Download Capital Structure and Exporting

Capital Structure and Exporting: Evidence from Import
Tari¤ Changes
Olga Kuzmina
Natalya Volchkova
New Economic School
New Economic School
This draft: April 2016
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Abstract
We investigate how foreign market entry through exporting a¤ects capital structure of …rms.
To identify the causal e¤ect of interest we use variation in foreign countries import tari¤s that
are plausibly exogenous to domestic …rms. We …nd that reaching export markets increases
…rms’ leverage, suggesting that diversi…cation can be achieved even through random export
transactions, and is an important channel in reducing …rm cash ‡ow volatility. The results of
the paper highlight the importance of examining operating strategies as integral determinants
of corporate …nancing policies, as well as contribute to the debate on the riskiness of export
transactions in the international economics literature.
Keywords: Capital Structure, Exporting, International Markets, Diversi…cation
JEL codes: D22, G32, F14
Email for correspondence: [email protected]; http://pages.nes.ru/okuzmina. Address: Suite 2.09, 100A Novaya
Street, Skolkovo, Moscow 143026 Russia.
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1
Introduction
The seminal work of Modigliani and Miller (1958) established the irrelevance of capital structure in
a perfect market – one of the most fundamental results in …nancial economics – that also implies
that …rms can make real and …nancial decisions independently. The introduction of market imperfections, such as bankruptcy costs of debt, however, opens up the scope for interactions between real
corporate strategies that concern organizational structure and production processes of a …rm, and
…nancial strategy guarding the choice between debt and equity. How do operations a¤ect …nancing
decisions? Our paper explores how foreign market entry through exporting a¤ects capital structure.
Importantly, we attempt to provide causal evidence –by using exogenous changes in foreign import
tari¤s.
A growing literature has examined how organizational and product-market strategies a¤ect
leverage. In particular, more debt in capital structure of …rms is associated with more ‡exible
manufacturing systems (Brennan and Schwartz, 1985, He and Pindyck, 1992, Kulatilaka and Trigeorgis, 2004), more ‡exible workforce (Kuzmina, 2014, Simintzi et al, 2015), more ‡exible pension
plans (Petersen, 1994). The main economic mechanism in these papers suggests that more ‡exible
operating strategy means lower cash ‡ow volatility, risk of default and expected bankruptcy costs
for any given level of debt, and hence …rms optimally choose a higher level of debt that is bene…cial
for various considerations (such as tax shield considerations (DeAngelo and Masulis, 1980), mitigation of free cash ‡ow problems (Stulz, 1990), or other motives). This is also often termed in the
literature as substitution between operating to …nancial leverage.
Our hypothesis is in line with this economic mechanism and looks at …rms’ product-market
strategy, and reaching foreign markets in particular. If exporting makes …rms’ cash ‡ow more
volatile (due to currency risks and/or less certain market demand in a foreign country), the risk of
default and expected bankruptcy costs increase, and …rms should have less leverage in their capital
structures. If, on the other hand, exporting …rms are able to hedge risks of domestic market (e.g.
when demand shocks are imperfectly correlated across countries, or …rms rely on foreign-currencypriced imported inputs), they should be able to bear more leverage in their capital structures.
Using a panel dataset of Russian …rms in 2006-2011 we show that exporting leads …rms to
increase debt …nancing. We build the identi…cation strategy on the exogenous changes in foreign
import tari¤s that di¤erentially a¤ect Russian …rms depending on the composition of products and
export destinations that are prevalent in di¤erent industries. This allows us to use both crosssectional and time-series variation, and identify the e¤ect of interest. We …nd a large economic
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magnitude of this e¤ect. In particular, a 3 percentage point increase in the propensity to export
(which is about the mean in the data) leads …rms to have 15 percentage points higher debt-to-assets
ratio (which is also about the mean of this variable). Finally, we complement our main results by
looking at other margins of debt. In particular, we …nd that the e¤ect on debt comes mostly from
the short-term, rather than long-term debt.
