 3 in the interest of standardizing and to conserve degrees of freedom 12 .

11 The reader will refer to [START_REF] Bayoumi | Shocking aspects of European Monetary Unification[END_REF] for a detailed presentation of the methodology and to [START_REF] Fidrmuc | Meta-Analysis of the Business Cycle Correlation between the Euro Area and the CEECs[END_REF] for papers applying this methodology to CEECs and the euro area. 12 We have checked that the lag length does not alter the qualitative results. As no large difference was found, we report here the results with the standardized value of three lags. Note also that in some cases (especially Lithuania), the GDP deflator turns to be an AR(2) process rather than an AR(1) process according to the ADF and PP tests of unit root. For robustness checks, we have then followed [START_REF] Süppel | Comparing economic dynamics in the EU and CEE accession countries[END_REF] in considering that inflation data are trend-stationary processes and adjusting for a long run HP trend (with a smoothing parameter of 1600) before entering the VAR estimation. Qualitative results are no longer altered by the use of de-trended GDP deflators. Tests of unit root are available from the author upon request.

Shocks are identified from the VAR residuals by assuming that demand shocks cannot have a permanent effect on GDP. This assumption is derived from an AD/AS model with sticky prices. The model implies that demand shocks should raise prices in both the short and long run, while supply shocks should lower prices. Since these responses are not imposed by the estimation procedure, they can be thought of as over-identifying restrictions useful in testing whether the shocks that have been identified from their effect on output actually correspond to the demand and supply shocks in the model. In our case, the over-identifying restriction is satisfied for most of countries, meaning that some of the long-term assumptions underlying the model are not completely satisfied in all countries.

Tables 8 and9 report the correlations of supply and demand shocks between CEECs and the euro area, resulting from the SVARs. Correlations of demand shocks are clearly higher than correlations of supply shocks for most of countries. Moreover, we observe an increase in demand shocks correlations over 2002-2006 compared to 1996-2001. Considering the previous findings of higher private consumption and GFCF co-movements between CEECs and the euro area, higher demand shocks correlations are no longer surprising. By contrast, supply shocks of CEECs and the euro area are poorly correlated, whatever the sub-period13 . Nevertheless, some countries like Lithuania, Poland and, especially, the Czech republic do not participate in this general trend of higher demand shocks synchronization with the euro area. For Lithuania, this finding can be easily explained by oil products and CIS markets. For Poland and the Czech republic, this finding is at first look very hard to reconcile with the finding of higher private consumption and GFCF co-movements. Note, however, that monetary and fiscal policies in these two countries may have been used to counter the effect of asymmetrical demand shocks with the euro area or, alternatively, may have been a source of asymmetrical demand shocks. In particular, as allowed by a flexible exchange rate arrangement, monetary activism may have been quite large in Poland and the Czech republic.

Conclusion

In this paper, we have shown how important are the recent years in the business cycles synchronization between CEECs and the euro area. In particular, we point out that over the recent period, internal factors versus external ones have played a growing role in explaining the business cycles synchronization, despite a deeper trade integration between new and old EU members.

Finally, based on an updated empirical evidence, if a high degree of GDP co-movements with the euro area over the recent period is a criterion for adopting the euro in CEECs, the actual EMU membership of Slovenia is making no mistake at all while early EMU membership of Slovakia, Latvia, Estonia and Poland appears quite a good option. But, if we consider that a high degree of demand shocks synchronization with the euro area is a better criterion -assuming that supply shocks are by far more permanent than demand ones, so that monetary policy can adjust the sole (temporary) demand shocks -only Slovenia and Latvia may still participate in EMU14 . Thus, even if Slovakia pass successfully its exam of Maastricht criteria in 2008, such a finding puts out that an early adoption of the euro could be desirable.

Appendix:

Tab.1: Correlation of GDP growth rates with the euro area (1) Instead 1998Q1/2006Q4 and 1998Q1/2001Q4 for respectively the full period and the first sub-period.

*

  Internal factors are defined as private consumption and GFCF while external factors as exports and imports. Cross-external factors refer to imports (resp. exports) of the euro area and exports (resp. imports) of CEECs. For each CEEC, the contribution of internal factors to the change in GDP co-movements with the euro area is computed as the "change in the sum of private consumption and GFCF correlations" to the "change inGDP correlations" between 1996GDP correlations" between /2001GDP correlations" between and 2002GDP correlations" between /2006. And, similarly for the external and cross-external factors. Source: Eurostat; own computations.

  *), (**), (*) stands for significantly different from 0 at 1%, 5 % and 10 % levels, respectively.(1) Instead 1998Q1/2006Q4 and 1998Q1/2001Q4 for respectively the full period and the first sub-period.(2) Instead 2000Q1/2006Q4 and 2000Q1/2001Q4 for respectively the full period and the first sub-period.

Correlation of private consumption growth rates with the euro area

		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	0,30 (**)	0,61 (***)	0,34	-0,27
	Croatia (1)	-0,20	0,16	0,00	-0,16
	Czech republic	-0,24	-0,30	0,46 (**)	0,76
	Estonia	0,22	0,39 (*)	0,72 (***)	0,34
	Hungary	0,44 (***)	0,70 (***)	0,07	-0,63
	Latvia	0,10	0,24	0,81 (***)	0,57
	Lithuania	-0,28 (*)	-0,15	0,01	0,15
	Poland	0,47 (***)	0,36 (*)	0,68 (***)	0,32
	Romania (2)	-0,01	0,13	0,51 (**)	0,39
	Slovakia	-0,28 (*)	-0,47 (**)	0,79 (***)	1,26
	Slovenia	0,43 (***)	0,20	0,89 (***)	0,68
	Unweighted average	0,09	0,17	0,48	0,31
	Tab.2: 1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	0,31 (**)	0,64 (***)	0,50 (**)	-0,14
	Croatia (1)	-0,58 (***)	-0,39	-0,34	0,05
	Czech republic	-0,38 (**)	-0,45 (**)	0,21	0,66
	Estonia	-0,49 (***)	-0,57 (***)	0,18	0,75
	Hungary	-0,08	0,45 (**)	-0,64 (***)	-1,10
	Latvia	-0,35 (**)	-0,30	0,56 (***)	0,86
	Lithuania	-0,36 (**)	-0,06	0,36	0,42
	Poland	0,18	-0,18	0,24	0,42
	Romania (2)	-0,37 (*)	-0,33	0,58 (***)	0,92
	Slovakia	-0,36 (**)	-0,72 (***)	0,26	0,98
	Slovenia	0,21	0,17	0,47 (**)	0,31
	Unweighted average	-0,21	-0,16	0,22	0,37
	Tab.3:

Correlation of gross fixed capital formation growth rates with the euro area

	Tab.4:
		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	0,40 (**)	0,40 (**)	0,59 (***)	0,19
	Croatia (1)	-0,49 (***)	-0,43	-0,20	0,23
	Czech republic	-0,37 (**)	-0,53 (***)	0,21	0,74
	Estonia	-0,20	-0,18	-0,03	0,15
	Hungary	0,06	0,27	-0,35	-0,62
	Latvia	0,09	0,06	0,39	0,33
	Lithuania	-0,28 (*)	-0,31	-0,12	0,20
	Poland	0,56 (***)	0,32	0,92 (***)	0,59
	Romania (2)	0,36 (*)	-0,32	0,64 (***)	0,95
	Slovakia	-0,24	-0,55 (**)	0,55 (**)	1,10
	Slovenia	0,48 (***)	0,38 (*)	0,58 (***)	0,21
	Unweighted average	0,03	-0,08	0,29	0,37
	(1) Instead 1998Q1/2006Q4 and 1998Q1/2001Q4 for respectively the full period and the first sub-period.
	(2) Instead 2000Q1/2006Q4 and 2000Q1/2001Q4 for respectively the full period and the first sub-period.

