Financial Constraints and Firm Export Behavior
Flora Bellone*

Patrick Musso†

Lionel Nesta‡

Stefano Schiavo§

This version: March 2009

Abstract
The paper analyzes the link between financial constraints and firm export
behavior exploiting a rich dataset of French firms. We use data from two main
sources. The first (Enquête Annuelle d’Entreprises – EAE) is an annual survey
conducted by the Ministry of Industry, which gathers balance sheet
information for all manufacturing firms with at least 20 employees. The
second source of information is the DIANE database published by Bureau
van Dijk, which collects data on over 1 million (French) firms. DIANE
contains many financial variables absent from the EAE survey. Merging thetwo datasets yields around 170,000 firm/year observations, stemming from an
unbalanced panel of over 25,000 manufacturing enterprises followed over theperiod 1993 – 2005. The actual number of observations used in the empiricalanalysis varies according to the specific econometric exercise. Our main
finding is that firms enjoying better financial health are more likely to become
exporters. The result contrasts with the previous empirical literature whichfound evidence that export participation improves firm financial health, but
not that export starters display any ex-ante financial advantage. On thecontrary, we find that financial constraints act as a barrier to export
participation. Better access to external financial resources increases theprobability to start exporting and also shortens the time before firms decide toserve foreign customers. This finding has important policy implications as itsuggests that, in presence of financial markets imperfections, public
intervention can be called for to help efficient but financially constrained firms
to overcome the sunk entry costs into export markets and expand theiractivities abroad.
Keywords: Export; Firm heterogeneity; Financial constraints; Sunk costs
JEL Classification: F14; G32; L25; D92*

University of Nice-Sophia Antipolis, GREDEG UMR n° 6227, e-mail: flora.bellone@gredeg.cnrs.fr
University of Savoie, IREGE (IMUS) and CERAM Business School, e-mail: patrick.musso@univsavoie.fr‡ Observatoire Français des Conjonctures Economiques, Département de Recherche sur l’Innovation et laConcurrence (OFCE-DRIC), e-mail: lionel.nesta@ofce.sciences-po.fr§ Corresponding author. University of Trento, Department of Economics, and OFCE-DRIC, e-mail:stefano.schiavo@unitn.it†

1. INTRODUCTION
The paper analyzes the link between financial factors and firm export behavior exploiting alarge dataset on French manufacturing firms. There are several reasons making this a relevant
issue. With the rise of the ‘global economy’ export performance is increasingly perceived as a
key aspect of economic performance, both for firms and for the entire macroeconomic
outlook. In the meantime, academics have been paying increasing attention to firm levelstudies. Wider access to firm level data, greater computational capabilities, as well as
theoretical advances that depart from the representative agent framework have led economists
to recognize that aggregate dynamics are the result of microeconomic behavior. Thus, a clear
grasp of the latter becomes crucial to understand the former and to design appropriate
policies.
In this paper we refer to export behavior in terms of both export participation and export
intensity. A vast empirical literature documents a substantial heterogeneity across firms withrespect to foreign markets penetration (ISGEP, 2008). This has mostly been explained interms of systematic differences between firms in productivity levels. We claim that financial
constraints represent an additional source of heterogeneity that can help to account fordifferent in export behavior across firms.
Our theoretical background is casted in terms of the recent ‘new-new’ trade theory(Melitz, 2003) which emphasizes both firm heterogeneity and the relevance of sunk entry
costs into export markets.1 Once extended to allow for imperfect capital markets, these models
show that financial variables can play a key role in determining firm export behavior (Chaney,
2005; Manova, 2006). Indeed, the existence of sunk entry costs into export markets brings
about the question of the financing of such expenditures that, by their very nature, are notmatched by contemporaneous revenues. In the presence of financial market imperfections, it
may well be —and this is the main research question from which we start— that only thosefirms that can successfully overcome this financial problem become exporters. In fact, thiswould be consistent with the evidence of internationalized firms outperforming non exporters
in several dimensions as shown in the large literature triggered by Bernard and Jensen (1995).
Rather than supporting this prior, the scant empirical evidence on the topic suggests thatexporting improves firm access to financial markets either by reducing informational
1

The assumption that entry into foreign markets involves large sunk costs is not a novelty in the trade
literature: see for instance Baldwin (1988); Roberts and Tybout (1997). This assumption is supported
by an expanding empirical literature (see, among others, Bernard and Wagner, 2001; Das et al., 2001;
Tybout, 2001; Bellone et al., 2008b).

asymmetries or by reducing exposure to demand-side shocks through diversification (GaneshKumar et al., 2001; Campa and Shaver, 2002; Greenaway et al.,2007). In what follows we
present an evaluation of the self-selection and ex-post effects based on a large panel of French
manufacturing firms. Our contribution is twofold. First, we propose a new way to measure thedegree of financial constraint (based on the multivariate index proposed by Musso andSchiavo, 2008), which we believe is superior to existing methodologies. Second, we shedlight on the role played by access to external financial resources in shaping firm export
behavior. In so doing, we do not limit ourselves to export participation, but we also look atexport intensity.
We can summarize our main findings as follows. First, firms starting to export display asignificant ex-ante financial advantage compared to their non exporting counterparts. This isconsistent with the idea that limited access to external financial funds may prevent firms fromselling their products abroad. Second, we do not find significant improvement in the financial
health of firms entering into export markets. Hence, in our sample foreign markets penetration
is not associated with easier access to external financial resources, contrary to the evidence
presented in other works (see for instance Greeneway et al., 2007). When we dig deeper into
the relation between financial factors and the decision to start exporting, we find that better
access to financial markets increases the probability of firm internationalization, and also
shortens the time before that happens. Finally, among the subsample of export starters, there
is a negative relationship between export intensity and financial health. Considering theformer as a proxy for the number of destinations served, our results suggest that entering
simultaneously into many different markets entails larger sunk costs and results in a
deterioration of a firm financial position.
The rest of the paper is organized as follows. We next present an overview of the literature
on financial constraints and firm export behavior. Section 3 presents the data, discusses theshortcomings of usual strategies employed to measure financial constraints, and illustrates themethodology adopted here. In Section 4, we test the two hypotheses that less constrained
firms self-select into exporting, and that selling abroad improves firm financial health. We
then look more in details at the role played by financial variables in shaping the decision toexport: these results are discussed in Section 5. Section 6 concludes and draws some policy
implications.