Russian data provide a rare opportunity to study this question for several reasons. Most importantly, Russia is one of the few countries where the highly-detailed transaction-level data on
exports by each …rm are available1 . These data allow exploring the exporting activities of …rms
in a multi-dimensional way by looking at quantities of various products and from various destinations exported to other countries, as well as constructing instruments for exporting based on the
destination and product baskets of …rms. Only similarly-detailed data can be neatly matched with
product-destination tari¤s that Russian …rms are subject to when exporting to foreign countries.
This means that exogenous changes in tari¤s (that are supposed to drive exporting decision in the
…rst stage of the instrumental-variable estimation) will exploit a lot of cross-sectional and time-series
variation to increase the power of detecting the …rst-stage correlation. This level of disaggregation
will also allow us to control for unobserved …rm, industry and year characteristics using …xed-e¤ects
speci…cations.
We contribute to the corporate …nance literature by showing that exporting is an important
factor that a¤ects capital structure of …rms. As Lemmons, Roberts, and Zender (2008) argue, a
major part of the variation in capital structure, as well as why leverage is persistent over time, is
yet unexplained despite capital structure being one of the most popular corporate …nance topics.
This suggests that we may seek to further explore the fundamental factors related to production
process, boundaries of the …rm, and organizational structure as the essential drivers of corporate
…nancing policies. To the best of our knowledge, we are the …rst to point at foreign market entry
as a new such driver. Addressing this question is also important from the applied perspective, as
the interplay between exporting and capital structure supports the complementarity of CEO’s and
CFO’s decision-making.
This paper also contributes to the international economics literature in several ways. First of
all, theoretical models often assume that exporting increases the risk of the …rm cash ‡ow (e.g.
Feenstra et al, 2014). However, it is not easily testable in practice for two reasons. First, it is not
illustrative to measure empirically the riskiness of an average export transaction, since …rms choose
1
Similarly-detailed data have been recently used by Paravisini et al (2014) for Peru to explore the e¤ect of
availability of credit on exporting.
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export products, quantities, times and destinations optimally for diversi…cation purposes (i.e. they
select into exporting only if the resulting pro…t-risk pro…le is better). We overcome this issue by
using exogenous foreign import tari¤ changes to trace the e¤ect of random export transactions,
i.e. when …rms select into exporting due to exogenous changes and not due to reasons correlated
with their unobserved characteristics a¤ecting capital structure (productivity, riskiness, …nancial
constraints, etc). And second, simple measures of riskiness (probability of default, overall cash ‡ow
volatility, etc), re‡ect both the change in risk due to exporting and the endogenous adjustment of
leverage to this change in risk, mitigating the original change. We overcome this second challenge by
proposing to look at changes in leverage themselves as a measure of a changed riskiness of …rm due
to exporting. The bene…t of using leverage is that it incorporates automatically the …rm’s perceived
change in risk, so that we do not need to rely on long time-series to calculate some volatility-related
measures. And, by construction, it also controls for the endogenous change in leverage.
The second contribution to the international economics literature is to note the policy implications of our study. In a setting with imperfect capital structure adjustment, a non-zero causal
e¤ect of exporting on capital structure also suggests that exogenous changes in government policies,
aimed at certain organizational changes in the …rm, such as export promotion policies, would have
indirect consequences for their …nancing decisions as well. Finally, this may have implications for
which …rms are less likely to survive in the long run and for the industry allocative e¢ ciency; with
quantitative predictions potentially achieved through incorporating adjustment frictions into a joint
model of …rm’s organizational structure and corporate …nancing.
The paper proceeds as follows. Section 2 describes the data and builds in the identi…cation
strategy. Section 3 presents the empirical results on the e¤ect of exporting on capital structure.
Section 4 concludes.
2
Data and Empirical Speci…cation
This section presents a description of the various sources of data used in this paper. It then discusses
how foreign import tari¤s can be used as a source of exogenous variation in exporting, both within
and across …rms, to identify the relationship between exporting and capital structure of a …rm.
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2.1
Data Description and Variables De…nition
The results of this paper are based on three sets of data. We combine transaction-level data on
exports, …rm-level …nancial data, and data on import tari¤s that Russian exporting …rms face
abroad.
