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Effect of Capital Structure on Firms' Product Market Performance: Empirical Evidence fromIndian ManufacturingAuthor(s): Arindam BandyopadhyaySource: Economic and Political Weekly, Vol. 40, No. 9 (Feb. 26 - Mar. 4, 2005), pp. 866-876Published by: Economic and Political WeeklyStable URL: http://www.jstor.org/stable/4416277.
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E f f e c t
o apital
Structure
o
F i r m s
Product
Market
Performance
Empirical
Evidence
r o m n d i a n
anufacturing
This
paper
empirically
examines the
effect of
a
firmn's
apital
structure
on its
product
market
outcome.
The
strategic
consideration
in the
product
market
may
induce Indian
corporates
to
take on
higher
debt
in order to
gain
strategic advantage. Using
a
balanced
panel
of
538
Indian
manufacturing
irmns
ver
the
period
1989
to
2000,
the
paper
studies
the
relationship
between short-
and
long-term
debt and sales
performance
(export
as well
as
total
sales).
In the
case
of long-term
debt,
firms
take
time to build their
infrastructure.
Hence,
considering
a
longer
time
horizon
of
two
years
and
seven
years
of
taking
the
loan,
the
paper
finds
that
long-term
debt
boosts
sales
growth
of firms belonging
to the
top
50 and
large
business
houses.
However,
long-term
debt
is
inconsequential
or
total
growth of
sales
for
smaller
group
and
unaffiliated irms.
The
study
finds empirical
evidence
on
the
interplay
between the
financial structureand product marketperformancein the Indian corporatesector.
ARINDAMBANDYOPADHYAY
Hypotheses
U ntil the late
1980s,
the
corporate
inance iterature as
ignored
the interactionbetween
capital
structure nd
firm's
product
marketdecisions.The lack of interest n
these
output-related
ecisions s due to the 'irrelevance
ropo-
sition' of finance
theory.
This
proposition,
known as the
Modigliani-Miller1958)
theorem,
postulates
hat he choice
of
a firm's
capital
structure
i
e,
its
financing
decisionof
holding
debt
or
equity)
s irrelevant
or its value.In
many
circumstances,
where he
product
market
s
imperfectly ompetitive
and firms
have some
market
power,
the irrelevancewould be
brokenand
financial tructure nd
output
marketdecisionswould
be inter-
related.Current
esearch
n
corporate
inance
has
begun
pointing
to
a
direct
nterrelation etween
he financialand realdecisions
of firms. That interrelation omes
from the role of financial
instruments
n
conveying
nformationo investors s well
as
the
product
market ivalsandconsumers.
The
literature
tresses hat
a
firm's
mode of
financing
nfluencesboth the firm's conduct
in the
product
marketas
well as
the conduct
of
other market
participants,hereby nfluencing ompetitive
outcomes.Harris
andRaviv
(1991)
make his
point
anddiscussrecent heoretical
work which model
product
market
and
capital
structurenter-
actions.
Ravid
1988)
also
surveys
he iterature
n
the nteraction
between
capital
structure
nd
product
market
decisions. Both
these
surveys dentify
two
types
of
interactions:
he
effect
on
a firm's
product
market
trategy
nd heeffecton
product
hoice.
Titman
1984)
and Maksimovic
and Titman
1991)
show how
the
capital
tructure
an affecta firm's
choice
of
product uality
and
the
viability
of its
product's
warranties.
Thus,
the
capital
structure an
alter
a
firm's
ability
to
compete
in the
product
market.Recent
empirical
vidence on how
financing
decisions
affect
product
market
competition among
firms
has
further
stimulated nterest n the area.
In
theory,
here
are
two
schools
of
thought
n the
interaction
between irm
product
market
trategy
nd ts
financing
hoices.
One believes
thata firm'sdebt issue
can lead to stiffer com-
petition
n the
product
market
y raising
ts
output
n a
strategic
way.
Brander ndLewis
1986
and
1988)
andMaksimovic
1988)
in their
pioneering
work
analyse
how debt
financing
commits
a firm
o
a
more
aggressive utput
tance n the
product
market.
Rotemberg
nd
Scharfstein
1990)
and Bolton
and Scharfstein
(1990)
also
predict
hat
increaseddebt
will
lead to increased
output
t the
firm evel
and
atthe
ndustry
eveland
hereby
make
the
competition
tiffer.
Another ine of
argument,put
forth
by
Telser
(1966)
and
extended
by
Bolton and
Scharfstein
1990)
suggests
that too
much
dependence
n
outside
inancing
hindersa firm's
ability
to
compete,promptingndustry
ivals o
pursue redatory
market
strategies.
Chavelier nd
Scharfstein
1996)
propose
hat
exter-
nally
financed irms nvest
ess in market
hare
buildingduring
recessions,
aisingprice
cost
margins
o boost
short-term
rofits
at
the
expense
of
locked-in
customers.The other more recent
papers
by
Showalter
1995),
Dasgupta
and
Titman
1998)
and
Grimaud
2000)
have
shown
thatdebt leads to
weaker
compe-
tition
n
the
output
market
y
helping
irms
colludeand
ncrease
their
prices
while
cuttingoutput.
The limited
amount
of
empirical
iterature
nvestigating
he
links between
capital
structure
nd
product
market
decisions
consists of
the
following
papers:
Showalter
1999)
studies he
strategic
use of debt in US
manufacturing
ndustries.Based
on
his own
theoreticalwork
Showalter
1995)
and
that
of Brander
andLewis
(1986),
he
regresses
he
debt
ratios
of
manufacturing
firms on
variables
pproximating
emandand
cost
uncertainty
as
well
as
a
set of control
variables.He finds
significant
ela-
tionships
between he
uncertainty
measures nd
firms
everage,
and can
support
he
hypothesis
of
strategic
use
of debt.