2. A GLANCE AT THE EXISTING LITERATURE
In presence of imperfect capital markets, one can figure out at least two reasons why
exporting firms should be less financially constrained than non exporting firms.
First, if firms have to incur large sunk costs to enter into export markets, then enterprises
unable to secure enough funds may have difficulties to reach foreign customers. This implies
that only less constrained firms will be able to start exporting: such an idea is formalized by
Chaney (2005) which adds liquidity constraints to a model of international trade withheterogeneous firms (in the spirit of Melitz, 2003). In fact, the new-new trade theory
postulates that a large part of trade barriers faced by firms take the form of fixed costs to be
paid up-front. The empirical literature documents significant hysteresis effects associated withfirm export participation and interprets this as signaling the relevance of sunk entry costs: seefor instance Roberts and Tybout (1997) for Colombia, Bernard and Wagner (2001) for
Germany, Campa (2004) for Spain, Bernard and Jensen (2004) for the US. Das et al. (2001)
estimate a structural model to quantify sunk costs and conclude that entry costs into export aresubstantial. In the business literature, Moini (2007) reports results form a survey among US
non exporters, where firms claim their primary obstacle to initiate an export program is thepresence of high up-front costs.
Second, the very fact of exporting could improve firm access to external financial funds.
Again there are different candidate explanations for such an effect. Exporting firms should in
principle enjoy more stable cash flows, as they benefit from international diversification of
their sales. Hence, under the assumption that international business cycles are only
imperfectly correlated, exporting reduces vulnerability to demand-side shocks. This is theargument put forward by Campa and Shaver (2002) and Bridges and Guariglia (2008).
Alternatively, selling in international markets can be considered as a sign of efficiency andcompetitiveness by domestic investors. In a context of information asymmetries —which lie
at the heart of financial markets imperfections— exporting would thus represent a clear signal
sent by the firm to external investors. Since only the best firms export —as we know very
well by the large body of empirical literature triggered by Bernard and Jensen (1995), and as
demonstrated theoretically by Melitz (2003) — then exporting represents by itself a sign ofefficiency and a costless way for creditors to assess the potential profitability of aninvestment. Ganesh-Kumar et al. (2001) find that this kind of mechanism is especiallyrelevant in an emerging market such India, characterized by low institutional quality. Finally,
exporting is likely to open up access to international financial markets as well, at least thosepertaining to the destination countries. In anything, foreign exchanges revenues represent a
better collateral to access external funds in foreign financial markets. Once again this channel
probably applies more directly to emerging economies, as postulated by Tornell andWestermann (2003).2
Empirically, Campa and Shaver (2002) show that investment is less sensitive to cash flow
for the group of always exporters compared to the group of never exporters. Since in presenceof perfect capital markets investment and cash flow should not be correlated, investment-cash
flow sensitivity is often regarded as a measure of financial constraints (more on this below).
Also, when the two authors consider firms that move in and out from export markets, theyfind these are more constrained when they do not sell abroad. Hence, they conclude thatexporting can help firms to reduce their financial constraints. One possible weakness of thepaper lies in the fact that export intensity has no role in the play. In fact, if the diversificationand the signaling channels were actually at work, one would expect a positive correlation
between the export to sales ratio and its ability to reduce financial constraints. Yet, Campaand Shaver fail to find such a relationship.
Two recent papers provide further evidence backing the idea that exporting exerts a
positive effect on firm financial health. Working with a large panel of UK manufacturingfirms Greeneway et al. (2007) look for a causal nexus between the two variables, andconclude that causality runs from export to financial health. In other words, they find noevidence in favor of the hypothesis of less constrained firms self-selecting into export
activities, but rather strong evidence of a beneficial effect of the latter on financial health.3 In
particular, they find no significant difference in the average liquidity (or leverage) ratio ofexport starters and never exporting firms. On the contrary, when comparing continuous
exporters and starters, they find the former to enjoy a better average financial health over the
sample period. Hence, they conclude that exporting does improve firm financial status, sinceparticipating to export for longer periods makes enterprises more liquid and less leveraged.
Bridges and Guariglia (2008) focus on survival among UK firms. More specifically, theylook at the interrelations between global engagement (of which export is just one possible
manifestation), financial health, and survival. They find that lower collateral and higher
2

The relevance of the institutional context is witnessed by a recent work by Espanol (2006) who findexporting firms in Argentina more financially constrained than their competitors only serving thedomestic market. This can be explained by the appreciation of the local currency in the early 1990s,
which resulted in a profit squeeze for exporters, and weakened their balance sheets.
3
We will discuss the issues related to the measurement of financial constrained in Section 3 below. For
the moment, it suffices to say that Greeneway et al. (2007) use the liquidity ratio and the leverage ratio
to proxy for financial constraints.

leverage do result in higher failure probabilities, but only for purely domestic firms. Theyinterpret this as evidence that international activities shield firms from financial constraints or,
to put it in the terminology used so far, that internationalization is beneficial from a financial
point of view.
Despite this body of literature, we claim that the issue is not fully settled yet. We base thisstatement on different considerations. First, the way financial constraints are identified andmeasured remains largely debated. As discussed below, the usefulness of investment cash
flow sensitivity as a measure of financial constraints is increasingly challenged and recent
theoretical works cast doubts also on other widespread proxies. Second, the role played byexport intensity has been largely disregarded so far and remains to be determined. Last, theeconometric specifications used in the literature appear not always consistent with the statedgoal of testing the relevance of self-selection into export markets and of the existence of abeneficial effect of internationalization on firm financial health.
3. DATA AND METHODOLOGY
We use data from two main sources. Both of them collect information on French firms,
though their coverage is somehow different. The first (Enquête Annuelle d’Entreprises –EAE) is an annual survey that gathers balance sheets information for all manufacturing firms
with at least 20 employees.4 The second source of information is the DIANE databasepublished by Bureau van Dijk, which collects data on over 1 million French firms. It provides
us with many financial variables absent from the EAE survey. Merging the two datasets yields
around 170,000 firm/year observations, stemming from an unbalanced panel of over 25,000
manufacturing enterprises followed over the period 1993 – 2005.
The actual number of observations used in the empirical analysis varies according to thespecific econometric exercise. When testing for self-selection effects or ex post benefits, we
restrict our attention to export starters and never exporting firms exclusively, reducing thenumber of firms to 5,700 (900 when we use models with a longer lag structure, see Section 4).
The duration analysis (Section 5a) also focuses on never exporters and starters only, but theabsence of any lag in the econometric model grants us with a dataset of 12,000 firms. On thecontrary, our analysis of the decision to export (Section 5b) pools all firm types together and4

The survey is conducted by the French Ministry of Industry. The surveyed unit is the legal (not the
productive) unit, which means that we are dealing with firm-level data. To investigate the role offinancial constraints on growth and survival, firm, rather than plant level data seem indeed appropriate.

therefore exploits the entire dataset: there the number of observations ranges between 19,800
and 22,000.
a. Measuring financial constraints
The way financial constraints are measured is a very sensitive issue in the literatureinvestigating the link between financial factors and firm behavior. Theory offers only limited
guidance in this domain, so that a clear-cut consensus has still to emerge. Under perfectcapital markets, internal and external sources of financial funds should be perfectly
substitutable (Modigliani and Miller, 1958), so that the availability of internal funds should
not affect investment decisions. Yet, when a standard investment equation is augmented withcash flow availability, the fit of the equation improves. The most common proxy for financial
constraints is thus the sensitivity of investment to cash flow. This methodology builds on
Fazzari et al. (1988) who first define firms as financially constrained or unconstrained basedon their dividend payout ratio, then show that likely constrained firms (low dividend payout)
display higher investment-cash flow sensitivity. A number of subsequent studies findsupporting evidence using different variables to identify constrained firms (see for instance
Bond and Meghir, 1994; Gilchrist and Himmelberg, 1995; Chirinko and Schaller, 1995).
On the contrary, Devereux and Schiantarelli (1990) find that larger firms (less likely to beconstrained) exhibit a higher cash flow coefficient in the regression equation, even after
controlling for sector heterogeneity. But it is only with the work by Kaplan and Zingales(1997) that the usefulness of investment-cash flow sensitivity as a measure of financial
constraint has been definitely questioned. Since then, other authors have reported evidence ofa negative relation between investment-cash flow sensitivity and financial constraints (forinstance Kadapakkam et al., 1998; Cleary, 2006).
Alternative strategies consist of simply classifying firms according to various proxies of
informational asymmetries (as these represent the main source of financial markets
imperfections). Hence, variables such as size, age, dividend policy, membership in a group or
conglomerate, existence of bond rating, and concentration of ownership are used to capture
ways to cope with imperfect information, which hinders access to capital markets (see for
instance Devereux and Schiantarelli, 1990; Hoshi et al., 1991; Bond and Meghir 1994;
Gilchrist and Himmelberg, 1995; Chirinko and Schaller, 1995; Cleary, 2006). Other papers(e.g. Becchetti and Trovato, 2002) use survey data where firms give a self-assessment of theirdifficulty to obtain external financial funds.