The …rst part of the data comprises of the universe of customs declarations that Russian …rms
…led in order to ship any product abroad during the period between 2006 and 2011. These data
are exceptionally detailed, as there is information on every single entry of the customs declaration,
including, but not limited to: the tax identi…er of the …rm, product shipped (at the very disaggregated HS10 level2 ), country of destination, export value of the transaction, name of the …rms
receiving the good, etc.
These data are then merged to the …rm-level …nancial information using the tax identi…er.
The …rm-level data come from Bureau van Dijk Ruslana database that contains all balance sheet
and pro…t-and-loss statement information for Russian …rms (collected from the Federal Statistical
Agency of Russia), as well as the primary industry identi…er (at 4 digits of NACE Rev 2). The
dataset contains information on both private and public …rms and represents all 83 regions of the
country.
The last set of data is the database of import tari¤s from WITS Worldbank that contains
information on product tari¤s (at HS6 level) that each country charges on imports from each other
country. Since we explore the exporting decision of Russian …rms, the relevant tari¤s that these
…rms face are the import tari¤s of each foreign country agains Russian imports. As these tari¤s are
set by foreign countries and Russian …rms are not at the core of the bargaining power in most of
the industries, they can be considered arguably exogenous for Russian …rms. We explain in section
2.3 how these tari¤s are assigned to each …rm.
As we also explain further, to avoid endogenous selection into product-destination baskets, we
drop the …rst year of data, when these baskets are calculated. We also consider only …rms with at
least 5 employees (as this is a minimum requirement to talk about …rms with capital structures,
rather than sole proprietorships), and to ensure consistent reporting – only those who have nonmissing …nancial information for at least 4 years.
Table I reports the summary statistics of our merged data. To eliminate outliers and data errors,
we have winsorized all …nancial variables at 1% and 99% tails. Following the literature, we use the
2
Harmonized Commodity Classi…cation Codes (HS) is the universal system of classifying goods. They are harmonized world-wide at 6 digits (HS6), between Russia and European countries at 8 digits (HS8), and the last 2 digits
are Russian-speci…c.
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ratio of total debt to total assets as a measure of leverage. Total Debt is de…ned as the sum of
short-term and long-term debt, while Total Assets are the book value of assets. As we observe,
around 15% of …rm …nancing comes from debt, which is comparable to other countries (e.g. Rajan
and Zingales, 1995). Of the total of about 300 thousand …rms in our data, about 16 thousand
export at least once in the data, while the average share of exporting …rm-years is 2.9%. Firm size,
measured as the natural logarithm of a …rm’s total assets, is equal to 5.4, which corresponds to
approximately 224 thousand US dollars. Average pro…tability, measured by a …rm’s gross pro…t
over assets, is equal to 60%. To proxy for the nature of assets, we also measure tangibility, de…ned
as the ratio of tangible net …xed assets to total assets, which is about 19% in our data. These
variables are typically found to be determinants of capital structure choice (Titman and Wessels,
1988, Rajan and Zingales, 1995, Frank and Goyal, 2009) and will be used as …rm control variables
in the analysis.
2.2
Panel Framework and Endogenous Choice of Exporting
The main hypothesis of this paper is that reaching foreign markets through exporting a¤ects capital
structure of a …rm –by changing the riskiness of cash ‡ow volatility, the corresponding probability
of default, and expected bankruptcy costs. This relationship of interest can be empirically estimated
in the following way:
Dit =
t
+ Exportit + Xit0 +
i
+
(1)
it ;
where Dit is the ratio of total debt to total assets of …rm i in year t,
t
are the year …xed e¤ects,
Exportit is the dummy variable that measures whether the …rm exported anything, Xit0 are various
…rm-level control variables; included in some speci…cations to account for …rm-speci…c shocks), and
i
are …rm …xed e¤ects.