Correlation of changes in inventories with the euro area (growth rates)

		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	-0,11	-0,11	n.a.	n.a.
	Croatia (1)	-0,06	0,21	-0,12	-0,33
	Czech republic	-0,01	0,01	-0,32	-0,33
	Estonia	0,04	0,06	0,02	-0,03
	Hungary	-0,17	-0,03	-0,25	-0,23
	Latvia	-0,07	-0,09	-0,04	0,05
	Lithuania	-0,19	-0,24	-0,06	0,18
	Poland	0,01	-0,01	0,01	0,02
	Romania (2)	-0,08	-0,63 (***)	0,01	0,64
	Slovakia	-0,02	-0,12	0,10	0,22
	Slovenia	-0,01	-0,01	0,15	0,16
	Unweighted average	-0,06	-0,09	-0,05	0,03
	Tab.5:

Correlation of final consumption of government with the euro area (growth rates)

		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	0,01	0,00	0,12	0,12
	Croatia (1)	0,00	-0,36	0,54 (**)	0,90
	Czech republic	0,29 (***)	0,19	0,38	0,19
	Estonia	0,21	0,26	0,07	-0,19
	Hungary	0,11	0,01	0,16	0,15
	Latvia	-0,23	-0,37 (***)	0,16	0,53
	Lithuania	0,00	0,10	-0,42 (***)	-0,52
	Poland	-0,01	0,07	-0,26	-0,33
	Romania (2)	-0,02	-0,14	-0,05	0,09
	Slovakia	-0,05	-0,21	0,29	0,50
	Slovenia	-0,15	-0,24	0,06	0,30
	Unweighted average	0,01	-0,06	0,10	0,16
	Tab.5

bis: Correlation of fiscal positions with the euro area

		1999Q1/2006Q4	2002Q1/2006Q4	2003Q1/2006Q4	2004Q1/2006Q6
	Bulgaria	-0,17	0,60 ***	0,77* ***	0,79 ***
	Croatia (1)	n.a.	n.a.	n.a.	n.a.
	Czech republic	0,19	0,37	0,48 **	0,14
	Estonia	-0,33 *0 , 3 8 *	0,46 **	0,50 **
	Hungary	0,42 **	-0,14	-0,38	-0,36
	Latvia	-0,39 **	0,34	0,49 **	0,31
	Lithuania	-0,46 ***	0,66 ***	0,76 ***	0,81 ***
	Poland	0,64 ***	0,52 **	0,53 **	0,33
	Romania	n.a.	n.a.	n.a.	n.a.
	Slovakia	-0,73 ***	-0,08	-0,33	-0,62 ***
	Slovenia	-0,37 **	0,73 ***	0,77 ***	0,77 ***
	Unweighted average	-0,13	0,38	0,35	0,30
	(***), (**), (*)

stands for significantly different from 0 at 1%, 5 % and 10 % levels, respectively. Tab.6: Correlation of exports growth rates with the euro area

		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4		Changes in correlation
	Bulgaria	0,41 (***)	0,52 (***)	0,38		-0,13
	Croatia (1)	0,09	0,11	0,14		0,03
	Czech republic	0,61 (***)	0,70	0,74 (***)	0,04
	Estonia	0,74 (***)	0,91 (***)	0,50 (**)	-0,41
	Hungary	0,77 (***)	0,74 (***)	0,75 (****)	0,01
	Latvia	0,28 (*)	0,37 (*)	0,17		-0,20
	Lithuania	0,04	0,11	0,05		-0,06
	Poland	0,31 (**)	0,34	0,47 (**)	0,13
	Romania (2)	0,57 (***)	0,68	0,32		-0,36
	Slovakia	0,10	0,10	0,41	(*)	0,31
	Slovenia	0,70 (***)	0,82	0,82		0,00
	Unweighted average	0,42	0,49	0,43		-0,06
	Tab.7:

Correlation of imports growth rates with the euro area

		1996Q1/2006Q4	1996Q1/2001Q4	2002Q1/2006Q4	Changes in correlation
	Bulgaria	0,38 (**)	0,44 (**)	0,62 (***)	0,18
	Croatia (1)	-0,30 (*)	-0,13	-0,23	-0,10
	Czech republic	0,33 (**)	0,11	0,65 (***)	0,53
	Estonia	0,62 (***)	0,66 (**)	0,75 (***)	0,09
	Hungary	0,79 (***)	0,86 (***)	0,54 (***)	-0,33
	Latvia	-0,09	-0,25	0,57 (***)	0,82
	Lithuania	-0,26 (*)	-0,27	0,05	0,32
	Poland	0,41 (**)	0,30	0,74 (***)	0,44
	Romania (2)	0,68 (***)	0,68 (*)	0,65 (***)	-0,03
	Slovakia	0,10	-0,18	0,72 (***)	0,90
	Slovenia	0,50 (***)	0,54	0,73 (***)	0,19
	Unweighted average	0,29	0,25	0,53	0,27

The Maastricht criteria consist in limits on inflation, interest rate, fiscal deficit and government debt. All these criteria are nominal in their nature. After completing the mandatory two-years in ERM-2, the country may apply for joining EMU based on an exam of its Maastricht criteria over the previous year.

See Fidrmuc and Korhonen (2006) for a meta-analysis based on

empirical studies.

According to[START_REF] Fidrmuc | Meta-Analysis of the Business Cycle Correlation between the Euro Area and the CEECs[END_REF], the SVAR methodology accounts for 18 empirical studies out of 35 aiming to assess the degree of business cycles synchronization between CEECs and the euro area. While some authors may consider monetary shocks alongside (real) demand and supply shocks, the basic SVAR with only real shocks remains predominantly used (i.e. 13 out of 35).

The euro area we consider in this paper consists in twelve members (the eleven pioneers members of 1999 and Greece which joined EMU in 2001), excluding then Slovenia, Malta and Cyprus.

This has been rarely made in previous studies, to the notable exception of[START_REF] Carmignani | The characteristics of Business Cycles in Selected European Emerging Market Economies[END_REF].

Cross-country correlations based on Hodrick-Prescott filters are available upon request to the author.

Such a finding constitutes one of the six major puzzles in international macroeconomics put forward byObsfeld and Rogoff (2000). Indeed, under complete financial markets, cross-country correlations of consumption would tend toward unity even if their GDP exhibit low correlations as a result of idiosyncratic shocks.

Using earlier data,[START_REF] Fidrmuc | The euro goes East. Implications of the 2000-2002 economic slowdown for synchronisation of business cycles between the euro area and CEEs[END_REF] as well as[START_REF] Süppel | Comparing economic dynamics in the EU and CEE accession countries[END_REF] find that correlations of demand shocks are lower than correlations of supply shocks, contrasting with our results. Taking into account the more recent period thus alters considerably the results.

The extent to which changes in monetary conditions can be used to smooth the effect of supply shocks is a lively debated issue in the literature. While supply shocks are viewed as requiring a structural adjustment in which monetary activism has to play no role at all, some authors argue that monetary activism can be yet used in the short run to favour a smooth adjustment towards the new long run equilibrium.


Sarah Guillou
email: sarah.guillou@ofce.sciences-po.fr

Exports and exchange rate: a firm-level investigation *

Keywords: F1, F31, F32, F4

This paper investigates the relation between export behaviour and the exchange rate at firm level. We use a dataset of French manufacturing firms from 1994 to 2004,

to study the sensitivity of firms' export intensity and probability of entering a foreign market, to the exchange rate. This large dataset allows us to differentiate among 21 manufacturing industries. We show that for most industries, the exchange rate has an influence on export entry, but that the effect of changes in the exchange rate on export intensity is fairly neutral. The probability of entering an export market is increased by depreciation. This supports the presence of export sunk costs, which are more easily incurred by firms in periods of exchange rate depreciation. We conclude that currency appreciation is a cause for concern because it increases import penetration implying higher levels of foreign competition for domestic firms.