The major weakness of these strategies —as already noted by Hubbard (1998) — is thatmost of the criteria tend to be time invariant whereas one can imagine that firms switch
between being constrained or unconstrained depending on overall credit conditions,investment opportunities and idiosyncratic shocks. As a further potential problem, we add that
all the abovementioned works rely on a unidimensional definition of financial constraint, i.e.
they assume that a single variable can effectively identify the existence of a constraint, which
is viewed as a binary phenomenon either in place or not. Notable exceptions are the works by
Cleary (1999), Lamont et al. (2001) and Whited and Wu (2006). The first paper derives afinancial score by estimating the probability of a firm reducing its dividend payments (viewed
as a sign of financial constraints) conditional on a set of variables that are observable also inthe case of unlisted firms. Lamont et al. (2001) build a multivariate index by collapsing into asingle measure five variables weighted using regression coefficients taken from Kaplan andZingales (1997). The main problem here rests with the need to extrapolate results derived
from a small sample of US firms and apply them to a larger and different population.5 Basedon a structural model, Whited and Wu (2006) use the shadow price of capital to proxy for
financial constraints.
In the paper, we experiment different measures of financial constraints. The first two are
the liquidity ratio and the leverage ratio as employed by Greeneway et al. (2007).6 We find
two main shortcomings in these measures. First, they only capture one dimension of access tofinancial markets: a firm may be liquid but nonetheless present a bad financial situation; on
the other hand, strong fundamentals may compensate for a temporary shortage of liquid
assets. Second, both ratios may suffer from some endogeneity. In other words, there are noclear-cut theoretical priors on the relation between either liquidity or leverage and financial
constraints. While liquidity is generally regarded a sign of financial health, firms may also beforced to withhold cash by the fact that they are unable to access external funds. In fact, arecent theoretical contribution by Almeida et al. (2004) shows that financially constrained
firms tend to hoard cash, so that liquidity would be associated with financial constraints, notlack thereof. In a similar vein, a high leverage, while signaling potential dangers, suggests

5

Furthermore, one of the variables needed to compute the index is Tobin’s Q, whose use as a proxy forinvestment opportunities has often been criticized.
6
The liquidity ratio is defined as a firm’s current assets minus its short-term debt over total assets; theleverage ratio as a firm’s short-term debt over current assets.

also that the firm has enjoyed, at least in the recent past, wide access to external financial
funds. Hence, one could argue that highly leveraged firms are not financially constrained.7
To account for these potential problems, we build two other measures of financial healthaccording to the methodology first proposed by Musso and Schiavo (2008). They exploit
information coming from seven variables: size (total assets), profitability (return on total
assets), liquidity (current asset over current liabilities), cash flow generating ability8, solvency(own funds over total liabilities), trade credit over total assets, and repaying ability (financial
debt over cash flow).9
For each variable, we scale each firm/year observation for the corresponding 2-digit
NACE sector average and then assign to it a number corresponding to the quintiles of the
distribution in which it falls.10 The resulting information for each of the seven variables (anumber ranging from 1 to 5) is then collapsed into a single index in two alternative ways: (i) asimple sum of the seven numbers (Score A); (ii) a count of the number of variables for which
the firm/year lies in the first or second quintiles (Score B).11 In both cases the index is thenrescaled to lie on a common 1–10 range.
[Insert Table 1 here]
The correlations between the four measures of financial constraints are presented in Table 1.
Both the Pearson’s and the Spearman’s correlation coefficients are reported, respectively
below and above the main diagonal of the correlation matrix. Leverage and liquidity arestrongly negatively correlated: more liquid firms are also less leveraged, meaning that these
two measures of financial health go hand in hand. Something similar happens for the two
multivariate scores: irrespective of the way information is combined firms are ranked in avery similar order in terms of access to external financial resources. This results in a7

A further problem is that leverage and liquidity appear as the financial variables best discriminating
between exporting and non exporting firms in the sample analyzed by Greeneway et al. (2007).
Therefore, one runs the risk of ending-up with some sort of a built-in relation between these two
financial variables and export status (see Greeneway et al., 2005, for details on the choice of thefinancial variables).
8This is the maximum amount of resources that a firm can devote to self-financing, and corresponds to
the French capacité d’autofinancement.
9
They are selected on the basis of their performance in existing studies, and their perceived importancein determining ease of access to external financial funds.
10
Sectoral averages are subtracted to account for industry-specific differences in financial variables.
Furthermore, to limit the effect of outliers we trim observations lying in the top and bottom 0.5% of
the distribution for each the seven variables.
11
We have tried also other ways to combine the information, with identical results. Additional details
are available upon request.

Spearman’s ρ correlation of 0.90, while Pearson’s correlation coefficient reaches 0.91. Hence,
Table 1 suggests that the two ratios, and the two scores provide very similar information. On
the other hand, measuring financial constraints by means of a ratio or of a multivariate index
provides us with a different picture of the phenomenon at stake. In what follows we willconcentrate on the liquidity ratio and on Score A only: both measures are increasing in
financial health (contrary to leverage), which simplifies the discussion. Results are
qualitatively unchanged if one uses the leverage ratio and Score B.12
b. Firm productivity
In the following empirical analysis we will often use measures of total factor productivity(TFP) to control for the existing heterogeneity among firms. TFP is computed using the socalled multilateral productivity index first introduced by Caves et al. (1982) and extended by
Good et al. (1997). This methodology consists of computing the TFP index for firm i at time t
as follows:

N



1
1
∑ ( yτ − yτ −1 ) − ∑ (Snit + Snt )(xnit − xnt ) + ∑∑ (Snτ + Snτ −1 )(xnτ − xnτ −1 ) (1)t

TFPit = yit − yt +

τ =2

 n =1 2

tNτ = 2 n =12where Yit denotes the real gross output of firm i at time t using the set of N inputs Xnit, where
input X is alternatively capital stocks (K), labor in terms of hours worked (L) and intermediate
inputs (M). Snit is the cost share of input Xnit in the total cost.13 Subscripts τ and n are indicesfor time and inputs, respectively, upper bars denote sample means, and small letters stand forthe logs of the variables (e.g. yit = ln Yit ). This index makes the comparison between any two
firm-year observations possible because each firm’s inputs and outputs are calculated as
deviations from a reference firm. The reference firm is a hypothetical firm that varies acrossindustries with outputs and inputs computed as the geometric means of outputs and inputs
over all observations and input cost-based shares computed as an arithmetic mean of costshares over all observations.14 This non parametric measure of relative productivity has been
12