Chevalier
1995)
considers he
supermarket
ndustry
nthe
US
during
he
late 1980s. In an
event
study analysis,
she
finds the
866
EconomicandPolitical
Weekly
Febraury
6,
2005
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announcement
of
a
leveraged
buyout
within the
industry
to
increase
the
expected profit
of rivals.
However,
leveraged buyouts
encourage
local
entry
and
expansion
by
rivals. Her results
suggest
a
higher leverage
to soften
product
market
competition.
These
findings
contrast with the
qualitative
results
found
in the
theo-
retical literature
following
Brander and
Lewis
(1986).
Opler
and Titman
(1994)
find that
during
industry
downturns
highly
levered
firms are
the
most vulnerable.
They
find that
firms
with
higher
levels
of
debt lose more sales
and market share than
their more
conservatively
financed
competitors.
Thus,
these
empirical
results seem to be at odds with the
predictions
of
the
theoretical
models.
Philips
(1995)
also
provides
evidence
that
financial
leverage
interacts
with
product
market
competition.
In
his
intra-industryanalysis
about US
markets,
he tests whether
industry
output
is affected
by
changes
in firms'
capital
structure.
He finds
a
change
in
the
firms'
marketshare
following
an increase
in
financial
leverage. Depending
on
certain
industry
character-
istics, however,
this effect
goes
in
opposite
directions.
This
study
shows the
importance
of
including industry specific
character-
istics both
on
the
supply
and demand side to
understand he firms'
capital
structuredecisions.
Hellmann and Puri
(1999)
investigate
the
relationship
between the
type
of
capital
thatnew firms choose
to finance their
projects
and their
product
market
strategies,
and
the
corresponding
market outcomes.
Their work is based
on
a
unique
data set of
173
start-upcompanies
in
California's
Silicon
Valley. They
find firms
pursuing
innovator
strategies
being
more
likely
to use venture
capital financing.
Also,
they
are much faster
in
bringing
new
products
on the market than imitator firms.
As one
can
see,
the
relevant
empirical
works
focusing
on
capital
structure
and
product
market
interactions are limited
to
US
firms
operating
in
various
industries
in
various cities of US. The
informational
problem
s much more
severe
for
developing
country
firms
(like
Indian
firms)
competing
in the
product
market. Das
and
Bandyopadhyay
(2003)
and
Bandyopadhyay
and Das
(forthcoming) provide empirical
evidence about the
linkage
between the firm's financing decisions and real marketperfor-
mance for Indian irms
operating
n
foreign
and domestic markets.
They
find that the
issuance
of commercial
papers
or debentures
by
firms leads
to better
performance
in
the
product
market;
while
it
directly impact
domestic sales of the
firms,
it also
acts as a
signal
and stimulates its
foreign
sales.
The
empirical
literatureon interactions
between
firm
financing
and
product
market
performance generally
seeks to
determine
whether debt
financing
either
hurts or boosts
competitive
per-
formance. Short-term
and
long-term
debt contracts involve trade-
offs
for
entrepreneurs.
Short-termdebt
comes
with
lower interest
charges
attached.
However,
an
entrepreneurmustgenerally roduce
positive
results within a
year
or two. If
these results
don't
materialise, the entrepreneurmay default on the loan and there
may
be a
shift
in
control to
the investors. In
this
case,
short-
term
debt
is
a
powerful
disciplining
device for
good
firms,
because
it
gives
control back to the investor.
Therefore,
the short-
term
forces
an
entrepreneur
to abandon his or her
dream if
the
business is
realistically
doomed to failure. It is
a
way
of
com-
mitting
the
entrepreneur
o a realistic
viewpoint.
Thus,
short-term
leverage provides
a firm with an
incentive to
perform
better
in
the
product
market
[Poitevin
1989].
In
contrast,
long-term
debt
gives
the
entrepreneur
more time
to make his or
her
company
successful
and
pay
back
the
debt,
with the
trade-off
being higher
interest
payments.
Therefore,
firms
taking long-term
debt take
time to
build
up
their
nfrastructure,
&D,
marketing
hannels
and distribution
etworks o
gain
strategic
advantage
ver
the
longer
period
of time.
Myiwork
proposes
hat
debt
financing
must have
a
positive
influence
on
firm's
competitiveperformance
n the
product
market.To
motivate
his
claim,
I
empirically
xamine he rela-
tionship
between hort-and
long-term
orporate
ebt and
sales
performanceexport
s well as total
ales)
using
Indianirm
evel
data roma
panel
of 533 firmsover 12
years.
However,
a
study
of
competitive
performance
ollowing
capital
tructure
hanges
may
sufferfrom a
potentialendogeneity
problem
whichfirst
needs to be taken care of. Anotherconcern s there
may
be
unobserved
actors
arising
rom the market nvironment
which
may
jointly
influenceboth a firm's financialstructure nd its
competitive
performance.
One
way
to
mitigate
concerns
about
the
endogenous
nature f the
relationbetween
capital
tructure
and
product
market
performance
s to look at the
real
market
performance hanges
following
the
changes
in financialdeci-
sions.
Accordingly,
we
perform
ome univariate
arametric
nd
non-parametric
eststo
examine he
consequences
f
taking
both
short-and
long-term
ebt on
variousreal
market ariables
ike
exports,
otal
sales,
advertising
ntensity,
marketing
ntensity,
R&D
ntensity
and
distribution
ntensity.
These univariateests
will tell us how firms(if
they
are
actually)
an use theirshort-
and
long-term
oans
to
gain
strategicadvantage
n
the
product
market.
Next,
in
my
multivariate
nalysis
of
product
market
impact
of
long-term
debt,
I use
lag
structures o
mitigate
he
simultaneity
problems.
The
unobserved irm
specific
factors
which
may
disturbheerror tructure
within
irm
autocorrelation)
are
being
corrected n
my
panel
Tobit model
by incorporating
21
industry
dummies and three
group
dummies.