Introduction

The strength of the euro is increasing and, unlike the situation in the United States, growth in Europe in recent years has been substantially export led. But if the euro continues to rise and if exports get more expensive, growth will be more difficult to sustain. Exchange rate parity is a rather sensitive subject in international affairs. The euro's appreciation is worrying European leaders while the weakness of the Asian currencies is a source of annoyance to the United States and, more generally, Asian firms' competitors. Exchange rate policy is regarded as unfair policy. In concrete terms, exchange rate overvaluation is seen as being responsible for artificially decreasing competitiveness and thus cutting export trade and growth. Economic arguments over the role of the exchange rate on trade are deeply rooted in the past. But research in this field since the 1970s has focused on understanding the failures of exchange rate policy. From the J curve [START_REF] Masera | The j curve: Uk experience after the 1967 devaluation[END_REF] to the hysteresis model [START_REF] Dixit | Entry and exit decisions under uncertainty[END_REF], scholars have tried to find explanation for the rigidity between trade flows and changes in the exchange rate.

At the same time, the collapse of the Bretton Woods system has led the theoretical research to focus on the effect of increased volatility of the exchange rate. Exchange rate volatility is undoubtedly non-neutral [START_REF] Cheong | The effects of exchange rate volatility on price competitiveness and trade volumes in the uk: A disaggregated approach[END_REF], but there are no consensual theoretical or empirical results related to its impact on trade. [START_REF] Mckenzie | The impact of exchange rate volatility on international trade flows[END_REF], states that "despite the best efforts of economists, a basic paradox as to the impact of exchange rate volatility on trade flows remains unresolved at both the theoretical and empirical level".

Since then, exchange rate volatility has undoubtedly decreased for European members. This is not a far cry from claiming that empirical difficulties have once again motivated new theoretical investigation. Examination of exporters' behaviour is one example. Initially, such investigations dealt with the response of relative prices to the nominal exchange rate. A seminal paper introducing the role of the exchange rate on exporters' behaviour, in a model of industrial organization, is [START_REF] Dornbusch | Exchange rate and prices[END_REF]. An exogenous exchange rate overvaluation decreases the marginal costs (in local currency) of foreign firms relative to local firms. [START_REF] Dornbusch | Exchange rate and prices[END_REF] showed that in a Cournot competition, domestic currency appreciation creates a cost disadvantage that increases the market share of foreign firms to the detriment of domestic firms. A currency appreciation decreases domestic export volumes. But a change in the exchange rate will seldom be completely passed through to local prices. Empirical studies thoroughly document this fact. It can be mainly seen in exporters' price discrimination among market destinations [START_REF] Krugman | Pricing to market when the exchange rate changes[END_REF][START_REF] Knetter | Price discrimination by u.s. and german exporters[END_REF]. This price-to-market behaviour also induces price rigidities to exchange rate changes. One of the main conclusions of the extensive literature on exchange rate pass-through (see for a survey [START_REF] Goldberg | Goods prices and exchange rates: what have we learned?[END_REF] is that market structure and other industry characteristics have an important effect on firms' pricing behaviour in international markets.

The second issue within these investigations is the response of exports to exchange rate changes, regardless of whether relative prices have changed or not. Research on the microfoundations of export supply response [START_REF] Roberts | An empirical model of sunk costs and the decision to export[END_REF] addresses the question of exchange rate changes, relying on the trade hysteresis literature [START_REF] Baldwin | Persistent trade effects of large exchange rate shocks[END_REF][START_REF] Baldwin | Hysteresis in trade[END_REF]. This literature starts with entry models in which sunk costs have to be incurred to enable entry to the foreign market. These sunk costs mean that exchange rate variation will affect the decision to enter, by changing the level of expected future profits from exporting. These models question the influence of exchange rate volatility.

Because entry costs are likely sunk, exchange rate volatility strongly affects the decision to enter. Acting as a vector of uncertainty, exchange rate volatility can induce a wait-andsee attitude (see [START_REF] Darby | The impact of exchange rate uncertainty on the level of investment[END_REF]. In [START_REF] Dixit | Entry and exit decisions under uncertainty[END_REF]'s model, uncertainty can deter entry even if the firm is risk neutral. Based on the theory of option pricing, a firm not currently exporting is seen as owning an option to enter the foreign market in the future. In this case, an increase in exchange rate volatility could postpone entry because it raises the value of maintaining the option. The greater the volatility, the longer the firms already in the export market will wait to exit, and the longer it will be before non-exporting firms can enter. Thus, the higher the volatility, the wider will be the band of exchange rate changes during which no entries and no exits will occur.

Empirical studies have focused on proofs of the existence of sunk costs [START_REF] Roberts | The decision to export in columbia:an empirical model of entry with sunk costs[END_REF]. In terms of exchange rate volatility, the empirical results are far from consensual (see for a survey, [START_REF] Cote | Exchange rate volatility and trade[END_REF][START_REF] Mckenzie | The impact of exchange rate volatility on international trade flows[END_REF]. [START_REF] Taglioni | Exchange rate volatility as a barrier to trade: new methodologies and recent evidence[END_REF] asserts that even new methodologies using time series "tend in general to indicate that there is not a systematic link between exchange rate variability and trade flows".

In both cases, a promising axis of empirical research is based on firm level data. These allow theoretical models of firm behaviour to be compared with firm data and take account of firm heterogeneity by rejecting the simplifying hypothesis of a single representative firm.

In fact, both exporters and non-exporters coexist in an industry.

Our paper is positioned within the literature that relies on theoretical models of exporters' behaviour and firm level data. It deals specifically with the relation between the exchange rate and exporter behaviour. There are a few recent empirical studies that focus on this relation and use firm data. [START_REF] Campa | Exchange rates and trade: how important is hysteresis in trade?[END_REF] study considers the period 1990-1997. He finds that exchange rate volatility has no impact on Spanish exporters and that the depreciation of the domestic currency increases the domestic export volume. [START_REF] Bernard | Why some firms exports[END_REF], focusing on participation in exporting, find that, for US plants, depreciation increases this participation. To our knowledge, the present study is the first analysis using French firm data, that deals with the question of exchange rate sensitivity. It is also the first study with sufficient observations to enable a focus on market entry (first time export participation) and differentiation among industries.

Our objective is to investigate how export intensity and export penetration by French firms are sensitive to the level and volatility of exchange rates, exploiting a database of large French manufacturing firms covering the period 1994-2004. Through this investigation we hope to contribute to the ongoing debate on the role of exchange rate policy. We find that having exported yesterday, increases the probability of exporting today. In addition, we show that, for most industries, the exchange rate has an influence on export entry whereas the effect of changes in the exchange rate on export intensity is rather neutral. The probability of entering the export market is increased by depreciation. Our results show the presence of sunk costs associated with export, which are more likely to be incurred when there is a depreciation in the domestic currency.

The next section presents the theoretical model behind our estimation. Section 3 deals with the econometric specification. Section 4 presents the data and describes the construction of the main variables. Section 5 presents the empirical results and section 6 concludes.