This second set of results is not reported but remains available upon request.
See Bellon et al. (2008a) for more details on the method and a full description of the variables.
14
Firms are allocated to one of the following 14 two-digit industries: Clothing and footwear; Printing
and Publishing; Pharmaceuticals; House equipment and furnishings; Automobile; Transportation
Machinery; Machinery and Mechanical equipment; Electrical and electronic equipment; Mineralindustry; Textile; Wood and paper; Chemicals; Metallurgy, Iron and Steel; Electric and Electronic
components.
13

popularized in the export-productivity literature by the contributions of Aw et al. (2000), andDelgado et al. (2002).
4. EXPORT AND FINANCE: SELF-SELECTION OR EX-POST BENEFIT?
We start our econometric analysis by explicitly testing the two hypotheses mentioned above,
namely that less constrained firms self-select into export, and the possibility that exporting
improves financial health.
a. Descriptive statistics
Table 2 presents descriptive statistics for the whole sample and also for different types offirms. We classify firms according to their export status separating those which export
throughout the sample period (Continuous Exporters), those not exporting initially but
entering foreign markets between 1993 and 2005 (Export Starters), and those always serving
the domestic market only (Never Exporters).
Consistently with the large empirical literature on export and performance (see Bernard etal., 2007, for a recent overview) we find that exporters tend to be larger and more productive,
as well as to pay higher wages. Similarly, exporting firms appear more liquid and display
easier access to external financial funds as measured by Score A. Export starters lie somewhat
in the middle of the two groups. The last column of the Table reports a F-test for equality ofmeans across the three groups. The F-statistics are always larger than the 1% critical values,
thus rejecting the null hypothesis of equal means across the different types of firms.
[Insert Table 2 here]
On average, continuous exporters are double the size of non exporting firms in terms of
employees, they pay salaries that are 17% higher, and are 33% more liquid. The difference
between starters and never exporters are much lower and in terms of productivity the equalityof means cannot be rejected.
b. The ex-ante financial advantage of future exporters
We start by comparing ex-ante financial health for exporters and non exporters. This tells us
whether future exporters were less financially constrained than their non exporting
counterparts even before entering foreign markets. The comparison is performed with firms
belonging to the same industry and sharing similar characteristics in terms of size andefficiency. The econometric specification is adapted from the literature on export andperformance (Bernard and Wagner, 1997; Bernard and Jensen, 1999), where this kind of
empirical exercises are routinely performed. We focus our attention only on non exporting
firms and export starters, and compare their financial health 1 and 3 years before the lattergroup begins to export. The resulting sample is comprised of 5,727 firms when we lag
observations one year (t-1), and 2169 firms when we lag observations three years (t-3).
Hence, t is the year of entry into foreign markets (in the case of export starters), while we set
it equal to the median year for never exporters (a similar solution is adopted in Bellone et al.,2008b; ISGEP, 2008). Specifically, we estimate:

FIN i , t − s = α + EXPit + γΖ i ,t − s + ε it(2)

where FIN is either Score A or the liquidity ratio, EXP is the dummy for export status, and Z avector of controls that comprises Size (captured by the log of Employment, measured in termsof total hours worked), productivity (TFP), and a set of industry-year dummies. It must beemphasized that equation (2) does not test for a causal relationship. Rather, it allows us toevaluate the strength of the pre-entry premium —i.e. to see to what extent firms that export intime t were already less financially constrained 1 and 3 years before entering foreign
markets— by means of a simple t-test on the significance of the β coefficient. Results arepresented in Table 3.
[Insert Table 3 here]
When access to financial resources is measured using of Score A, the coefficient of the export
dummy is positive and significantly different from zero both in t-1 and in t-3. Although thepoint estimate for the β coefficient is larger in t-3, we cannot reject the hypothesis of the twobeing equal, so that the difference in the estimated β in t-1 and t-3 is not statisticallysignificant. The better financial health of future exporters is less pronounced in terms ofliquidity: exporters appear more liquid one year before entry, but not 3 years before.
As discussed above, we claim that liquidity captures just one aspect of firm ability toaccess external financial resources, and suffers from potential endogeneity since higher
liquidity may signal the need for the firm to hoard cash due to its difficulties in accessing
external financial funds. Therefore we give more credit to results obtained using Score A.

Overall, Table 3 suggests that firms deciding to enter into foreign markets enjoy better
financial health ex-ante.
Equation (2) is estimated using never exporters and export starters alone; moreover, we
include both successful exporters (i.e. those firms that keep exporting ever since their entry
into foreign markets) and firms that stop exporting after a few years. This reduces potential
sample selection biases and reinforces our results since it works against the hypothesis of selfselection.
Our conclusions differ sharply from those reported in Greeneway et al. (2007): suchdifference can be imputed both to the introduction of a new way to measure financial
constraints, and to the econometric methodology. In particular, while Greeneway et al. (2007)
look at the average liquidity of firms prior to entry into foreign markers, here we look at
different points in time as normally done in the literature on export and productivity (Bernard
and Jensen, 1999).
c. Detecting ex-post effects
The results from the previous Section suggest that less constrained firms tend to become
exporters. This does not rule out the possibility that internationalization further boosts firm
financial health. Here we look at the extent to which this happens while disregarding thespecific reason behind the phenomenon: this is to say that we do not ask whether it is adiversification rather than a signaling effect that matter.
Once again we stick to an empirical specification taken from Bernard and Jensen (1999).
The idea is very simple and consists in running a regression of the change in financial
variables on initial export status and initial firm characteristics. From the previous Section weknow that exporters enjoy better access to external funds: if export participation is beneficial,then we should observe a differential in the way financial variables move after exporting
firms have started to serve foreign markets. We focus on a subsample made of newlyinternationalized firms (export starters) and purely domestic enterprises, and we estimate thefollowing equation:∆ % FIN i , t + s / t + q = α + βEXPi , t + γΖ i , t + ε i ,t(3)where ∆% FIN i ,t + s / t + q identifies the growth rate of the financial variable between time t+s andt+q computed as log differences. As before, t is the first year of export for starters, whereas itidentifies the median year of observation in case of never exporters. The coefficient β
represents the increase in the growth rate of exporting firm financial health relative to nonexporters. If export is truly beneficial then we expect β to be significantly different from zero.

[Insert Table 4 here]
As highlighted by estimated coefficients in Table 4 we do not find any evidence to support theidea that exporting improves firm access to external financial funds. We look at the growth offinancial variables over a very short time span, namely between the first year of entry and thefollowing year, and also over 3- and 5-year periods. In none of the cases is the export dummy
significant. Arguably, this does not necessarily means that exporting does not affect financial
health, but simply that beneficial effects do not appear within a 5-year horizon. Data
limitations prevent us from looking at longer horizons, since we would end up with too few
observations.
Equation (3) is estimated on the sample comprising export starters and never exporting
firms only, restricting the first group to successful entrants, i.e. only those firms that do notexit from foreign markets (this explains the drop in the number of observations when movingfrom Table 3 to Table 4. Results are qualitatively unchanged if they are included: their
exclusion should make easier to find an ex post benefit since the sample is biased in favor of
the most successful firms.
In Section 2 above we have discussed two possible reasons why exporting may exert apositive effect on firm financial health, namely a diversification effect and a signaling effect.
In both cases one could argue that the mere fact of selling part of the production above is not
sufficient to trigger those beneficial effects, but that there is a sort of threshold effect below
which export does not count. In other words, it seems natural to look at whether export
intensity plays a role in the game or not. As already mentioned, Campa and Shaver (2002) failto find a relation between the share of sales to foreign customers and financial constraints,
while Greeneway et al. (2007) disregard the issue.
Hence, we substitute the export dummy with the log of export intensity (defined as export
over sales). Results presented in Table 5 are qualitatively unchanged: a higher share of sales

in foreign markets is not associated with higher growth in financial health, regardless of theway this is measured.15
Our conclusion is confirmed when we re-estimate equation (3) on a subsample comprising
only non exporting firms and those export starters characterized by an export intensity larger
than the sector median. Results (not reported) mimic those already presented in Table 4 andtherefore do not provide any support to the existence of a beneficial effect of exporting on
financial health. Thus, overall we do not find any compelling evidence that export
participation is associated with improved financial health in the years that follow entry into
foreign markets.