The
yearly
changes
are
also
being captured y
taking
12
yearly
dummies.
Similarly,
n
studying
hestatistical
ignificance
of interactions
between
irms'
hort-term
ebt
inancing
nd
exportperformance
I
apply
a
two-step
GMM
estimation
method o take
care of
the
possible
endogeneity
of
short-term
everage.
This paper s organisedas follows. In Section II, I briefly
discuss the
corporate
inancingpattern
n
India
during
he
post-
reform
eriod.
Here,
also talk
about
developments
n the
Indian
financial
market,
which
provides
a
background
o
my study.
Section III
discusses
the
data,
summary
tatistics
and variable
construction
methods.
There,
also ookat
the
rends n
financing
pattern
f
my
sample
irms
over he
period
1989-2000.Section V
contains he
methodology
f
different
conometric ests and
the
main
mpirical
esults
f the
chapter.
Using
univariate
arametric
and
non-parametric
estsand
multivariate
anel
Tobit
and
GMM
regressions,
have
madean
attempt
o
find
empirical
vidence
of the
effects
of
capital
tructuren
product
markets
ith
particular
reference o the
Indian
orporate
ector.
In
Section
V,
I discuss
the major indingsof my empirical ests andconclude.
II
Indian
Financial
Sector:
Some
Relevant
ssues
Corporate
Financing
Pattern
in
India
India
has
historically
olloweda
financial
ntermediary
ased
system,
where
banks and
financial
intermediaries
layed
a
dominant ole.
The
corporate
inancing
pattern
n
India
ndicates
that,
on
average,
nternal
ources
constitute
about
one-third
f
total
sourcesof
funds,
while
external
ources
account
or
the
rest. As far
as the
RBI
Report,
Trend
and
Progress
of
Banking
Economicand Political
Weekly
February
6,
2005
867
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in India2001-02
is
concerned,
he shareof
borrowings
n
total
sources
all
sources
f finance rom he
capital
market)
as
moved
inversely
with
equity
inancing
n the
post-reform
eriod
1991).
The main
sources
of
long-term
ebt
are
development
inance
by
the all-Indiaand state financial
institutions
DFIs),
who lend
mainly
for investment
n
the
priority
sector,
and
debentures
(cumulative
and
non-cumulative),
which is a
capital
market
instrument.
Short-term
inancing,mainly
or
working
apitalpurposes,
s
usuallyprovided y
thecommercial anksas a mix of cashcredit
andbills
discounting
acilities.
Commercial
aper
CP)
came
nto
existence
ollowing
a
RBI notification n 1990 as a new short-
termdebt nstrument. Ps
usually
has
a
maturity
f 90
days.
CPs
can
also be issuedfor
maturity eriods
of 180 and one
year
but
the most active market
s
for 90
day
CPs.
During
he
early
1990s,
the Indian inancialsector was un-
dergoing mportant
hanges.
The
banking
ector
reforms,
with
the
publication
f
the
Narasimham
ommittee
Report,
imedat
increasing
he
profitability
nd
efficiency
of
the then
27
public
sectorbanks.
Entry eregulation
as
accompanied y
progressive
deregulation
f
interest
ateson
deposits
and
advances,
eduction
of
reserve
equirements
nd emoval f credit llocation.
trength-
ening inancialystemshasbeenone of thecentral ssuesbecause
sound inancial
ystems
drive
competitive
fficiency
n the real
sectors
of
the
economy.
Thus,
he
principal bjective
of financial
sectorreformwas to
improve
mobilisation f
financial
avings,
putting
hem to
productive
use,
transforming
arious
risks
and
accelerating
he
growthprocess
of the real sector
by removing
structural eficiencies
affecting
the
performance
f
financial
institutions
nd financialmarkets.
From
October
994,
nterest ateswere
deregulated
n
a
phased
manner
nd
by
October
1997,
banks
were
allowed
o
set interest
rates
on all term
deposits
of
maturity
f
more han30
days
and
on
all advances
xceeding
Rs
2,00,000.
Three
major
redit
ating
agencies
hadbeen set
up
by
the
early
1990s.1 The
Security
and
ExchangeBoardof India(SEBI)was given regulatory owers
in
1992 o oversee
he
inancial
markets nda new
stock
exchange
was
set
up
in
1994
(National
Stock
Exchange).
Withthis back-
ground,
will examine
whether,
n
the
post-reform
eriod,
both
the
short-
and
ong-term
debt
can contribute o
promoting
or-
porateperformance
n
the
product
markets.
III
Research
Design
Proxies
for
Product
Market
Performance
In
examining
he
link
betweena firm's
product
market
per-
formance ndcapital tructure, reviousempirical esearch as
often linked
price-setting
ahaviour
with some
aspect
of debt
financing
o
reflect
how
a
firm's
financial status
affects its
competitive
bahaviour
e
g,
Chavelier
1995;
Phillips
1995;
Chavelier nd
Scharfstein
996].
However,
irmscan
mplement
a
number
f
alternative
olicies
that
significantly
ffect
product
market
utcomes ut hat
may
notbe reflected n
how
they
choose
to
price
their
products.
Examples
of
such
policies
are decisions
about
ixed
nvestments,
esearch
nd
developmentxpenditure,
advertising,
romotion
nd
distribution
ctivities.One
way
to
build a
practical
measureof
performance
hat
summarisesn-
formation
rom he
combined ffectsof
pricing
nd
other
product
market
trategies
s to look at
the firm'stotal sales
growth
n
general. imilarly,
nthe
oreign
market,
more
practical
measure
wouldbeto look atthe
changes
n its
export
hare
as
proportion
of
total
ales.I
use
a firm's otal
sales
growth
t thecurrent
eriod
to
gauge
its
performance
n
the
product
market
n
general,
and
export ntensity
o sales o determine
ts
performance
n the
export
market.