Export and irreversible costs

We follow [START_REF] Campa | Exchange rates and trade: how important is hysteresis in trade?[END_REF] who estimates a dynamic discrete choice firm model. This model is based on the irreversible cost associated with the entry in to a foreign market. Let us assume a French exporter that potentially can produce for both the domestic market and the foreign market. The share of production that will be exported is γ i . But this exporter can also decide not to export, in that case γ i = 0. The more γ i tends to one, the more the firm's export intensity will increase. The expected revenue of firm i given the level of information, Ω it is:

V it (Ω it ) = max I it ,Q it ,γ it E t   ∞ j=t δ j-t R ij (I ij , γ ij , Q it ) |Ω it   (1)
I it is a qualitative variable that takes the value 1 if firm i exports at time t; γ it is the share of the production that is exported at time t, δ is the one-period discount factor. R it is the expected net revenue of the firm depending on its export behaviour (exporting or not and if so, how much). Let us assume fixed costs of entry in the export market (Fi) and fixed costs of exit (Gi) 1 , then the expected net revenue is:

R it (I it , γ it , Q it ) = π d it ((1 -γ it )Q it ) + I it [π x it (γ it , e it ) -F i (1 -I it-1 )] -G i I it-1 (1 -I it )
where π d it , π x it are the gross profits from domestic production and from exporting, both depending on γ it , the share of exports in production. The gross profit from exporting depends on the exchange rate, e it , defined as the amount of French currency per foreign currency. The expected net revenues depends on the export behaviour: on whether the firm exported in the last period and is exporting in the current period. If the firm was an exporter in the last period (I it-1 = 1) and is still exporting

(R it (I it , γ it , Q it ) = π d it ((1 -γ it )Q it )+π x it (γ it , e it ); if the firm exited, then R it (I it , γ it , Q it ) = π d it ((1 -γ it )Q it )- G i .
If the firm is a prime exporter (i.e. decides to export for the first time in the current period), then

R it (I it , γ it , Q it ) = π d it ((1 -γ it )Q it ) + π x it (γ it , e it ) -F i .
From (1) and using Bellman's equation, the firm's behaviour will be such that:

1 We are assuming here that the entry and exit fixed costs are time invariant. But European market integration and decreasing trade costs with all partners may have changed these costs during the period (1994)(1995)(1996)(1997)(1998). Let us also suppose that these costs are paid once and for all. But the entry cost may have a decreasing value from the period of first entry to further periods. Finally, these costs are related to the firm and represent an aggregation of the fixed costs spent for each destination market of the exporting firm.

V it (Ω it ) = max γ it ,I it ,Q it [R it (I it , γ it ) + δE t (V it+1 (Ω it+1 )) |I it ]
(2)

The first order condition of this problem gives the following export participation decision rule. The firm i will decide to export when:

π d it ((1 -γ it )Q it ) + π x it (γ it , e it ) + δ [E t [V it+1 (Ω it+1 ) |I it = 1] -E t [V it+1 (Ω it+1 ) |I it = 0]] ≥ F i -(F i + G i ) I it-1
(3)

The firm's entry and exit decisions depend on the current value of the exchange rate and on its conditional distribution. The current value of the exchange rate affects the expected profits from exporting in the current period. The exchange rate volatility affects the decision to enter. Whereas the export volume is only affected by the exchange rate level, both volatility and level of exchange rate affect the decision to enter or to remain in the export market. Exchange rate volatility affects the decision to enter or to exit as it is an element of uncertainty that influences future revenue flows. So the conditional distribution of the exchange rate will only have an impact on the extensive margin. It

does not have any impact on the level of current exports of existing exporters.

Econometric specification

Our objective is to estimate the sensitivity of export intensity2 and export status to the level and volatility of the exchange rate. As defined previously:

I it = 1 if [R it (Q it , I it , γ it ) |I it = 1] ≥ 0 0 otherwise
Where,

R * it = π d it ((1 -γ it )Q it ) + π x it (γ it , e it ) + δ [E t [V it+1 (Ω it+1 ) |I it = 1] -E t [V it+1 (Ω it+1 ) |I it = 0]] -F i -(F i + G i ) I it-1
First, we estimate the export intensity γ it of exporter i at time t.

γ it = α 0 +α 1 X it +υ it if [R * it (Q it , I it , γ it ) |I it = 1] ≥ 0 0 if I it = 0
X it are observable exogenous variables, α 0 and α 1 are the parameters, and υ it is the error term. X it is a vector of the two types of variables: firm characteristics and industry variables. Firm characteristics include: the previous year's export intensity, size of the firm (employment), firm's labour productivity, labour intensity (ratio of wages on sales), and capital intensity (ratio of investment on sales). The coefficient on previous export intensity should exhibit a positive and significant sign, indicating the presence of export sunk costs. Size and productivity should have a positive effect on export intensity. Labour intensity and capital intensity should have positive or non-significant signs. The industry variables are the effective exchange rate and the import rate. An increase in the effective exchange rate means depreciation. It is expected to increase export market share and thus export intensity. Changes in export intensity reveal changes in export volume among other things. Indeed, on the one hand, an increase in export intensity may be the consequence of a lag in the cycle of growth between the domestic and the foreign market. If the growth in domestic demand is low vis-à-vis the growth in foreign demand, then this could increase export intensity because the share of value added that satisfied foreign demand increases, but export volume might stay constant. On the other hand, an increase in export volume could occur without any increase in export intensity if the scale of production has increased. In any case, the sensitivity of export intensity to the exchange rate will reveal a price effect. Depreciation of the domestic currency is expected to increase export intensity3 .We should also bear in mind that export intensity is measured here, regardless of the destination market. Therefore, an increase in export intensity could result from entry into a new foreign market. The industry import rate has an unexpected effect. It will undoubtedly increase competition within the domestic market. Then, it will either induce domestic firms to become more productive (or to exit) and therefore allow them to start exporting; or it will increase the focus on the domestic market, postponing export.

Second, we estimate the probability of entering a foreign market for the first time.

This requires the definition of a variable for export entry:

I it = 1 if R * it (Q it , I it , γ it ) |I it = 1 ≥ 0 0 otherwise and I it-1 = 0
The latent variable is:

R it = β 1 Z it + β 2 σ it I it + ε it Pr(I it = 1/I it-1 = 0) = Pr (R it ≥ 0|I it = 1, I it-1 = 0) = f (Z it , σ it ) (4)
Z it is a vector of the variables that explain the firm's export decision. These variables are the same as the X it vector except for past export intensity. We add exchange rate volatility to estimate the role of exchange rate uncertainty on export status. Past export status (one year earlier) is included when the focus is not on a prime exporter. We differentiate export status between being an exporter in year t or becoming an exporter in year t. The first status makes no hypothesis about the status for the year before. The second assumes that the firm was never an exporter before year t.

First we estimate export intensity equation using a Heckman model [START_REF] Heckman | Sample selection bias as a specification error[END_REF] in which export status (being or not an exporter) is used as the selected equation. The

Heckman two-step selection model avoids the selection bias induced by excluding nonexporters from the export intensity equation.

Second, we estimate the role of the level and the volatility of the exchange rate on the probability of entering the export market by applying a dynamic discrete choice model.

Because we assume that it is the sum of a permanent, firm-specific component and a white noise component: it = α it + ω it , we estimate equation ( 4) by a random-effects probit model.

Data

This paper uses firm-level data on French manufacturing production from 1994 to 2004.

Data are from the annual survey of French manufacturing firms implemented by the French Ministry of Industry. This survey covers all firms with more than 19 employees, belonging to the manufacturing sector 4 . It represents annually around 17% of French exporters, and 68% of French export 5 .

Table 1 shows the percentage of firms that exported in 1998, by industry and firm size. As expected, the larger the size of the firm, the higher is the percentage of exporters.