The panel structure of the dataset allows us to control for any time-invariant observed and
unobserved di¤erences that …rms may have with respect to their capital structure. Some examples
include whether the …rm in general has a more variable cash ‡ow due to the nature of demand for its
product, whether it is a small …nancially constrained business, etc. This provides an opportunity to
explore what drives within-…rm changes in …nancing decisions holding constant any time-invariant
heterogeneity across …rms. Notably, …rm …xed e¤ects also automatically control for any timeinvariant industry and region characteristics. In addition, including year …xed e¤ects makes sure
that the di¤erences in leverage ratios are not explained by overall macroeconomic e¤ects driving the
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cost of …nancing, the availability of credit, purchasing power of other countries, average exchange
rate ‡uctuations.
Although equation (1) already picks up a lot of endogeneity as outlined above, there is still a
possibility for the time-varying unobserved component of the error term being correlated with the
…rm’s choice of exporting. In this case, estimating (1) using ordinary least squares would yield
an inconsistent estimate of . First of all, exporters have long been known to be larger and more
productive. This positive selection into exporting has been modelled theoretically by Melitz, 2003
and tested empirically in the context of various countries – by Bernard and Jensen, 1999, for the
U.S.; Pavcnik, 2002, for Chile; De Loecker, 2007, for Slovenia; Lileeva and Tre‡er, 2010, for Canada;
Eaton et al for France, 2011; Bustos, 2011, for Argentina; Volchkova, 2011, for Russia; and others).
At the same time larger …rms have more debt in capital structure (Frank and Goyal, 2009, among
many). To some extent, our size variable controls for such a selection, but there may still be
unobserved productivity shocks that drive both, biasing the -coe¢ cient in (1) upwards.
Second, exporters and non-exporters may face di¤erent …nancial constraints. In particular,
Chaney (forthcoming), Manova (2013), Manova and Foley (2015) provide theoretical and empirical
evidence that a developed domestic …nancial market facilitates exporting as well. For our hypothesis
it means, that a potential correlation between debt and exporting may be explained by unobserved
…nancial constraints that are associated with less exporting and less debt, thus biasing the true
coe¢ cient upwards as well.
Finally, and most importantly, …rms may endogenously choose where, when and what to export
in an attempt to diversify their sales and match better the currency of their revenues with the
currency of their costs. If there is such an unobserved desire for diversi…cation (e.g. if domestic
sales are too risky because the costs are in foreign currency), then …rms would also tend to have
less debt in their capital structures and we would observe a correlation of debt and exporting, that
would be a downward-biased estimate of the causal e¤ect.
All of the above concerns illustrate the importance of using exogenous variation in the propensity
to export. Hence, we proceed by explaining how foreign import tari¤s can help in addressing
endogenity in the instrumental-variables framework.
2.3
Implementation of the Identi…cation Strategy
In order to establish the causal e¤ect of exporting on capital structure of the …rm, one needs to
hold other determinants of capital structure constant. We do so by estimating
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in equation (1)
through IV-2SLS using an exogenous source of variation in exporting coming from foreign import
tari¤s.
The role of tari¤s in shaping international trade is hard to underestimate. The cross-country
trade models, e.g. the gravity model, rely on an aggregate bilateral tari¤ as one of the most
important factors of bilateral trade ‡ows (Anderson and van Wincoop, 2003). The recent …rmlevel trade models, e.g. the original heterogeneous-…rms framework in Melitz, 2003, also show
that variable trade costs, such as tari¤s, explicitly enter the condition which partitions …rms into
exporting and non-exporting ones. Melitz (2003) emphasizes the role of import tari¤s for both
intensive and extensive margins of trade and makes it a natural candidate for an instrument of
export market entry.
In this respect, Russia provides a unique opportunity to study this e¤ect, because of the availability of very detailed transaction-level data that allow for a …nest calculation of implied tari¤s at
the industry-year level. Each exporting …rm ships a variety of products to a variety of destinations
every year, and each of these combinations of products and destinations is subject to a speci…c
foreign tari¤. The natural question is then how to aggregate these tari¤s at the …rm level. Of
course, the actual average tari¤ paid by the …rm cannot be used as a valid instrument, because
it depends on the …rm’s choice of products and destinations every year, and is thus endogeneous
itself. To overcome this endogeneity of weights and deal with aggregation bias (Tre‡er, 2001) we
rely on the weights of the …rst year of the available sample, and this year is subsequently dropped
from the estimation. This means that the composition of products and destinations is …xed and
predetermined for the …rm.