Proxies
for
Capital
Structure
Capital
tructures defined
by
short-and
ong-term everage.
Short-term
everage
s the ratioof short-term ebt o total
assets.
Long-termeverage
s
the ratio
of
long-term
ebt o total
assets.
Total
debt
is
total
borrowings
f
firms.
In
Prowess,
otal bor-
rowings
ncludeall
forms
of debt-interest
earing
or otherwise.
All
securedand unsecured ebt
is included
under
borrowings.
Thus,
borrowings
ncludedebt rom
banks
both
hort-
nd
ong-
term)
nd
inancial
nstitutions,
nter-corporate
oans,
ixed
deposits
from
public
and
directors,
oreign
oans,
oans rom
government,
etc. Fundsfrom the
capital
market
hrough
he issue of
debt
instrumentsuch as
debentures
both
convertible nd non-con-
vertible)
andcommercial
paper
are also
includedhere.
I
define short-term
ebt as
the
loans
of short
maturity
f less
thanone year.Accordingly takeshort-term ankborrowings
since
they
have
a
maturity
f
less thana
year.
I
have also
added
commercial
aper,
which
s a
relatively
new
type
of debt nstru-
ment
through
which
corporates
ource their
short-term und
requirements.
he current
portion
of
long-term
debt
is also
included n
generating
he short-term
ebt variable.This
is
the
amount f
long-term
ebtdue
for
repayment
within 12 months.
It
measures
he
funds
needed or
repayment
f debt n
the near
future.
Long-term
ebtsare hose oans
having
a
maturity
f
more han
one
year.
subtract
hort-term ebt rom
otal
debt o
derive
ong-
term
debt. In
Appendix
A,
I discuss
in detail
the
construction
of
these
financial
variables.
Control
Variables
Profitabi
ity
and
nvestmentanbe
determinants
f sales
growth
and
export
growth
and
may
be correlated
with
everage.
There-
fore,
one
shouldcontrol
or
profitability
n
any
empirical
model
designed
o
measure
he effect of
debton
sales or
exportgrowth.
Similarly,
everage
oefficients
may
be
biased f themodel ails
to
control
for
investment
spending,
which
might
have been
financedwith
debt.
Throughout
his
chapter,
irm
profitability
(proxiedby
cash
profit
over
total
assets),
investment
proxied
by growth
n
fixed
assets,
net of
revalued
eserves
over
assets),
and
ize
(proxied y
natural
og
of
total
assets)
are
usedas
controls
in regressionof sales growthor exportgrowthon bothshort-
and
long-term
everage.
Data
Description,
Variable
Construction
and
Summary
Statistics
The
dataare
retrieved
rom
he
Prowess
database
rovided
y
the
Centre
or
Monitoring
he
Indian
Economy
CMIE).
Firms,
which
were
dropped,
nclude irms
without
he
basic
data rom
1989 to
2000 and
without
any
industry
category.
Further,
droppedmany
firms
with
zero
wages
and
salaries but
with
positive
sales. All
these
corrections
esulted n
a final
sample
of 538
firms.
This final
sample
ncludes
242
independent
nd
868
Economic and
Political
Weekly
Febraury
26,
2005
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5/12
small
group
firms and
296
top
50 and
large
business
group-
affiliated irms.
Table 1 shows
the
comparison
between
top
50 and
large
business
houseswiththeir
private
tand-alone
ounterparts.
he
top
and
arge
business
group
irmson
average
are
bigger
n
size
(in
terms f total
ales
and otal
assets)
n
comparison
withsmaller
group
nd
private
tand-alone
irms.Both he
parametric
nivariate
t-test
and
non-parametric
ank
um
est
confirm hat he
difference
in size is
significant.
here
s
also
significant
ifference
n
average
sales growthdistribution etweentwo categoriesof firms.
From
Table
1,
one can also see that herearesome
remarkable
differences
n the
composition
f
corporate
ebt n
capital
truc-
turebetween
group
affiliationsand
stand-alone irms.The
top
group
irmson
average
are more
dependent
n
long-term
oan
compared
o the smaller
group
irms.
On
the
other
hand,
maller
group
and
private
irmshave more
everage
owards hort-term
Table
1:
Descriptive
Statistics:
Average
Comparison
between
Top
50 Business
Group
vs
Non-top
50 and
Private
Stand-alone Firms
(Units
n Rs
Million,
thers in
numbers)
All
irms
Top
50
Non-top
50
t statistics
Business Business for
Group Group
Difference
Firms Firms
Panel
A:
mean
difference
Export
ales ratio
in
per
cent)
7.3
6.52
8.26
-4.7'**
Annual
ales
growth
at
time
t
0.19
0.19
0.19
0.07
SHORTLEVa 0.15
0.13
0.17 -12.42**
LTLEVb
0.26 0.28 0.24 7.92***
INVEST 0.1 0.11
0.95 3.21***
PROFITABILITY@
0.06 0.07
0.05 5.73***
LNASSETS#
6.65 7.28 5.88 42.49***
LNSALES# 6.64 7.24
5.88
42.17***
Wilcoxon
z-statistics
for
difference
in
distribution
Panel
B:
median
difference
Export
ales
ratio
(in
per
cent)
1.53 2.06 0.78
8.67***
Annual
ales
growth
at time t 0.135
0.14
0.13 2.34***
SHORTLEV
0.126 0.114 0.147
-11.2**
LT_LEV
0.23 0.24 0.2
9.27***
INVEST
0.06
0.07
0.05
8.27***
PROFITABILITY
0.07
0.07
0.06
5.7***
LNASSETS#
6.59 7.24
5.81
38.63***
LNSALES#
6.59
7.27
5.93
33.9***
Notes:
z-statistic enotes the
outcomeof a Wilcoxon
ank-sum
Mann-Whitney)
test of
equality
of
distribution etween series.
a
Short-term
everage
measures
the
short-term ebtofa
company
as
a fraction
f
ts
otal
ssets. Short-termebtconsistsofshort-termank
borrowing
commercial
aper
oan+
current
ortion
f
long-term
ebt.
b
Long-term
everage
measures a firm's
ndebtness
towards
ong-
termdebt as proportion f its totalassets. Long-term ebt is total
borrowing
short-term
ebt.