[Insert Table 5 here]
5. MODELLING THE DECISION TO EXPORTFirm export behavior must ultimately be conceived as a series of decisions regarding bothparticipation to export markets and the firm’s commitment to international trade. These
decisions can be modeled as the outcome of a variety of factors. Heterogeneity of firm
productivity levels is the utmost explanation for the observed differences in export behavior
across firms. Because firms are heterogeneous in their productive efficiency, they all have an
idiosyncratic ability to cope with the sunk entry costs associated with exporting. Yet this may
not exhaust the explanation. The firm’s ability to access external financial resources may wellconstitute another important part of the story. In this Section, we first investigate the factors
driving the decision to export in a standard binary choice framework (Roberts and Tybout,
1997; Bernard and Wagner, 2001). Then we focalize on those firms entering for the first time
foreign markets since, in presence of sunk costs, financial constraints should be particularly
relevant for first-time exporters. Finally, we look at the determinants of export intensity.
Taking stocks of our previous findings, we expect financial constraints to be an importantdriver of export behavior by firms, controlling for other relevant factors such as productivity,
human capital and firm size.

15

The message does not change if both the export dummy and export intensity are included among theregressors, as well as when we add additional controls such as a dummy for multi-plan firms and wage
per employee.

a. The decision to export
In this Section we follow closely Roberts and Tybout (1997) and Bernard and Wagner (2001)
and estimate a reduced form econometric specification whereby the probability of exportingat time t is considered as a function of firm characteristics at time t-1. These include firm size,
productivity, wage per employee, along with industry and year dummies. We augment thisstandard specification with our two alternative measures of financial constraints, so that we
end up estimating the following equation:

EXPit = α + β1Sizei , t −1 + β 2Wagei ,t −1 + β 3TFPi , t −1 + β 4 Subsid i + β 5 FIN i , t −1 + industry + year + ε it(4)

where the subscript i indexes firms and t, time. EXP is a dummy variable equal to 1 if firm iexports in year t, and 0 otherwise. Size is the log of employment (total hours worked), Wage isthe log of the ratio between the firm total wage bill and the number of hours worked, TFP is
our index of relative productivity (in logs), Subsid is a dummy variable equal to 1 if the firm
has multiple business units, and 0 otherwise. FIN denotes alternatively Score A or the
Liquidity ratio.
In first instance equation (4) is estimated by means of a random effect probit model. Then,
taking stock from the previous literature that stresses the importance of hysteresis in export
markets, we augment the model with lagged export status. The dynamic specification is thenestimated again using both a random effect probit model and a Dynamic GMM estimator(Bernard and Wagner, 2001; Greeneway et al. 2007). Results are presented in Table 6.

[Insert Table 6 here]
In the static model of Columns (1) and (2) financial variables have the expected sign and are
significant: more liquid firms are more likely to export in the next period, as well as firms
enjoying a better financial health (as measured by Score A). Results do not change when we
introduce lagged export status, suggesting that financial constraints remain a significant
determinant of export strategy also for firms that already operate in foreign markets.
However, once the lagged dependent variable is included among the regressors, potential
endogeneity biases arise, which must be taken care of. This is done by means of the panel
GMM estimator developed by Arellanoo and Bond (1991): results obtained using thismethodology are presented in Columns (5) and (6). These show that once endogeneity biases

are corrected for, past export status captures the effect of all other controls. Only size retains asignificant effect on the probability of exporting, whereas all other variables —including
financial constraints— seem irrelevant for firms that are already selling abroad.
Hence, results from Table 6, while broadly consistent with the prior of financial variables
being relevant for the decision to export, are not unambiguously supporting this intuition. The
likely cause is that in estimating equation (4) we are pooling together new exporters and firmsthat already operate in foreign markets. But since the main reason why external financial
resources are relevant is because of the need to cover sunk entry costs into destination
countries, then it is likely that financial variables affect very differently first-time exporters
and firms already accustomed to selling abroad. To investigate this issue further, we now
focus our analysis only on those firms faced with the decision to start exporting for the firsttime. This is done mobilizing two econometric methodologies that account for time durationand selection biases, respectively.
b. Accounting for time duration to export markets
To model firm entry decision into foreign markets in terms of time duration is tantamount toequating firm growth with entry into export markets. Although export is a relatively rare
activity (Bernard et al., 2007), it becomes extremely widespread for larger firms: for instance,
French data reveal that 70% of firms with more than 20 employees (with represent our unit ofanalysis) will ultimately penetrate foreign markets; this proportion increases to 95% for firms
with more than 500 employees16. This suggests that entry into export markets is a necessary—yet significant— step for growth. Hence, the relevant issue is not so much whether firms
enter into export market, but rather the time it takes for a firm to eventually start serving
foreign customers. This Section tackles this issue explicitly using discrete-time duration
models.
We estimate a duration model for grouped data following the approach first introduced byPrentice and Gloeckler (1978). Suppose there are firms i = 1,…,N, that enter the industry attime t = 0. The hazard rate function for firm i at time t and t = 1,...,T to start exporting isassumed to take the proportional hazard form: qit=q0(t)×X',itb, where q0 (t) is the baseline
hazard function and Xit is a series of time-varying covariates. More precisely, let X = {Size;
Wage; TFP; Subs; FIN}, where Size stands for employment weighted by the numbers of hours
worked, Wage is the wage bill per employee in order to control for systematic differences
16

Data for all firms, on the contrary, tells that less then one fifth of manufacturing firms exports, afigure well in line with the literature.

between firms in terms of human capital, TFP is total factor productivity, Subs is set to unity
if firms has one or more subsidiaries and FIN is a measure of financial constraints. In linewith the theoretical literature, a clear assumption of the empirical model is that two firms of
the same age and with similar characteristics should start exporting after the same number ofyears from start-up. The discrete-time formulation of the hazard rate of first export for firm i
in time interval t is given by a complementary log logistic function such as:

{

(

)}

ht ( X it ) = 1 − exp − exp X it' β + θ (t )(5)where θ(t) is the baseline hazard function relating the hazard rate ht ( X it ) at the tth interval withthe spell duration (Jenkins, 1995). Importantly, our sample includes companies of different
age, which in turn may harm the estimation of the non-exporting spell. To deal with the issueof left censoring, we augment vector X with a full vector of dummy variables controlling forfirm age. We also add a full set of year fixed effects in order to control for the business cycle.
Hence, controlling for both firm age and year-specific effects will allow us to interpret thehazard function as the result of the mere passage of time on the probability to expand
activities abroad. Finally, we model the baseline hazard function by using the log-transformedof θ(t), an integer counting the number of years of presence in the market. This choice is thediscrete-time counterpart of a continuous-time specification with a Weibull hazard function.17
This model can be extended to account for unobserved heterogeneity —or ‘frailty’, toaccount for systematic differences between firms.18 In a way, the inclusion of unobserved
heterogeneity is a generalization of a pooled specification ignoring it. It controls for both theomitted variable bias and measurement errors in observed survival times and regressors(Jenkins, 1995). Suppose that unobserved heterogeneity is described by a random variable ei
independent of Xit. The proportional hazard form with unobserved heterogeneity can now be
written as:
It is important to distinguish between the time variable θ(t) and year specific effects. Variable θ(t) is
an integer counting the number of years of observations in the dataset for a specific company, and year
dummies control for macro-economic shocks which pertain to all companies. Moreover, we haveexperimented for alternative specifications if Time, namely the semi-parametric, polynomial
specification using time together with its squared θ(t)2 and cubic values θ(t)3, and a fully nonparametric approach using duration-interval-specific dummy variables. Because this choice does notaffect the conclusions, we do not report the results from these specifications, but they are available on
request.
18
The term ‘frailty’ comes from medical sciences where it represents the unobserved propensity toexperience an adverse health event.
17

{

(

) }

ht ( X it ) = 1 − exp − exp X it' β + θ (t ) + ε i(6)where εi is an unobserved individual-specific error term with zero mean, uncorrelated with theX’s. Model (6) can be estimated using standard random effects panel data methods for abinary dependent variable, under the assumption that some distribution is provided for theunobserved term. In our case, we will assume that the εi are distributed Normal and Gamma(see Jenkins, 1995 for more details). Note that our comments will focus on the Gamma
distribution exclusively, and other estimates are provided as robustness checks.19 Lastly, we
perform a likelihood ratio test between the unrestricted model (with unobserved
heterogeneity) and the restricted model (without unobserved heterogeneity) to test for the
relevance of unobserved frailty. The reported estimates are chosen from the log likelihood
ratio test (LR test).

[Insert Table 7 here]
The results are displayed in Table 7, where Score A and the liquidity ratio have been used as
proxies for financial constraints. The first two columns provide estimates for pooled data andcolumns (3) to (6) display estimated parameters controlling for unobserved heterogeneity.20
Generally speaking, all specifications exhibit strong consistency in the direction andsignificance of the parameter estimates. Since the LR test confirms the importance ofunobserved heterogeneity, we exclusively comment on the specification controlling that
controls for it.
Particularly satisfactory is the consistency and significance of the two measures offinancial constraints, despite the fact that, with a critical probability value of 7% in model (5),
Score A appears somewhat less significant. Both suggest that financially healthy firms find it
easier to start exporting. To put it differently, availability of financial resources shortens timeleading to first export. In a way, this should come as no surprise. Both the theoretical andempirical literature insist on the sunk costs implied by the expansion of activities abroad.
19

This choice is arbitrary. As of today, there is no particular reason to prefer the assumption of a
Gamma-distributed frailty over the normal distributed one. This choice is mainly motivated by the factthat the Gamma distribution is particularly convenient to manipulate and has thus been the mostpopular. As displayed in Table 7, results under the alternative assumption are in all respect consistent
with the Gamma assumption.
20
In columns (3)and (4) unobserved heterogeneity is assumed to be normally distributed, while incolumns (5) and (6) the assumption is a Gamma distribution.

Hence firms with stronger financial resources should be in a better position to cope with theextra costs —with no immediate compensation— associated with first exports. One surprisecomes from the counter-intuitive sign of TFP, implying that more productive firms are lesslikely to enter into export markets. Although apparently at odds with the theoretical literature(Melitz, 2003), this results is consistent with Bellone et al. (2008b), where a U-shaped
productivity pattern is revealed for future French exporters.21 Lastly, both size and humancapital, i.e. respectively employment and wage per employee, have the expected sign. Theseestimates imply that large firms intensive in human capital are more likely to go abroad.

[Insert Table 8 here]
Using model (5), Table 8 displays the estimated baseline hazard function for the
representative firm. Note that in using a discrete-time duration specification, hazard rates canbe interpreted as probabilities of entry into export markets. First, we observe that the hazard
rate function is monotonically decreasing in time. This suggests that as time goes by, firms
will find it increasingly difficult to start exporting. We observe that the probability of entry
into export markets is 35% at the year of entry into the industry, to reach 24% after year 13.
Second, Table 8 also displays the hazard function for firms located in the 1st and 9th decile ofeach significant explanatory variables, holding all other firm characteristics constant. For
simplicity, we choose to comment year 5 exclusively. We observe the followings: withrespect to the first decile of the size distribution, firms located at the 9th decile are 30% more
likely to enter into export markets (the probability rises from 24 to 32%); firms paying higher
salaries (in the 9th decile of the distribution) are 7 times more likely to enter into export
markets than firms in the first decile; firms located at the 9th decile of financial constraints(Score A) are 6% more likely to enter into export markets.
This important role for average wage suggests that the variable is probably serving as aproxy for some unobserved firm characteristic, that are relevant in the decision to export, suchas human capital. Turning to the effect of financial constraints, its limited magnitude (+6%)
leads us to conclude that if their effect is statistically significant, its economic relevance issomewhat limited as compared with other variables. Using the liquidity ratio of model (6), the21

The paper shows that future exporters outperform their non exporting counterparts five years prior toentry into export market. But in their preparation to first export, firm productivity is found to
temporarily decrease to then recover contemporaneously with entry. The interpretation is that thebenefits from sales to foreign markets accrue at the time of entry, boosting the firm’s level ofproductivity.

marginal effect of financial constraints on the probability of exports rises somewhat, with aprobability of exporting of 25.8% and 29.0% for firms located at the 1st and 9th decile,respectively. Comparing these values with those of Table 8 shows strong consistency and theconclusion that the effect of financial constraints on the probability to export is limited inscope still holds.
To recapitulate, we find that financial constraints are a significant determinant of firm
export decision, but that as firm size, the impact of financial constraints upon the probabilityof exporting is far less important than the firm’s endowment in human capital and skills. Next
Section extends the analysis to investigate the role of financial constraints on firm-level
export intensity.

c. Accounting for initial export intensity
Here, we tackle the issue of the relationship between financial constraints and export volume
at the year of entry. Because positive exports implies that non-exporters be excluded from thesample of analysis, one must first correct for sample selection bias and depict in thequalitative equation the probability of being an exporter. In other words, explaining firm
commitment to export markets necessarily calls for a broader investigation explaining why
firms choose to expand their activities abroad in the first place. First, firms must decide
whether to export and, conditional on this decision, they set the volume of their production tosell abroad. We model these two decisions by means of a Heckman model as follows()

eiit = Χ i ,t − s β + σ 'λ Χ i' , t − s βˆ ' + υi(7)where i stands for firm i, t stands for year t, ei is log export intensity, X is the vector of
explanatory variables as previously defined, β is the vector of parameter of interest and υ is anerror term.22[Insert Table 9 here]

22

(

)

The Heckman specification augments the model by adding the inverse Mill’s ratio λ Χ i' , t − s β̂ ' , whereβ̂ is obtained from the first step probit regression of export decision on X', a vector of variables
describing the determinants of export entry, which may or may not be equal to X. In the present case,
we set X≡X'. Parameter σ' is then used to estimate ρ, a measure of selection bias correction.
'