The second complication is how to impute the tari¤ for …rms that never export in the data (as
they do not have a basket of products and destinations in any year, and thus no product-destination
tari¤s can be aggregated). A possible suggestion is to use …rm-speci…c composition of products and
destinations for …rms that ever export in the data, and some industry average for never-exporters.
This approach, however, su¤ers from …rst, being inconsistent across …rms, and second, one may still
argue that original …rm-speci…c composition of product-destinations is not completely exogenous
(even though predetermined). One cannot analyze ever-exporters only either, as this is a selected
sample (based on unobservables that determine exporting).
To overcome these empirical challenges, we propose to aggregate tari¤s using industry-speci…c
composition of products and destinations. In particular, we use the following aggregate tari¤ to
predict exporting decisions of …rms:
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T arif fst =
X
X T otalExportsjd0
X
wsjd0 T arif fjdt =
T arif fjdt ,
T
otalExport
sjd0
jd
jd
(2)
jd
where T arif fjdt is the import tari¤ on product j in destination d in year t 3 , and wsjd0 is the
industry-speci…c weight of each product-destination (which is held constant at the …rst year of
data to make it predetermined). This weight is the export value share of each particular productdestination among all product-destinations that are exported from Russia in a particular industry.4
To illustrate this calculation, consider industry "Manufacturing of cold drawing of bars " (NACE
code 24.31). In 2006 all Russian …rms in this industry exported in total $634,839 of goods, of which
$47,490 constituted export of "Semi-…nished Products of Other Alloy Steel" (HS6 product 722490)
to India, $155,587 of "Casing, tubing and drill pipe, of a kind used in drilling for oil or gas"
(HS6 product 731210) to India, $122,930 of "Stranded wire, ropes and cables, of iron or steel,
not electrically insulated" (HS6 product 730420) to Kazakhstan, $308,238 of the same product
to Ukraine, and $604 of "Magnesium raspings, turnings and granules, graded according to size;
powders" (HS6 product 810430) to the European Union (Germany). This means that the weight of
each product-destination tari¤ will be …xed over time at 7.48%, 24.51%, 19.36%, 48.55%, and 0.1%,
respectively. In this simple example, there are 5 product-destinations that are aggregated to the
industry level. Overall, the number of product-destinations per industry ranges from one to more
than …ve thousand, with a median of 47 product-destinations.
Notably, this approach treats exporting and non-exporting …rms symmetrically, suggesting that
implied tari¤ incentives to change exporting status are treated equally across …rms. It also overcomes
the potential problem of endogenous (even though potentially predetermined) …rm-speci…c weights
by using the industry-speci…c …xed composition of product-destinations. The underlying sources of
variation are both cross-sectional (due to the di¤erent intrinsic exposure of industries to di¤erent
tari¤s through the originally-exported product-destination baskets), as well as time-series (due to
foreign countries changing their import tari¤s over time).
The intuition behind this identi…cation strategy can be further illustrated by the similarity
3
These tari¤s range from 0% ad valorem on many products in many countries to 1% for importing ethers to
European Union, to 5% for railway …xtures to Colombia, to 20% for …sh products to China, and up to 500% for soya
beans to Korea and even 3000% for some fermented beverages to Egypt.
4
A similar approaches have been used by Bustos (2011), who aggregates HS9-level tari¤s to 4-digit ISIC industries
(without explicitly considering the cross-sectional heterogeneity in destination) –to estimate the e¤ect of MERCOSUR on Argentinian …rms productivity; and Lileeva and Tre‡er (2010) who use plant-speci…c production weights of
each HS6 product to estimate the e¤ect on Canadian …rms productivity (considering a single destination only).