Only 65.5% of small firms (20-50 employees) are exporters, whereas 97.7% of firms with more than 2,000 employees are engaged in export. The concentrations of exporters vary depending on the industry. Pharmaceuticals, chemicals, aircraft and spacecraft, and the metal industries include a large number of exporters, more than 80% of firms. Export intensity also differs by industry and by firm size. The average export intensity of small firms (20-50 employees) is 11% whereas it is 35% for large firms (1,000-2,000 employees).

Contrary to [START_REF] Campa | Exchange rates and trade: how important is hysteresis in trade?[END_REF], who studied Spanish firms, there is a positive relationship 4 All firms belong to "D", minus the food processing and tobacco industries, i.e. from 17 to 39 of the ISIC 2digit rev-3 level. We opt for a two-year standard deviation of the first difference of the logarithm of the quarterly exchange rate between the destination country and France. [START_REF] Darby | The impact of exchange rate uncertainty on the level of investment[END_REF], [START_REF] Tenreyro | On the trade impact of nominal exchange rate volatility[END_REF] uses a similar measure. This is a measure of short term volatility. We also control for whether the estimations are sensitive to our measure by substituting this measure of volatility by a simple mean of the quarterly coefficients of variation 6 . Both measures of bilateral volatility are aggregated using trade shares as weights to obtain what is referred to as the "industry effective volatility". This ensures that the measure of volatility is, as far as possible, linked to the exchange rate risk perceived by the firm. However, we need to account for simultaneous causality problems. As pointed by Dell'Ariccia (1998), all institutional processes aimed at cutting (or suppressing) exchange rate volatility between the trade partners in a growing integrated trade area, can induce a negative correlation between volatility and trade although no causality has come into play. The effective exchange rate volatility of the French currency undoubtedly diminished during the second half of the 1990s and has fallen dramatically since the introduction of the euro.

Each industry and each firm within it, is confronted by specific changes in the exchange rate. In addition, the variability between industries is larger after than before 1999 (see [1994][1995][1996][1997][1998][1999][2000], French Ministry of Industry, IFS for exchange rate index.

Results

Presence of sunk costs and sensitivity of export intensity

Table 3 presents the results of the Heckman selection model estimation on export intensity where the sample selection equation is the export status. The first column gives the results for the probability of being an exporter and the second column the results for export intensity. The first row shows the estimation results for the pooled sample. Subsequent rows show the results for firms pooled by industry.

We find that the intensity of past exporting activity increases the current intensity of exports, and that past export status increases the current propensity to export. These results support the presence of sunk costs: as expected, past export behaviour is a significant predictor of current export behaviour. The size and labour productivity of a firm has the expected significant influence. An increase in size (or in productivity) implies an increase in export intensity while bigger size (or greater productivity) raises the probability of being an exporter. The influence of size and productivity has a more significant impact on export status than on export intensity. In terms of export intensity, no sensitivity to a change in the exchange rate was found. Changes in exchange rates are expected to impact foreign market share as a result of changes in local prices. However, we did not find any market share effect. This result must be linked to firms' pricing behaviour, which prevents changes in local prices (see [START_REF] Goldberg | Goods prices and exchange rates: what have we learned?[END_REF]. However, export intensity is strongly determined by past export intensity. This hysteresis in export intensity is coherent with the hypothesis of sunk costs. We find a low positive significance only for the Paper Products industry, and a rather surprising negative significant coefficient for the Motor Vehicles industry. This latter result may be associated with the international organisation of the production from this industry, and the fact that most products are imported before assembly. Domestic depreciation inflates the import costs and this effect reduces the firms' profitability and could act to decrease the volume of exports and export intensity. However, with the exception of these two cases, our results show inertia in export intensity to the exchange rate level. For export status, six industries have a positive and significant coefficient (at the 5% level) meaning that a depreciation increases the probability of being an exporter. Remember, that an increase in the effective exchange rate means a depreciation of the French Franc. Firms in most industries other than these six seem to be neutral vis-à-vis the level of the exchange rate, with the exception of Railroad Equipment where the coefficient is significantly negative. Numerous industries show a significant and positive sign on import rate. This means that the more the industry imports, the more its firms will be exporters. In these industries, import rate is an indication of the degree of openness, i.e. the degree of globalisation of their production. The positive sign indicates a positive correlation between an industry's export and import rates. Exchange rate volatility has a weak but significant and positive effect for all industries but two. This sign indicates that export firms are also risk takers and that an increase in volatility rather creates profit opportunities for firms.

Export intensity is not sensitive to the level of the exchange rate, indicated by the low variability in export volumes. Export intensity is mainly determined by firms' past export intensity and thus mainly by firms' characteristics. At the same time, in a third of manufacturing industries, we can see that the probability of being an exporter rises in the case of depreciation. This result suggests that changes in the exchange rate may affect the decision to become exporter. In order to investigate this point, we need to focus on first time exporters.

Exchange rate and entry to the export market

Only first time exporters and non-exporters are included in our sample.In this case, export propensity is the probability of becoming an exporter. All other variables are the same as in the previous estimations. Table 4 shows the influence of each variable on the probability to enter the foreign market for the first time. The results presented are for the whole pooled sample of manufacturing firms and for every industry.

Size and productivity have the expected sign in the pooled regression. An increase in size or/and an increase in productivity, increase the probability to enter a foreign market for the first time. In terms of size, this is true for 13 out of 21 manufacturing industries. For productivity, this is true for 15 out of 21 industries (with the exception of a negative sign for Other Non-metallic Products). For industries where the coefficients for size and productivity are non-significant this may indicate that the supply characteristics are outweighed by the demand dynamics in the decision to enter a foreign market. The level of the exchange rate has a positive impact on the export propensity of French firms. This is true for the pooled sample and for the 11 individual industries. Also, for most manufacturing industries, depreciation increases the probability to become an exporter.

Contrary to the probability of continuing to be an exporter, the probability of becoming a first time exporter is positively linked to depreciation in most industries. Calculating the marginal change from the estimated coefficients leads to the conclusion that a marginal change in the exchange rate implies an increase in the probability of becoming an exporter by 12 percentage points. This reinforces the hypothesis of the existence of sunk costs in exporting. These sunk costs are more likely to be incurred when the exchange rate Industry characteristics seem to play a significant role in this relationship and further research on this area is necessary. . Volatility has a positive impact on the probability that a firm will enter the export market. Fifteen industries show this positive influence of volatility. This supports the idea that French firms are risk takers. Volatility seems to be perceived by firms as creating opportunities for profits. This is not consistent with the Dixit (1989)'s model of hysteresis in which increasing volatility induces firms to postpone entry. Our result, however, is sensitive to the measure of volatility used. If we apply the coefficient of variation for past exchange rates, only half of the industries in our sample have a significant positive coefficient. In other words, volatility in this case has no influence on half of the manufacturing industries. Nevertheless, whatever the specification, the sign is never negative. Exchange rate depreciation acts as a disguised export policy, aimed at encouraging firms to enter foreign markets.

The import rate is negatively significant for the whole sample. By industry, the sign is mostly negative: eight industries display a negative significant sign. For these industries, an increase in the industry import rate decreases the probability to enter the foreign market. An import rate rise implies stronger foreign competition in the domestic market.

This stronger competition discourages any attempt to conquer new market. French firms will likely choose to focus on the domestic market to protect their market share. They will be less motivated to export. More generally, an increase in an industry's import rate is an indication of the openness of the industry, but also the decline of the industry, notably a cut in its contribution to domestic production. There are two different explanations for the propensity to continue to be an exporter or to become an exporter for the first time. One is related to the exchange rate level and the other to the import rate. While the level of the exchange rate cannot explain export status, it is an indicator of future market penetration.

Depreciation makes entry to a foreign market easier. While being an exporter is positively related to the industry import rate, becoming an exporter is negatively correlated with it. The former correlation demonstrates that globalisation results in greater openness; the latter that this process increases competition.