Table 9 reports the results for both the selection equation explaining the decision of entry into
export markets and the quantitative equation explaining export intensity at the time of firstexport (firms exporting throughout the sample period are therefore excluded from theanalysis). Control variables are set both at time t-1 and t-3. Altogether, the qualitativeequation shows consistency with the previous results concerning wage per employee andfinancial variables, whereas the role of size vanishes. Looking at the quantitative equation attime t-1, the striking result is the switch in sign regarding financial constraints. Financiallyhealthy firms are more likely to enter into export markets, but conditional on this decision,
firms which commit more to international trade appear to be financially more constrained.
Since the regression analysis is performed only on export starters, export intensity
measures the share of sales shipped abroad in the first year of export. Hence, we interpret theresults as a signal that export intensity is an indirect measure of sunk entry costs into export
markets. Indeed, since these costs tend to be market-specific, high export intensity is most
likely associated with a firm entering simultaneously in several foreign markets.23
The above remarks should call for caution. Our interpretation suggests that financial
constraints suffer from an endogeneity problem yielding this negative association with export
intensity. Importantly, the endogeneity problem is essentially caused by the simultaneous
relationship between sunk costs of entry into export markets and financial health. Hence inorder to control for that, we lag all explanatory variables three years.24 We find that three
years before entry, financially healthy firms find it easier to enter into export markets, butfinancial variables do not bear any relation with the choice of the share of production whichgoes abroad.
To recapitulate, financial health is an important determinant of the decision to enter into
export markets made by firms. But export intensity is chosen irrespective of financial health.
The choices about the volume of export and the number of markets served are not driven by
the availability of external financial resources.

23

We do not have information on the number of markets served by exporting firms in the sample.
However, the (Spearman’s) correlation between export intensity and the number of export markets
served —computed on a different sample of French firms— ranges between 0.53 and 0.65, thus
signalling a rather strong relationship between the two variables (we thank Matthieu Crozet forproviding us with this information).
24
We also experimented for a five-year lag but since the results are strictly equivalent to those using athree-year lag, we report the results for a three-year lag exclusively.

6. CONCLUSION
In the last 10 years or so, a large empirical literature has emerged that studies the peculiar
characteristics of exporting firms. Two broad stylized facts emerge: exporters perform
substantially better than their non exporting competitors; there are wide cross-country
differences in firm export behavior. This paper adds to this stream of the literature by looking
at financial factors as a key determinant of firm decisions. More specifically, we investigate
whether limited access to external financial resources may prevent firms from expanding theiractivities abroad, and whether internationalization has any positive effect on financial health.
We find strong evidence that less credit-constrained firms self-select into export markets
or, from a complementary point of view, that external funds are an important determinant offirm export status. In fact, export starters display better financial health than their nonexporting competitors even before starting to operate abroad. On the contrary, the hypothesisthat internationalization leads to better access to financial markets finds no support in our
analysis. We observe that access to external financial resources is a significant but not crucial
determinant of the probability to start exporting, but we find no evidence of a positive
relationship between financial health and the share of production sold abroad. This result
corroborates the idea that the relevance of financial constraints is due to the presence of sunk
entry costs.
All in all, we conclude that our empirical analysis supports recent models of international
trade based on firm heterogeneity and sunk entry costs. In this context public intervention canbe called upon to help efficient but financially constrained firms expand their activities
abroad.
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Acknowledgments
The authors blame each other for any remaining mistake. They nevertheless agree on the needto thank Sylvain Barde, Jean-Luc Gaffard, Sarah Guillou, Mauro Napoletano and Evens
Salies for insightful conversations. Seminar participants at GREDEG, University of Trento,
the 2nd ACE International Conference in Hong Kong, the ISGEP Workshop in Nottingham,
the 12th International Schumpeter Society Conference in Rio de Janeiro, the 10th ETSG
Conference in Warsaw, and the DIME Workshop ‘The dynamics of firm evolution:
productivity, profitability and growth’ in Pisa provided us with valuable comments on earlier
drafts.

TABLE 1
Correlations between Financial Constraints indexes
Pearson’s r and Spearman’s ρ Correlation Coefficients
Liquidity ratio Leverage ratio
Score A
Liquidity ratio
–
-0.98
0.49
Leverage ratio
-0.92
–
-0.53
Score A
0.46
-0.44
–
Score B
0.41
-0.40
0.91

Score B
0.44
-0.47
0.90
–

Notes:
Numbers in italics denote Spearman’s ρ correlation coefficients

TABLE 2
Descriptive statistics
All
Firms

Continuous
Exporters

ExportStarters

Never
Exporters

Employees
TFP
Wage per employee

88.0830.997
0.103

115.839
1.003
0.107

59.6720.990
0.099

56.7990.992
0.091

1,477.65***
85.57***
515.14***

Score A
Liquidity ratio
Observations

5.620
0.293
167,597

5.825
0.320
85,7205.448
0.273
63,4025.261
0.240
18,475

1,133.34***
727.78***

F-statTABLE 3Self-selection into exporting by less constrained firms
Score A
s=1

Liquidity ratio
s=3

(1)

s=1

s=3

(2)

(3)

(4)

Export0.146***[0.052]0.228**[0.094]0.016*[0.010]0.009[0.016]log Emplt-s

0.188***

0.075

0.006

-0.025**[0.041][0.067][0.008][0.012]2.794***

2.933***

0.347***

0.443***[0.142][0.247][0.026][0.043]Firms/Observationsof which: starters†

5,727
3,427

2,169
1,284

5,727
3,427

2,169
1,284

non export.

2,300

885

2,300

885

0.111

0.159

0.073

0.132

log TFPt-s

R-squared

Notes:† Including firms that later stop exportingStandard errors in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

TABLE 4
Measuring ex-post effects
t
t+1
(1)
log (Exp/Sales)t=0

Score A
t+1
t+3
(2)

t+1
t+5
(3)

t
t+1
(4)

Liquidity ratio
t+1
t+3
(5)

t+1
t+5
(6)

-0,003

0,036

0,042

0,047

-0,057-0,052[0.013][0.022][0.034][0.033][0.057][0.090]log Emplt=0-0,012-0,001

0,019-0,0350,007-0,003log TFPt=0[0.009][0.014]-0.185*** 0,065[0.018]-0,071[0.026]
0,066[0.038]-0,198[0.053]-0,302[0.034][0.059][0.083][0.093][0.157][0.226]

Firms/Observations
4,387
of which: starters† 1,423

1,823
833

1,152
541

3,307
1,144

1,448
686

905
441

990

611

2,163

762

464

0,088

0,113

0,043

0,093

0,143

non export. 2,964
R-squared

0,056

Notes:† include only firms that keep exporting thereafterStandard errors in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

TABLE 5.
Measuring ex-post effects controlling for export intensity
t
t+1
(1)
log (Exp/Sales)t=0

Score A
t+1
t+3
(2)

t+1
t+5
(3)

t
t+1
(4)

Liquidity ratio
t+1
t+3
(5)

t+1
t+5
(6)

0.100

0.054

-0.047-0.013-0.166-0.534[0.061][0.082][0.120][0.158][0.217][0.335]log Emplt=0-0.013