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with a di¤erence-in-di¤erences approach. A given decrease in a foreign country import tari¤s for a
particular product a¤ects propensity to export more for those …rms that belong to industries that
intrinsically produce and export more of this product to this particular destination. Importantly,
the di¤erential industry exposures themselves are captured by industry …xed e¤ects (subsumed by
…rm …xed e¤ects in all speci…cations). This identi…cation relies on the assumption that foreign
import tari¤s a¤ect debt of local …rms only through their industry-speci…c propensity to export
more to countries where the tari¤s have been reduced. Since this aggregate tari¤ variable combines
predetermined …rm eligibility (de…ned by the historic export basket of the industry it belongs to;
to be …ltered out by …rm …xed e¤ects) with the variation in foreign import tari¤s that is orthogonal
to …rms (conditional on year …xed e¤ects), it is arguably exogenous for the …rm. This makes this
variable uncorrelated to current …rm-speci…c shocks and validates its use as an instrument.
3
The E¤ect of Exporting on Capital Structure
In this section we apply the estimation strategy outlined in Section 2 to study the e¤ect of employment ‡exibility on capital structure.
3.1
Main Results
Table II shows the OLS and IV-2SLS estimates of the coe¢ cient of interest in di¤erent speci…cations.
The standard errors throughout the paper are two-way clustered at the …rm and industry-year levels,
so that all statistics are robust to heteroskedasticity and arbitrary within-…rm and within-industryyear correlation (which could potentially arise because the instrument is measured at this level of
aggregation).5
The …rst two columns report the results of estimating a panel …xed-e¤ects speci…cation. The
coe¢ cient at …rm Export status in column 1 is highly insigni…cant and e¤ectively equal to zero.
This result is consistent with our expectations, since the simple OLS regression does not account
for endogeneity of selection into being an exporter. In particular, if …rms optimally choose to
enter the foreign market as a response to a higher volatility in the domestic market, then in the
optimum the overall cash ‡ow volatility should be similar across exporting and non-exporting …rms.
Column 2 adds several other …rm-level control variables that have been identi…ed in the literature
5
The two-way clustered standard errors, proposed by Cameron, Gelbach and Miller (2006) and Thompson (2011),
were obtained using the Scha¤er (2010) xtivreg2 command in STATA.
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as determinants of capital structure: pro…tability and tangibility6 . As expected, the coe¢ cient of
interest stays insigni…cant and very small.
Although these speci…cations already pick up both all time-invariant di¤erences across …rms
and the e¤ects of size, pro…tability and tangibility, as we argue, exporting and capital structure
are still likely to be chosen endogenously due to within-…rm changes in domestic market volatility,
…nancial constraints, and productivity shocks. To identify the causal e¤ect we exploit the exogenous
variation induced by the foreign import tari¤s and report the IV-2SLS results in columns 3 to 6.
Columns 3 and 5 report the results of regressing the export dummy on the aggregate tari¤
instrument, size, …rm and year …xed e¤ects, and additional …rm-level controls (in column 5). These
regressions correspond to the …rst stage of the IV-2SLS estimation of (1) and are given by
Exportit =
The estimate of
t
+ T arif fit + Xit0 +
i
+
it ;
(3)
in column 3 has a p-value of 5.3% and shows that an average foreign tari¤
reduction of 10 percentage points increases the propensity to export by 0.26 percentage points,
which is about a tenth of the average export propensity across Russian …rms. Since the variation
in the foreign import tari¤s is reasonably exogenous conditional on the …rm and year …xed e¤ects,
the change in the propensity to export can be entirely attributed to changes in the average foreign
tari¤. For each …rst-stage speci…cation throughout the paper, we also report the weak-identi…cation
Kleibergen and Paap (2006) F-statistic.
Although the use of …rm-level controls is not necessary with this identi…cation strategy, it helps
in corroborating the exclusion restriction and can add more power to the estimation. In column
5, we add a range of …rm-level control variables to account for time-varying …rm-speci…c shocks.
The estimate of
remains similar and is signi…cant at 5% level, suggesting that the instrument is
uncorrelated with the range of included variables.