$
Annual nvestment s
proportion
f Assets:
change
in
gross
fixed
assets
(one
period
ag)
net of
revalued
reserves
over totalassets.
@
Annual
rofitability:
ash
Profit/Total
ssets. Cash
profit
smeasured
as net
profit
+
depreciation+
mortisation.
#
LNSALES
s
natural
og
of total
sales;
LNASSETS
s
natural
og
of
total
assets.
We have taken either
of therm
s
proxy
or firm
ize
(FSIZE).
otal
Assets
include ixed
assets,
investments,
and
current
assets.
c
The observations
are
separated
into
top
50
business and
large
business
group
affiliated
nd their
non-top
50
groupcounterparts.
The
Wilcoxon
Rank-sum
est
is
a
non-parametric
est.
The null
hypothesis
s
that
variables
n
both
groups
are
from
populations
with
the
same distribution nd
the same
medians.
***,
*
denote
significance
at the
5
per
cent or
better,
5-10
per
cent-
level,
respectively.
loans.
The
averageprofitability
nd investment
are
higher
or
top groupcompared
o
privategroup
firms.
Table 2
shows that the most
remarkable ifferencebetween
the
op
group
nd
tand-aloneirms s themuch
higher
oefficient
of variation f
export ntensity
and
growth
of sales for
smaller
group
and
private
tand-aloneirms.
Therefore,
need o
control
for
group
affiliation
n
assessing
firm's
performance.
Table 3
gives
a correlationmatrixof the main
variables
or
the different
groups
of firms.
For
both the
top
group
and non-
topgroup irms,short-termeverage s positivelyrelated o the
export-sales
ratio.
Similarly,
ong-term
everage
is
positively
related
o
growth
of
sales. This
is
the first ndication hat irms'
capital
structure s related to
real market
performance.
The
profitability
s
inversely
relatedwith both
short-and
long-term
leverage
mplying
hat firms
with
good growthprospects
will
exhaust heir
nternal
ources
of
funds
before
soliciting
outside
financing.
However,
n
all the cases the correlation
oefficients
between
ndependent
ariables re not
high enough
which
may
cause
multicollinearity
hen
I take hem
ogether
s
regressors.
In
every regression,
I
have
checked the
correlations
among
independent
ariables
long
with nstrumentsnd he tests
reject
the
presence
of
multicollinearity
roblem.
In Table4, my samplefirms have been classified under21
industry ategoriesaccording
o their
business
activities.
Here,
I
have harmonised
CMIE
ndustry
ategories
with
NIC
2
digit
industryategory
or
ndustry
lassification. he able lso
displays
thenumber
f
firms
under
ach
ndustry
ategory.
Later control
for
industry
ffects in
assessing
the
role
of
long-term
debt
on
firm
performance.
Trends in
Financing
during
1989-2000
InTables5 and
6,
I
record ertain
rends
n
corporateinancing
over
the
sample
period.
In
Table
5,
I
look
at
the
long-term
financing
rends etween
he
periods
1989 o
1995
and
hen
1995
Table 2:
Sample
Descriptive
Statistics
(Units
n
Rs
Million,
thers
n
numbers)
Mean
Std Dev
CVa Min
Max
All irms
EXPSLRP
7.3 14.76
2.02
0
100
GRSALES
0.2
0.79
3.95 -1
30.53
SHORTLEV
0.15 0.14
0.93 0
2.02
LT_LEV
0.26 0.22
0.85
-0.89
3.57
INVEST
0.1
0.19
1.9 -4.61
3.57
PROFITABILITY
0.06 0.11
1.83
-2.51 1.53
LNASSETS
6.65
1.48
0.22
1.82
12.59
LNSALES
6.64 1.44
0.22 0
12.22
Top
50 and
large
business
group
EXPSLRP
6.52
10.78 1.65
0
100
GRSALES
0.19
0.54
2.84 -1
15.71
SHORTLEV 0.13 0.11
0.85 0
1.71
LT_LEV
0.28
0.24
0.86
-0.51 3.57
INVEST
0.11
0.19 1.73
-1.1
3.57
PROFITABILITY
0.07
0.11
1.57
-1.65
1.53
LNASSETS 7.28
1.43
0.2 3.14
12.59
LNSALES 7.24
1.36
0.19 0
12.22
Non-top
50
and
private
tand-alone
irms
EXPSLRP
8.26
18.49
2.24
0
100
GRSALES
0.19
1.01
5.32 -1
30.53
SHORTLEV
0.17
0.16
0.94
0 2.02
LT_LEV
0.24
0.2
0.83
-0.89 2.14
INVEST
0.09
0.19 2.11
-4.61
1.82
PROFITABILITY
0.05
0.12 2.4
-2.51
0.52
LNASSETS
5.88
1.15
0.2
1.82
11.4
LNSALES
5.89
1.17
0.2 0
10.28
Note:
a
CV s
coefficientof
variation
Std
dev/Mean
Economic and
Political
Weekly
February
26.
2005
869
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6/12
to 2000. FromTable
5,
we
see
thatfor
my
sample
irms,
bank
debt anddebentureswere almostas
significant
as DFI loans
as
of 1989.
However,
he
arge
difference
n the
figures
or the first
and
second
columns
or
debenture
uggest
thatdebenture
ssue
was
dominated
y
some
of
the
largest
irms,
a
pattern
hathad
not
changed
even in 1995 and in 2000.