We next introduce interaction variables to consider the effects of the import rate, the institution of the EMU (European Monetary Union), and firm size on the sensitivity of the probability to export to the exchange rate. Three dummies are defined for : (i) a high industry import rate -more than 47%; (ii) years after 1999 (date of creation of EMU); and (iii) large sized firms -more than 249 employees. We constructed three interaction variables that multiply the effective exchange rate by the dummy. The regressions (I to IV) introduce these three variables first successively and then simultaneously. The results show significant interaction coefficients. -14785.37 -14771.13 -14794.59 -14758.64 Firms from industries with a high import rate are more sensitive to changes in the exchange rate. A high import rate is allied to strong foreign competition. Strong competition leads to greater vulnerability to exchange rate shocks. EMU reduced the sensitivity to exchange rate variations of the probability of entry. This is an example of heteroskedasticity. There is less sensitivity to low levels of exchange rate changes, than to high one.

Likelihood

EMU has resulted in lower effective exchange rate changes. Thus, the probability of entering the export market has been less sensitive to exchange rate changes since 1999 (EMU).

This could also result from the lack of enough time since 1999. Reducing the sample period to 1999-2004 results in a much small number of new exporters. Larger firms are more sensitive to the exchange rate. Larger firms are more internationalised, i.e. more export oriented, but also more multi-located. In other words, greater international exposure renders firms more sensitive to exchange rate changes.

Conclusion

The main result of this paper is that changes in the exchange rate have an impact on entry into exporting. Firms are incited to enter foreign markets when their domestic currency depreciates. This result provides strong support for the presence of sunk costs, which likely explain the high hysteresis in firms' export intensity. Changes in the exchange rate do not have much effect on a firm's export intensity, which is mainly determined by past export intensity. Thus, we found no market share effect of exchange rate changes. At firm level, a change in the exchange rate induces a supply adjustment. Because entry to and exit from foreign markets are costly, depreciation, if of sufficient magnitude, enables firms to incur entry costs. We can conclude, then, that generally depreciation accelerates entry into foreign markets. There are, however, some industries that appear indifferent to exchange rate changes. In these industries, entry cost is not one of the variables determining entry into a foreign market. // Overall, it can be said that appreciation of the euro appears problematic for some industries, first, because it increases domestic competition and reinforces the positions of non-European firms, and second, because it reduces the rate of participation of European firms in world exports.

Import

Industry import rates are calculated from production and imports using ISIC-rev3 classification.

Appendix C Complementary Results

Table 1 :

 1 Percentage of firms that export in 1998 by firm size (N = number of employees) Source :French Annual enterprises survey (1998), French Ministry of Industry, ISIC-rev3 classificationbetween export intensity and the size of the firm. Lastly, some industries are more export oriented. Chemicals, office machinery, shipbuilding and aircraft industries have average export intensities of over 30%.During the 10 years from 1994, it can be seen from Table2that the share of exporters in French manufacturing industries has remained quite stable with the highest levels in 2000 (74.5%) and 2004. This shows that manufacturing industries have always been open to foreign markets. At the same time, average exports expressed in euros, have shown a steady increase. Compared to non-exporters, exporters have larger turnovers and higher productivity. The average sales of exporters are five to six times greater than the average sales of non-exporters'. The average labour productivity of exporters is more than 1.5 times more than that of non exporters.Information on the destinations of individual firm's exports is not available. The export structure by destination is at industry level, extracted from the OECD trade database, and is used to weight individual bilateral exchange rates. An effective exchange rate by industry at the ISIC 2-digit level is used which leads to the hypothesis that all firms within

	ISIC-rev3 20-49 50-99 100-249 250-499 500-999 1000-1999 2000¡N All size Exp. Intensity (allsize)	17-19 63.6 70.5 81 93.3 96.3 100 73.6 100	20 54.9 67.1 80.9 76.9 100 100 61.5 19	21-22 62.8 73 78.8 83.9 86.7 86.7 68.5 11 100	23 81.3 60 100 33.3 60 40 63.9 14 100	2423 89.7 95.1 93.9 100 93.5 100 92.5 22 100	24ex2423 85.8 91.4 93.5 96.9 95.6 100 90.7 33 100	25 76.4 86.2 88.5 83.8 97.4 94.1 82.3 18 100	26 50.1 66.3 72.9 92.6 93.1 88.9 61.2 21 100	271+2731 87 96.7 96 88.2 100 88.9 92.7 32 100	272+2732 83.8 91.2 92.5 86.4 90.9 100 88.5 25 100	28 56.8 71.1 85.8 96.4 100 100 64.3 100	29 75.3 87.8 95 93.7 96 100 83 27 100	30 61.8 100 90.9 100 100 100 81.3 41 100	31 64.4 77.2 83.8 92 97.6 100 75.7 24 100	32 62.3 68.8 70.6 86.8 100 100 69.6 27 92.3	33 75.8 83.9 92.5 97.8 93.8 100 82.2 30 100	34 66 85.5 87.3 94.6 93.3 100 79.1 23 91.7	351 65.2 70.6 78.9 50 100 100 72 43 100	352+359 74.3 83.3 93.8 100 100 100 84 29 100	353 84.8 64.3 86.7 87.5 100 100 86 33 100	36-37 77.5 87.5 93.7 93.5 100 100 82.5 20 100	17-39 65.5 77.8 86.3 90.8 95 95.9 97.7	10.9 16.4 23.1 29.7 32.5 35.3 40.4
	Industry	Textiles, textile products, leather and footwear	Wood and products of wood and cork	Pulp, paper products, printing & publishing	Coke, refined petroleum products & nuclear fuel	Pharmaceuticals	Chemicals excl. pharmaceuticals	Rubber and plastics products	Other non-metallic mineral products	Iron and steel	Non-ferrous metals	Fabricated metal products, except mach. & equip.	Machinery and equipment, n.e.c.	Office, accounting and computing machinery	Electrical machinery and apparatus, nec	Radio, television and communication equipment	Medical, precision and optical instruments	Motor vehicles, trailers and semi-trailers	Building and repairing of ships and boats	Railroad equipment & transport equipment n.e.c.	Aircraft and spacecraft	Manufacturing nec recycling	All industries	Export intensity (%)

an industry export to the same destinations. From 1994 to 2000, the bilateral exchange rates for the 23 first French partners are used (see the appendix on data). According to the industry, these 23 partners account for between 80% and 95% of French exports. An increase of this effective exchange rate represents a depreciation of the French Franc (euro since 1999) against its main partners. Exchange rate volatility is not directly observable.