0.001

0.018

-0.0300.004-0.008log TFPt=0[0.009][0.013]-0.186*** 0.064[0.018]-0.070[0.026]0.064[0.038]-0.191[0.053]-0.289[0.034][0.059][0.083][0.093][0.157][0.226]

Firms/Observations
4,387
of which: starters† 1,423

1,823
833

1,152
541

3,307
1,144

1,448
686

905
441

non export. 2,964
R-squared

0,055

990

611

2,163

762

464

0,089

0,115

0,044

0,093

0,140

Notes:† Include only firms that keep exporting thereafterStandard errors in brackets* significant at 10%; ** significant at 5%; *** significant at 1%

TABLE 6.
Determinants of the decision to export
RE Probit
log Emplt-1

log (Wage/Empl)t-1
log TFPt-1
Subsidt-1
Score At-1

Dynamic RE Probit

Dynamic GMM

(1)

(2)

(3)

(4)

(5)

(6)

0.925

0.931

0.375

0.384

0.155

0.201

[0.020]*** [0.020]*** [0.012]*** [0.012]*** [0.089]*
0.897
0.878
0.564
0.549
-0.004[0.092]**0.050[0.042]*** [0.042]*** [0.028]*** [0.028]*** [0.144]-0.0970.001-0.203-0.097-0.001[0.141]
0.038[0.069][0.066][0.049]*** [0.047]** [0.099][0.102]

0.130

0.130

0.126

-0.1300.212[0.027]*** [0.027]*** [0.018]*** [0.018]*** [3.359][2.773]

0.042

0.045

[0.005]***0.003[0.004]***0.235

Liquidityt-1

0.128[0.029]***
Exportt-1

1.902[0.012]0.195-0.017[0.021]***
1.898
0.348

[0.081]0.349[0.017]*** [0.017]*** [0.066]*** [0.067]***
Observations
Firms

134,926
22,713

134,926
22,713

134,926
22,713

134,926
22,713

108,755
19,880108,755
19,880of which: exporters

11,678

11,678

11,678

11,678

10,241

10,241starters†

7,938

7,938

7,938

7,938

7,306

7,306

non export.

3,097

3,097

3,097

3,097

2,333

2,333

Sargan J-statistic

0.39

0.54

m2

1.29

1.17

Notes:† Including firms that later stop exportingStandard errors in brackets; sector and year dummies included* significant at 10%; ** significant at 5%; *** significant at 1%

TABLE 7.
Estimating the hazard rate of entry into export markets
Pooledlog Time
log Empl.
log (Wage/Empl.)
log TFP
Subsid.
Score A(1)
-0.651[0.024]***0.103[0.018]***0.817[0.052]***-0.454[0.076]***-0.034[0.041]
0.011[0.006]*

Liquidity Ratio
Observations
Firms

35,794(2)-0.652[0.024]***0.106[0.018]***0.825[0.052]***-0.475[0.075]***-0.033[0.041]0.137[0.033]***35,794of which: starters♯
non export.
LR test §Normal RE†(3)(4)-0.327-0.340[0.098]***[0.096]***
0.172
0.172
[0.030]***[0.030]***
0.959
0.959
[0.073]***[0.072]***-0.450-0.465[0.092]***[0.090]***-0.054-0.052[0.047][0.047]0.014[0.007]*0.159[0.040]***35,79435,794
12,193
12,1938,133
8,133

Gamma RE‡
(5)
(6)
-0.189-0.198[0.096]**[0.095]**
0.226
0.226
[0.036]***[0.036]***
1.002
1.002
[0.073]***[0.072]***-0.398-0.412[0.100]***[0.098]***-0.069-0.068[0.050][0.050]0.014[0.008]*0.167[0.042]***35,79435,794
12,193
12,193
8,133
8,133

4,060
19.82***

4,060
33.05***

4,060
19.17***

Notes:† Random Effect model with Normal distributed frailty‡ Random Effect model with Gamma distributed frailty§ Likelihood Ratio test for unobserved frailty; H0: non significant unobserved frailty♯ Including firms that later stop exportingStandard errors in brackets; dummies for firm age, sector and year specific effects included* significant at 10%; ** significant at 5%; *** significant at 1%

4,060
33.90***TABLE 8.
Estimated hazard rate of entry into export markets by deciles of explanatory variables
Time(in years)

Baseline
HazardSize
1 decile

9 decile

1 decile

9 decile

1 decile

9 decile

1 decile

9th decile

1
2
3
4
5
6
7
8
9
10
11
12
13

0.353
0.318
0.298
0.285
0.275
0.267
0.261
0.255
0.250
0.246
0.242
0.239
0.236

0.316
0.284
0.266
0.254
0.245
0.237
0.232
0.226
0.222
0.218
0.215
0.212
0.209

0.410
0.371
0.349
0.334
0.323
0.314
0.306
0.300
0.294
0.289
0.285
0.281
0.278

0.094
0.083
0.077
0.073
0.070
0.068
0.066
0.064
0.063
0.062
0.061
0.060
0.059

0.616
0.568
0.541
0.522
0.507
0.495
0.485
0.476
0.469
0.462
0.456
0.451
0.446

0.379
0.341
0.321
0.307
0.296
0.288
0.281
0.275
0.270
0.265
0.261
0.258
0.254

0.330
0.297
0.278
0.266
0.256
0.249
0.242
0.237
0.233
0.229
0.225
0.222
0.219

0.344
0.309
0.290
0.277
0.267
0.259
0.253
0.247
0.243
0.239
0.235
0.232
0.228

0.363
0.327
0.307
0.294
0.284
0.275
0.269
0.263
0.258
0.254
0.250
0.246
0.243

st

Notes:The table uses estimates from model (5) of Table 7

Wage per employee
th

st

th

TFP
st

Financial constraintsthstTABLE 9.
Entry and export intensity: Heckman Two-Step modellog Emplt-s

Exportdecision(1)
0.047

Exportintensity(2)-0.032s=1
Exportdecision(3)
0.052

Exportintensity(4)-0.038

Exportdecision(5)0.067

Exportintensity(6)-0.007s=3
Exportdecision(7)
0.072

Exportintensity
(8)
-0.02

log TFPt-s[0.031]-0.569[0.069]-0.117[0.031]*-0.531[0.070]-0.214[0.052]-0.567[0.112]-0.946[0.052]-0.477[0.113]-0.936log (Wage/Empl)t-s[0.124]***0.511[0.373]0.333[0.123]***0.514[0.355]0.361[0.232]**0.134[0.537]*0.555[0.232]**0.130[0.505]*0.551

Subsid.t-s[0.085]***0.311[0.297]0.030[0.085]***0.312[0.297]0.054[0.154]0.106[0.303]*0.135[0.154]0.119[0.303]*0.123[0.060]***0.028[0.198]-0.059[0.060]***[0.198][0.091]0.044[0.204]-0.035[0.091][0.207]

Score At-s

[0.010]***[0.025]**[0.017]**[0.041]0.102-0.3080.079-0.301[0.054]*[0.124]**
5727
3427

[0.099][0.196]2169
1284

Liquidity ratiot-s
Firms/Observationsof which: starters†
never exporters
Wald c2
ρ

5727
3427
2300
883.60***2300
881.33***885
586.05***885
581.03***-0.169-0.111-0.046-0.053

Notes:
Standard errors in brackets; dummies for sector and time specific effects included* significant at 10%; ** significant at 5%; *** significant at 1%† including firms that later stop exporting

33

2169
1284