This is
also confirmed
by
columns
3,
4 and5 that
give
an idea about he distribution
of
firms.
By
1995,
bank
debt as
a
source of
long-term
inance
haddiminished
drastically
n
importance.
However,
t
slightly
recovered ntil2000butwas still much ower han he1989 evel.
In
contrast,
DFI
lending
and debentureshave
played
a more
important
ole
during
his
period.
DFI
lending
grew
rapidlyby
1995,
and hen loweddown
lightly
by
2000.Debentures
harply
increased
ll
over.Bank
debt
recovered
fter
1995.
These wo are
responsible
or
offsetting
he
slight
decrease
n
DFI
borrowing.
From
Table
6,
it is
clear
hat hort-term
ank
borrowing
onsti-
tutes
the
major
ource
of short-term ebt
requirement
f firms
in all the
years
1991,
1995
and
ill the
year
2000.
However,
here
is a
growing
emand
or
commercial
aper
n
the
ater
earsmainly
by
the
large
firms.
There
s
also
growing
mportance
f funds
needed
or
repayment
f debt n
the near uture
reflected
y
the
ratio f
current
ortion
f
long-term
ebtover otal hort-term
ebt).
IV
Econometric
Models nd
Results
Effect of Short-Term Debt on
Export
Performance
First,
want o address he
question
as to
how
short-term
ebt
affects
export
performance.
or
his,
I
analyse
heeffectof short-
term
everage
on
firm
performance
n the
foreign
market,
on-
trolling
or
the other irm
characteristics
f
exporting
irms.In
order
o
makea statement n
the short-term
everage
position
of the firm I
needto takeinto accountall the
sources
of
short-
term oans
available o the firm. I
define
short-term
everage
as
theratio f short-termebt ototalassets.Short-termebt ncludes
bank
borrowings lus
commercial
apersplus
a
current
ortion
of
long-term
ebt.Also
leverage
s
endogenously
etermined
y
Table
4:
Industry Categories
of
Sample
Companies
Industry Industry ype
Number
Dummy
of
Firms
IND1
Hotel,
banking,
insurance
and
financial
ervices 16
IND2
Manufacture f
dairy
products,
ugar,
tea,
coffee,
vegetable
oils and
fats,
bakery
and
food
products
39
IND3 Manufacture
f
beverages,
breweries,
obacco and
related
products
3
IND4
Manufacture f cotton extiles
46
IND5
Manufacture f
wool,
silk
and man-made ibre extiles 18
IND6 Manufacturefjuteand othervegetable fibre extiles
(except cotton)
2
IND7
Manufacture
f textile
products
including
earing
pparel)
2
IND8
Manufacture
f wood and wood
products,
plywood,
furniture nd fixtures
3
IND9 Manufacture f
paper
and
paperproducts,
newsprint
and
printing,
ublishing
nd allied
17
IND10 Manufacture
f
organic
and
inorganic
hemicals
and
chemical
products,
ertilisers,
esticides,
drugs,
medicines
and allied
products,
matches,
explosives, paints,dyes
and
pigments,photographic
nd
cinematographic
oods
89
IND11
Manufacture
f
rubber,
olid rubber
yres,
tube,
plastic,
petroleum
nd
coal
products
26
IND12
Manufacturef non-metallic ineral
roducts
ike
cement,
mica
stone,
glass
and
glass
products,
eramic
nd
refractory,
tc
36
IND13 Basic metal
and
alloys
industries: ronand
steel,
ferro
alloys,
aluminium,
asting
of
metals,
copper,
steel
tubes,
transmission
owers,
etc 52
IND14
Manufacture
f
metal
products
and
parts,except
machinery
nd
equipment
7
IND15
Manufacture f
machinery
nd
equipment
ther
han
transport
quipment:
lectronics,
lectrical,
quipment,
computers,
hydraulics,
ngineering,
nsulatedwires
and
cables,
fire
protection
quipment,
ndustrial
machinery
for
ood and textile
ndustries,
tc
61
IND16
Manufacturef
transport
quipment
nd
parts:
hips
and
boats
building,
ailway
nd
ramway
quipment,
ommercial
vehicles,
passenger
cars and
eeps,
automobiles
ncillaries
and
transport
quipment,
wo and
three
wheelers,bicycles,
cycle
rickshaws,
aircrafts,
ullock
arts,
etc
81
IND17
Jewellery
and
related
articles
3
IND18 Power
generation
nd
electricity eneration
nd
ransmission
6
IND19 Diversified
miscellaneous)
27
IND20 Watches
and clocks
1
IND21
Other
manufacturing:
edical,
urgical,
cientificand
measuring quipment, pticalgoods,
stationery
rticles,
sports
and
athletic
goods,
etc
3
Total
number
f firms
538
Table 3:
Correlation
Matrix
EXPSLRP
GRSALES
LNASSETS
INVEST
PROF SHORTLEV
LT_LEV
All
irms
EXPSLRP
1.00
GRSALES
0.02
1.00
LNASSETS 0.07
-0.02
1.00
INVEST
-0.03
0.06
-0.01
1.00
PROF
0.04
0.13
0.03
0.28
1.00
SHORTLEV
0.1
-0.05
-0.15
-0.14
-0.33
1.00
LT_LEV
-0.1
0.06
0.016
0.06
-0.07
-0.18
1.00
Top50 and largebusiness group irms
EXPSLRP
1.00
GRSALES
-0.01
1.00
LNASSETS
0.11
-0.03
1.00
INVEST
-0.03
0.05
-0.05
1.00
PROF
-0.01
0.18
-0.02
0.2
1.00
SHORTLEV
0.11
-0.04
-0.08
-0.1
-0.24
1.00
LT_LEV -0.1
0.08
-0.04
0.03
-0.02
-0.16
1.00
Non-top
0 and
private
tand-alone
irms
EXPSLRP
1.00
GRSALES
0.03
1.00
LNASSETS
0.13
-0.01
1.00
NVEST
-0.03
0.08
-0.03
1.00
PROF
0.08
0.11
0.01
0.36
1.00
SHORTLEV
0.09
-0.06
-0.11
-0.16
-0.39
1.00
LT_LEV
-0.1
0.06
-0.02
0.11
-0.14
-0.19
1.00
870
Economic
and
Political
Weekly
Febraury
26,
2005
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7/12
the firm at a
point
n time.