Table 2 :

 2 Main statistics on French exporters

	1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004	Exporting firms Number 5956 5859 5877 5922 5622 5546 5453 5718 5619 5420 5150	Avg. sales (Me) 40308.3 42254.21 26761.27 28762.81 29806.32 31823.89 33223.29 35117.88 39773.69 41173.25 39807.22	9477.11 10185.21 11589.01 12393.55 13105.38 15251.79 15724.14 15460.23 15989.29 16965.61 Avg. exports(M e) 8627.98	73.90% 74.40% 73.40% 73.00% 72.70% 74.20% 74.30% 74.50% 73.40% 73.60% % firms 73.30%	23.50% 23.70% 20.00% 20.60% 21.30% 21.80% 22.20% 22.70% 23.10% 23.30% Avg.Int. exp. 19.40%	93.90% 93.90% 92.60% 93.00% 92.10% 93.40% 92.40% 93.20% 93.00% 93.80% % total sales 92.80%	89.10% 89.30% 88.20% 88.30% 87.70% 88.70% 86.00% 86.70% 86.70% 89.00% % employ. 88.20%	251.5 237 151 176.9 170.2 192.8 186.9 205.4 190.5 195.1 Productivity 138.4	245 254 154 162 183 200 210 243 248 242 Exports 141	(M e)	106.2 107.6 110.3 110 105.5 106.4 104.4 100 100.4 101.7 Effective Exch. rate 106.3	15334 14976 16207 15926 15806 16170 16012 15961 15759 15648 Non-exporting firms Number 16376	7360.59 7930.48 6331.77 6254.3 7244.41 6747.68 8191.03 8449.08 8528.22 7264.18 Avg. sales 5703.72	128.3 132.8 94.6 91.3 155.9 101.1 145.8 114.3 118.8 124.4 Productivity 88.5	20754 20126 22066 21803 21728 21792 21558 21414 21477 21267 Total Number 22332	Source : French Annual enterprises survey (

Table 3 :

 3 Export intensity -Heckman Model 1994[START_REF] Campa | Exchange rates and trade: how important is hysteresis in trade?[END_REF]

	To Be exporter EXPORT Intensity	I it-1 Size Prod ER Vola Import γ it-1 Size Prod Obs. ER Import	-0.05 147968 All industries 17-37 9.57*** 0.33*** 0.45*** -0.69** -0.09*** 0.39*** 0.80*** 0.05*** 0.11*** -0.13	21528 -0.13* Textiles 17-19 4.63*** 0.29*** 0.44*** 3.47*** 0.58*** 0.75*** 0.80*** 0.06*** 0.17*** 0.41	4323 -1.08 Wood 20 4.69*** 0.29*** 0.40*** 4.77 0.28*** 1.77* 0.82*** -0.06 0 1.78	16655 -0.93* Paper products 21-22 4.98*** 0.33*** 0.26*** -1.33 0.23*** 3.53*** 0.82*** 0.07*** 0.04 1.73*	410 -0.11 Petroleum prod. 23 17.78 0.15 1.16 3.55 2.50* 4.66 0.96*** 0.01 -0.08 0.39	1864 -0.04 Pharmaceuticals 2423 10.72** 0.22*** 0.03 6.16 1.25* 1.01 0.85*** 0.02 0.07 0.88	6497 -0.11 Chemicals excl. 24ex2423 5.85*** 0.20*** 0.47*** 1.48 0.66*** 3.77*** 0.86*** -0.01 -0.05 -0.01	10178 0.32 Rubber plastics 25 4.97*** 0.20*** 0.43*** 5.02*** 0.21*** 1.40* 0.75*** 0.09*** 0.12 0.26	7081 0.01 Oth. non-metallic prod. 26 6.03*** 0.40*** 0.25** -0.44 0.47*** 2.80*** 0.89*** 0.03 0.04 -1.11	1607 0.14 Iron and steel 271+2731 5.93*** 0.59*** -0.04 5.82*** 0.22 -0.41 0.80*** -0.01 0.28 -2.6	1392 -0.22 Non-ferr. metals 272+2732 5.40*** 0.21*** 0.47** 4.37*** 1.06*** -3.02* 0.78*** 0.10*** 0.16 -0.83	30588 0.18 Fabricated metal prod. 28 5.11*** 0.42*** 0.51*** -0.99 0.43*** 2.69*** 0.74*** 0.13*** 0.23*** -0.62	15565 -0.47 Machinery & equip. 29 4.94*** 0.43*** 0.23*** -1.92 0.96*** 3.63*** 0.76*** 0.04** 0.18*** 0.31	502 0.52 Office & comput. mach. 30 5.52** 0.32** 0.86** 12.21* 1.34*** 1.28 0.79*** 0.02 -0.01 -2.12	5106 -0.08 Electrical machinery 31 5.10*** 0.31*** 0.30** 3.32* 0.62*** 1.24* 0.79*** 0.04* 0.14** -0.31	3708 -0.06 Radio, TV & comm. 32 4.77*** 0.18*** 0.69*** 5.21*** 0.83*** 1.02 0.81*** 0.03* 0.19** -0.82	6177 0.28 Medical & optical inst. 33 4.72*** 0.25*** 0.58*** 1.49 0.97*** 0.13** -0.47 1.41* 0.79*** 0.06***	4290 -0.80* 0.02 0.06 -2.2*** Motor vehicles 34 5.22*** 0.30*** 0.19 5.04*** 0.24*** 1.03 0.82***	657 -0.04 1.28 0.15** 0.52** Ships and boats 351 4.99*** 0.30*** 0.12 0.4 0.99*** 0.73** 0.64***	664 -0.75 -0.36 0.05 0.29* Railroad equipm. 352+359 4.33*** 0.50*** 0.47 -12.6** 0.15 5.05*** 0.82***	704 -0.15 0.2 0.05 0.06 Aircraft & spacecraft 353 5.90*** 0.24** 0.82* -1.78 1.80** 0.26 0.62***	8472 0.29 -0.71 0.02 0.16*** Manuf. nec; recycling36-37 5.37*** 0.39*** 0.19* 1.86 1.03*** 1.04** 0.84***	Significativity: * < 0.1; * * < 0.05; * * * < 0.01. Robust standard errors available upon requests.	All models include industry dummies.

  depreciates. Depreciation offers a cost advantage over foreign firms already insiders. Cost of entry is incurred more easily. The sign on Aircraft and Spacecraft is significant and negative sign , except where variable for volatility changes (see annex A.3). The entry to export of firms from some industries is non-sensitive to the exchange rate. These are: Pharmaceuticals, Chemicals, Iron and Steel, Non-ferrous Metals, Office and Computing Machinery; Motor Vehicles, Ships and Boats; Railroad Equipment, Manufacturing nec.

	and Recycling.

Table 4 :