The coe¢ cients in columns 4 and 6 report the corresponding second-stage IV-2SLS estimates of
. As outlined in Section 2.3, the presence of unobserved …rm-speci…c shocks that can be correlated
with exporting and choice of …nancing would bias the OLS estimate of . We …nd that the magnitude
of the IV-2SLS estimates is substantively larger. The preferred estimate of
in column 4 (5.409
with a standard deviation of 2.655) means that increasing the propensity to export by 3 percentage
points (the average in the sample) would lead to the increase in total debt-to-assets ratio by 15
6
Another common determinant of capital structure – the R&D expenditures – are not well reported in the
database, hence we opted to exlude it from the analysis to keep the number of observations.
11
percentage points or about the average of this variable. This result is economically and statistically
signi…cant. Finally, in column 6 we add …rm control variables, and the resulting estimate is also
statistically signi…cant and similar in magnitude.
To complement our main result we also look at di¤erent margins of debt. In particular we
perform the IV-2SLS estimations on short- and long-term debts separately. Table III reports results
of the second stage of these estimations. Columns 1 and 3 report the results with size as a control
variables, while columns 2 and 4 add other …rm-level controls.
From this table we see that the main results are driven mainly by the changes in short-term
debt, rather than long-term debt. Although the latter is only marginally insigni…cant (the p-value
is abour 12%), the economic magnitude of the coe¢ cient is about 4 times smaller than the one for
the short-term debt. We thus conjecture that exporting does not lead to a substitution between
long-term and short-term debts, and that primarily short-term borrowing reacts to the entry into
foreign market.
4
Concluding Remarks
The interplay between …rm’s international strategy and …nancial structure, while being an important
subject of business literature for more than half a century, has not yet gained enough attention in
economics and …nance research. However, it might be very bene…cial to incorporate exporting
into product-market strategies and evaluate them jointly with …nancing decisions, since it allows
broadening the scope of available information about the …rm, and bringing new means to explore
causal inferences.
An analysis of Russian customs data allows us to measure the changes in international strategy
of the …rm, which we interpret as a sign of a change in product-market ‡exibility. In addition, the
intertemporal and cross-sectoral variation in trade policies of foreign countries allows us to evaluate
the causal e¤ect of …rm’s operation ‡exibility on its capital structure in a quasi-experiment setting.
We show that an increase in a …rm’s exporting leads to a large increase in its short-term debt
…nancing.
As far as the risk exposure of exporting …rms is concerned, we interpret our results as an
indication that exporting driven by external shocks results in less volatile cash ‡ow than domestic
market operations. While this result supports the diversi…cation bene…ts of exporting it also raises
an important question –how large are in fact the currency risks brought by exporting. In particuar,
12
if exporting is less riskier than domestic operations, as we …nd in the paper, the foreign currency
of revenues may actually support the lower cash ‡ow volatility, rather than increase it – through
matching the foreign currency of revenues with the one of costs. One of the plausible ways to
investigate this question would be to explore for the currency composition of the …rm’s costs.
Namely, we expect that our results will become even stronger for a subsample of Russian …rms that
import inputs from abroad. For these …rms exogenous increase exporting may bring even more
diversi…cation bene…ts and result in an even lower cash ‡ow volatility. Given the availability of the
dataset of customs import transactions, we plan to explore this issue further.
From the applied perspective, our results provide new evidence on the complementarity of CEOs
and CFOs decision-making. From the policy perspective, it suggests that exogenous changes in trade
policies aimed at export promotion and export diversi…cation at …rm level will have important consequences for their …nancing decisions as well. Additionally, in a setting with imperfect capital
structure adjustment, this may have implications for which …rms are less likely to survive in the
long run and for industry allocative e¢ ciency; with quantitative predictions achieved through incorporating frictions into a joint model of a …rm’s organizational structure and corporate …nancing,
potentially in a general equilibrium framework.
13
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16
Table I. Descriptive Statistics
Notes: The sample includes all firms from Ruslana database (2007-2011) that have at least 5 employees
during this period and a non-missing industry identifier. Total Assets is book value of total assets of the firm.
Total Debt / Total Assets is the ratio of total debt (which is the sum of short-term and long-term debt) to
total assets. Export is a dummy variable that is equal to 1 in the year the firm exports any goods abroad.