The
possibility
of
expanding
o the
export
marketnthe future
may
causethe
firm o increase hort-
term
leverage
to ease
capacity
constraints,
build distribution
networks,
ncrease
marketing
fforts.
or
promote
heir
product
through
ncreased
advertising.
Thus,
in order to answer how
short-term
everage
affects
export performance,
he
possible
endogeneity
of
leverage
should be taken into account.
To
test
whether hort-term
ebt
affects firm's
exportperfor-
mance,
I
estimate
he
following equation:
EXPSLRPit Yo
+
y1EXPSLRPit-I
y2FSIZE
+
y3NRFATAit
+
4CPROFTAit+ySHORTLEVit
y6T+
ai
+Ui
..
(1)
where
EXPSLRPs the
percentage
f total ales
exported y
firm
i
at time
t,
NRFATA s the
gross
fixed assets net of revalued
reserves
ver
otal
assets,
CPROFTAs
profitability,
HORTLEV
is short-term ebtover total
assets
andT a
time
trend o
controls
for
timely
changes.
Lags
of
export
are
included
o control or
firm
specific
characteristics
hat
may
contributeo
performance
over time.
To test whether
export
performance
parameters
re
signi-
ficantly
different
between
he
top
50
and
large
business
group
firmsandsmaller
roup
or
private
tand-aloneirms, introduce
two
dummy
variables,
DTOP50
and DPVT. DTIP50
dummy
s
equal
o
1
when
the firm s
owned
by
top
50
and
arge
business
groups.
imilarly
DPVT s
equal
o I if
firm
itherdoes
not
belong
to
any
business
group
or
it
belongs
to smaller
group.
I
derive
different
ets of results or the two
types
of firms.I also derive
results orall firms
aken
ogether
nd
here
compare
heeffects
of DTOP50
andDPVTwith
respect
o
firms
belonging
o other
business houses
represented
y
another
dummy
DOTHGRP.
Taking
irstdifferences f
equation
1)
eliminates
he
a
i,
which
were the source of the bias
in the
OLS
estimator.
This
gives:
AEXPSLRPity1AEXPSLRPit_y2AFSIZEi y3ANRFATAit
+
Y4ACPROFTAit y5ASHORTLEVit
+
Auit
i
=
1......
N t
=
1,....,
...(2)
Arellano ndBond
1991
argue
hata more fficient stimator
results rom he use of
additional
nstrumentswhose
validity
s
based
on
orthogonality
etween
agged
values
of
the
dependent
variable
yit
and the
errors
uit.
The
first two observationsare
lost to
lags
and
differencing.
The first differencesof the
exo-
genous
variables
will
serve as its own
instrumentsn estimating
the first
differenced
quations.
Now
I
have
to
instrument or
AEXPSLRPtli
=
(EXPSLRPiti-EXPSLRPt_2),
hich s
clearly
correlatedwith
the error
Auit
=
(uit
-
uit_).
Assuming
hat
uit
arenotautocorrelated,or each att=3,EXPSLRPiactsas valid
instrumentor
AEXPSLRPitJ.
Similarly,
at
t=4,
EXPSLRPil,
EXPSLTRPi2
re
valid
instruments.
ontinuing
n this
fashion,
I obtain
an
instrument
matrixwith one
row for
each time
period
that I am
instrumenting.
The
basic
instrumentet used in
my
results n
Table
7 is
of the
form:
Yi,
0
0
0.....
0..........0....
Axi3
1991
1992
o0
Y,,
Yi2
0
0.........0....
Axi4
199
Zi
=
19
93
o0 o ? O............ Yi..... x
Yii~~
0..l2..
Here,
Y
represents
the
dependent
and
X
represents
the
inde-
pendent
variables.
Short
term
leverage changes
are
likely
to reflect
changes
in
expectations
about future
product
market outcomes. Note
that
as
we
see from the
descriptive
statistics that
the
top
50 and
large
business
group
firms are
typically bigger
than stand-alone
or
smaller
group
firms. One
may
argue
that hese differences
between
the two
sub-samples
drive
my
results. In order to correct for
this
possibility,
I introduce the variable FSIZE
as additional
control
variable.2 The variable FSIZE is the natural
log
of total assets.
OLS estimates
or
even
panel
Tobit
estimates
are biased
and
inconsistent
due to
endogeneity problems.
Therefore,
I estimate
my
models
using
an instrumental
variable
approach.
The instru-
mental variable estimation
technique
controls for the fact that
the
explanatory
variables are
likely
to be correlated with the
error
term
and the
firm-specific
effect,
and deals
with
possible
endogeneity problems. Equation
(2)
is
therefore estimated
with
Table 5: Relative
Importance
of
Different Sources
Long-Term
Debt in
1989,1995
and 2000
(1) (2)
(3) (4) (5)
_(Num)/,(Denom)
Sample
1st
Median 3rd
i i Mean Quartile Quartile
Year=
1989
DFI/LTD 0.31
0.31 0.01 0.2 0.54
LTBNKD/LTD 0.21 0.25
0 0.17 0.43
DEBEN/LTD
0.23
0.13
0
0.00
0.2
Year= 1995
DFI/LTD 0.35 0.47 0.1 0.43 0.7
LTBNKD/LTD 0.1
0.1
0 0.00
0.1
DEBEN/LTD
0.34 0.2
0 0.1 0.34
Year
=
2000
DFI/LTD
0.31
0.42
0
0.34
0.63
LTBNKD/LTD
0.14
0.17
0 0.01
0.22
DEBEN/LTD
0.32 0.21 0 0.02
0.29
Notes:
The
numbers
n
the first
columnare the
ratios
of sum
(over
all
sample
firms)
of a
particular
ype
of
debt,
to
the sum
(over
all
sample firms)
of
long-term ebt.Thenumbersn he next our olumnsaresamplemeans
and
quartile
alues
(N=538).