 4 Random effect Probit Model of export participation[1994][1995][1996][1997][1998][1999][2000][2001][2002][2003][2004]

	Size Prod ER Vola IC Import Obs. MaxLik	All industries 17-37 0.64*** [0.03] 1.35*** [0.03] 5.80*** [0.82] 2.03*** [0.08] 0.13*** [0.01] -1.62*** [0.28] 47716 -14799.1	Textiles 17-19 0.58*** [0.07] 2.22*** [0.07] 9.91*** [2.07] 1.73*** [0.41] 0.05* [0.03] -2.07** [0.82] 7036 -2006.39	Wood 20 0.68*** [0.19] 1.86*** [0.26] 37.01*** [12.90] 0.72 [0.59] 0.13* [0.08] -9.22*** [3.34] 1793 -635.43	Paper products 21-22 0.38*** [0.08] 1.39*** [0.08] 18.79*** [3.68] 1.71*** [0.34] 0.10*** [0.04] -4.68*** [1.73] 5914 -1918.13	Petroleum prod. 23 -0.29 [0.35] 0.61 [0.61] -18.02 [24.16] 4.55** [2.31] -0.16 [0.21] 6.36 [4.68] 131 -35.96	Pharmaceuticals 2423 0.06 [0.12] -0.08 [0.20] 11.02 [8.79] 3.50** [1.53] 0.25*** [0.09] 3.13 [2.36] 467 -104.65	Chemicals excl. 24ex2423 0.54*** [0.10] 0.02 [0.10] 11.99** [4.67] 2.32** [0.94] -0.04 [0.06] -2.26 [5.16] 1557 -356.23	Rubber plastics 25 0.21** [0.10] 0.64*** [0.15] 14.04*** [5.38] 1.29*** [0.43] 0.17*** [0.05] -6.35** [2.67] 2719 -781.29	Oth. non-metallic prod. 26 1.14*** [0.13] -0.90*** [0.15] 9.01*** [3.46] 0.43 [0.61] 0.1 [0.06] -6.63*** [2.31] 3135 -943.2	Iron and steel 271+2731 0.18 [0.17] 0.42 [0.34] 22.31 [13.96] 2.08*** [0.66] -0.1 [0.14] -1.1 [2.90] 347 -65.38	Non-ferrous metals 272+2732 0.3 [0.25] 0.59* [0.31] 14.67 [9.55] 2.58*** [0.84] 0.01 [0.14] -0.48 [5.13] 346 -72.99	Fabricated metal prod. 28 1.29*** [0.09] 1.36*** [0.10] 11.40*** [2.43] 1.38*** [0.43] 0.16*** [0.03] -6.24*** [2.00] 10654 -3621.25	4281 -1223.23 Machinery & equip. 29 0.43*** [0.08] 1.30*** [0.13] 11.91*** [2.71] 1.54*** [0.49] 0.21*** [0.04] -6.52*** [1.86]	181 -48.14 -2.56 [2.86] Office & computing mach. 30 0.49 [0.31] 0.5 [0.33] 15.28 [14.22] 0.56 [1.37] 0.32* [0.18]	1562 -483.72 -1.77 [1.91] Electrical machinery 31 0.90*** [0.14] 1.57*** [0.20] 14.80*** [5.12] 2.31*** [0.72] 0.09 [0.08]	1419 -495.08 -5.70* [3.28] Radio, TV & comm. 32 0.43*** [0.14] 1.52*** [0.21] 14.62*** [4.93] 2.67*** [0.57] 0.37*** [0.09]	1851 -484.91 -3.14 [2.32] Medical & optical inst. 33 1.14*** [0.17] 1.95*** [0.17] 10.14** [4.88] 3.30*** [0.49] 0.15** [0.06]	1138 -313.91 -4.3 [3.00] Motor vehicles 34 0.31*** [0.10] 1.34*** [0.23] 10.23 [9.00] 1.22*** [0.39] 0.25*** [0.07]	253 -82.48 -0.53 [1.36] Ships and boats 351 0.34 [0.37] 1.46*** [0.48] 3.43 [8.64] 1.32 [2.68] 0.39** [0.18]	149 -23.71 3.17 [2.24] Railroad equipment 352+359 0.54 [0.52] 0.72 [0.52] -12.94 [17.02] 0.89 [2.41] 0.06 [0.25]	196 -54.44 3.12 [3.33] Aircraft and spacecraft 353 0.15 [0.26] 1.60*** [0.56] -7.83** [3.40] 0.9 [2.27] 0.54** [0.24]	2587 -785.11 0.87 [1.45] Manuf. nec; recycling36-37 0.73*** [0.11] 1.15*** [0.12] 1.83 [3.29] 3.62*** [0.59] 0.03 [0.05]	Industry geometric effective exchange rate and volatility with constant scheme.All models include industry dummies.	Significativity based on the test of Student: * < 0.1; * * < 0.05; * * * < 0.01	Standard errors in brackets.

Table 5 :

 5 Interaction effect coefficients

		I	II	III	IV
	size	0.64***	0.64***	0.70***	0.69***
	prod	1.35***	1.35***	1.36***	1.37***
	ER	6.14***	3.98***	5.74***	4.35***
	vola	2.00***	2.31***	2.02***	2.27***
	ic	0.14***	0.14***	0.14***	0.14***
	import	-1.62***	-1.90***	-1.64***	-1.90***
	ER*high imp	1.39***			1.06***
	ER*EMU		-0.99***		-0.91***
	ER*Large firms			0.60***	0.59***
	Observations	47716	47716	47716	47716

Table A1 :

 A1 Statistics on Volatility

		vola	cv
		Bef 99 Aft 99 Bef 99 Aft 99
	Textiles 17-19	0.0131 0.0074	0	0.0171
	Wood 20	0.0096 0.0029	0.024	0.0072
	Paper products 21-22	0.0107 0.0044 0.0283 0.0104
	Petroleum prod. 23	0.0136 0.0087 0.0348 0.0202
	Pharmaceuticals 2423	0.0123 0.0062	0.032	0.0148
	Chemicals excl. 24ex2423	0.0123	0.006	0.0328 0.0144
	Rubber plastics 25	0.0105	0.004	0.0277 0.0095
	Oth. non-metallic prod. 26	0.0121 0.0058	0.032	0.0141
	Iron and steel 271+2731	0.0112 0.0042 0.0302 0.0099
	Non-ferrous metals 272+2732 0.0124 0.0059	0.034	0.0137
	Fabricated metal prod. 28	0.0112 0.0052 0.0293 0.0127
	Machinery & equip. 29	0.0136 0.0074 0.0364 0.0181
	Office & computing mach. 30 0.0115	0.005	0.0303 0.0122
	Electrical machinery 31	0.0124 0.0059 0.0334 0.0143
	Radio, TV & comm. 32	0.0141 0.0076 0.0378 0.0183
	Medical & optical inst. 33	0.0141 0.0085 0.0374 0.0207
	Motor vehicles 34	0.0105 0.0036 0.0286 0.0087
	Ships and boats 351	0.0135	0.008	0.0379 0.0199
	Railroad equipment 352+359	0.0122 0.0064 0.0343	0.015
	Aircraft and spacecraft 353	0.0194	0.016	0.0522 0.0409
	Manuf. nec; recycling 36-37	0.0137 0.0085 0.0355 0.0203
	Mean	0.0126 0.0065 0.0319 0.0158
	Sd	0.002	0.0027 0.0092	0.007
	Cv=Sd/mean	0.1619 0.4201 0.2895 0.4438

  Table generated by Excel2LaTeX from sheet 'Feuil1'

Export volumes are difficult to obtain because of the lack of export deflators. Not only are unit values unsatisfactory price measures but they are based on product classifications meaning that an aggregation process would be necessary to obtain prices at industry level. This aggregation process increases the noise in the unit value. Thus, we concentrate on export intensity.

A currency appreciation is not always the sign of increased growth. Although appreciation can be linked to high interest rates and high rates of growth, numerous historical cases show the reverse to be the case. European economies are the most recent famous examples.

Appendix A Data source

Firm data are taken from the Annual Enterprises survey of French manufacturing firms with more than 19 employees. These data are observed from 1994 to 2004.

Industry import rates and industry export weights are calculated from the STAN Trade Database using the ISIC rev3 classification. We consider the export share by industry of the top 24 French partners: Austria, Belgium, Canada, Denmark, Finland, Germany, Greece, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States, China, Taïwan, Hong Kong, Singapore. Export shares are

Appendix B Description of variables Appendix B.1 Firms'variables

Size: the size of the firm given by its employment Prod: the firm's labour productivity X it : the export intensity level for the previous year LI: labour intensity (ratio of wages on sales) IC: capital intensity (ratio of investment on sales).

Appendix B.2 Industry variables

ER: effective exchange rate

The industry effective exchange rate is calculated at the 2-digit industrial classification level (ISIC rev3). For 1994 to 2000, we use the export shares of the top 24 French partners.

We retain constant weights in order to reinforce the exogeneity of export behaviour to the industry exchange rate. The effective exchange rate, then, is a geometric mean of each bilateral exchange rate weighted by the export share. We consider both a constant (1995) and a variable weighting scheme. We also calculate an effective exchange rate based on an arithmetic mean.

Vola: effective exchange rate volatility

Exchange rate variability between country j and France in year t, is denoted by σ jt , is:

where e jtq is the bilateral exchange rate of the quarter q relative to year t for the French Franc (the euro since 1999) and the currency of country j. An alternative measure of volatility was also used: the average of the quarterly coefficients of variation for the previous eight quarters. This means that a coefficient of variation was calculated for each quarter (over the past eight quarters). Yearly volatility is the mean of the four quarterly coefficients of variations.