EverExport is a dummy variable that is equal to 1 if the firm ever exports any goods in the sample. Tariff is
the industry-year aggregate tariff, defined in Section 2.3, measured in decimal points. Size is the natural
logarithm of firm's total assets. Profitability is the ratio of gross profit to total assets of the firm. Tangibility
is the share of net fixed assets in total assets. All firm-level variables are winsorized at 1% and 99% tails.
Variable
Mean
Std. deviation
N
Capital Structure:
Total Assets ($mln)
Short-term Debt / Total Assets
Long-term Debt / Total Assets
Total Debt / Total Assets
2.839
0.096
0.060
0.154
9.264
0.218
0.182
0.274
1,386,206
1,386,206
1,386,206
1,386,206
Exporting:
Export
EverExport
Tariff
0.029
0.053
0.016
0.167
0.224
0.030
1,386,206
1,386,206
1,331,447
Additional Variables:
Size
Profitability
Tangibility
5.411
0.192
0.603
2.399
0.261
1.150
1,386,129
1,386,206
1,386,188
17
18
Number of firms
Observations
F-statistic
Size control
Other firm-level controls
Firm FE
Year FE
Tariff
Export
307,922
1,401,996
Yes
No
Yes
Yes
-0.000572
(0.00169)
1
Yes
Yes
Yes
Yes
-0.00236
(0.00170)
2
307,922
1,401,996
OLS
292,241
1,331,367
3.749
Yes
No
Yes
Yes
-0.0255*
(0.0132)
First Stage
3
292,241
1,331,367
Yes
No
Yes
Yes
5.409**
(2.655)
Second Stage
4
292,241
1,331,349
3.884
Yes
Yes
Yes
Yes
-0.0257**
(0.0131)
First Stage
5
IV-2SLS
292,241
1,331,349
Yes
Yes
Yes
Yes
5.199**
(2.475)
Second Stage
6
where Dit is the leverage (ratio of total debt to assets) of firm i in year t , Exportit is the dummy variable that equals 1 if the
firm exports this year, and 0 otherwise, αt are year fixed effects, ηi are firm fixed effects, and Xit are firm-level controls (size in all specifications, and profitability and tangibility - in specifications 2, 5, and 6). Tariff is the industry-year aggregate tariff,
defined in Section 2.3, measured in decimal points. Standard errors are two-way clustered at the industry-year and firm levels
and are reported below the coefficients. The first year of the data (2006) is dropped from all regressions. The number of firms
and observations excludes singletons. * indicates 10% significance; ** 5% significance; *** 1% significance.
Dit=αt+βExportit+Xit′γ+ηi+εit ,
This table reports the results of estimating the following specification using the OLS and IV-2SLS frameworks:
Table II. Capital Structure and Exporting: Main Results
Table III. Capital Structure and Exporting: Short-term vs Long-term Debt
This table reports the results of estimating the following specification using the OLS and IV-2SLS frameworks:
Dit=αt+βExportit+Xit′γ+ηi+εit ,
where Dit are different measures of leverage (ratio of short-term debt to assets and ratio of long-term debt to
assets) of firm i in year t , Exportit is the dummy variable that equals 1 if the firm exports this year, and 0
otherwise, αt are year fixed effects, ηi are firm fixed effects, and Xit are firm-level controls (size - in all
specifications, and profitability and tangibility - in specifications 2 and 4). Tariff is the industry-year aggregate
tariff, defined in Section 2.3, measured in decimal points. Standard errors are two-way clustered at the industryyear and firm levels and are reported below the coefficients. The first year of the data (2006) is dropped from all
regressions. The number of firms and observations excludes singletons. * indicates 10% significance; ** 5%
significance; *** 1% significance.
IV-2SLS
ST Debt / Assets
Export
Size control
Other firm-level controls
Firm FE
Year FE
Number of firms
Observations
LT Debt / Assets
1
2
3
4
4.317**
(1.833)
4.177**
(1.689)
1.445
(1.228)
1.301
(1.174)
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
292,241
1,331,367
292,241
1,331,349
292,241
1,331,367
292,241
1,331,349
19