DFI
s loan
from
Development
Financial nstitutions. TBNKD
s
long-
term
bank
debt. DEBENs debenture.LTD
s total
ong-term
ebt.
Table
6:
Relative
Importance
of Different
Sources
Short-Term
Debt in
1991,
1995 and
2000
(1)
(2)
(3)
(4)
(5)
Z(Num)/Y(Denom)
ample
1st Median
3rd
i
/
i
Mean
Quartile Quartile
Year= 1991
STBNKBOR/STD
0.9
0.92
0.93
1
1
CP/STD
0.01
0.003 0 0 0
CURLTD/STD
0.09
0.08 0
0
0.06
Year=
1995
STBNKBOR/STD 0.85 0.87 0.83 1 1
CP/STD 0.02
0.01 0 0
0
CURLTD/STD
0.13
0.12
0
0 0.16
Year
=
2000
STBNKBOR/STD
0.7
0.82
0.73
0.99
1
CP/STD
0.07
0.03
0
0 0
CURLTD/STD
0.23
0.15
0 0
0.22
Notes: The
numbers
n
the
first
columnare
the
ratios
of
sum
(over
all
sample
firms)
of a
particular
ype
of
debt,
to the
sum
(over
all
sample
firms)
of
short-termdebt.
The
numbers
n
the next
four
columns are
sample
means
and
quartile
alues
(N=538)
STBNKBORs short-term
bank
borrowing.
CP is
commercial
paper
borrowing.
CURLTDs
current
portion
of
long-term
debt. STD
is total
short-term
ebt.,
Though
CP/STDhave "0"
alue
at
3rd
Quartile,
hey
are
positive
at 99
percentile
ndicating
he
presence
of
extreme
values.
Economic
and
Political
Weekly
February
26,
2005
871
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8/12
the
system
of
generalised
methods
of moments
GMM)3
sti-
matorsas
proposedby
Arellanoand Bond
(1991).4
For details
about the
technique
refer to
the
appendix
B.
The Arellanoand Bond
two-step
GMMresultsas
reported
n
Table
7 show
GMM
estimates of
equation
2
(based
on
first
difference).
Export
n the
current
eriod
s
significantly
elated
to
the
previous
year's
changes.
We
clearly
see that
export
re-
sponds
positively
to the current ncrease
n firm's
short-term
leverage.
Thisis true or all firms taken
ogether
column2)
as
well as for two sub groups(columns3 and 4). The control
variables
re
irm
ize,
profitability
nd nvestment.f
current
rofit
rises,
the smaller
group
or stand-alone
irm's
export
decreases.
This
may
be
due to the
fact that if
profit
rises,
firms
are
more
willing
o
sale
n thedomesticmarket
nstead
f the
risky
oreign
market.5
However,
he
rise in
investment
oosts
export.
Forthe
top groups,
I
find a
change
in
profit
or investmentdoes
not
significantly
nfluence
export.
If the
firm
gets bigger,
t
expe-
riencesan increase n
foreign
ales. Thussize matters or
export.
Effect of
Long-Term
Debt
on Firm Performance
The
pooled
cross-sectional-time eries
Tobit
regressions
I
estimatebelow resemble hose of Oplerand Titman 1994). I
test the
significance
of
lagged
values of
long-term everage
(LT_LEVERAGE)
o examinethe effect
of
long-term
debt on
firm's
growth
f sales
n the
current
ear.They
have he
ollowing
general
orm:
Sales
Growthit
=
0
+
PILNSALESi.
+
REINVESTi,
+
03CPROFRAi,_I
21
+
P4LT_LEVERAGEi
_2(or
t7)
+
yj
(Industry
Dummies).
j=1
12
3
+X
t,
(YearDummies),+y k
GroupDummies)+ei
...(3)
t=1 k='
The
lagged
structure
sed in
the above
equation
s meant
o
mitigate imultaneity roblems.
As far as the
error tructures
concerned,heMaximumLikelihoodEstimation f panelTobit
assume that
eit
is
uncorrelatedwith
Eit'
and
ct
t,
=
0,
when t
?
t';
wheret and
t'
are indexes for
time
periods
when
observations
of the ame irm
re ollected.
imilarly,
assume
Eit
s
uncorrelated
with
ej,
Vi
i
at
same t.
However,
one
may
articulatehe
argument
or
unobserved r
unmodelledirm
specific
variablewhich
may
actually
nfluence
the
dependent
variable
and are
thus
capable
of
introducing
simultaneity
bias in
my
empirical
specification.
It
is
a
very
difficultcase to
argue
since
there
s no
such
endogeneity
est
in
a
panel
structure
keeping
in mind the
Hausman wo
step
estimationmethodof
endogeneity
est).
In such a
case,
I
control
these
unobserved actors
by taking
three
business
group
dum-
mies. 12 yearlydummiesand21 industrydummies.I assume
that firms
within each
business
group category
and
industry
category
have
common haracteristicsut heir
bahaviour aries
across
groups
and industries
and also across
various
years.
Accordingly,
nclude
1
industry
ummies nd12
yearly
ummies
to control
firm
specific
fixed
effects.
It is
obvious
that
I
should
drop
1
dummy
each
from 12
year
dummies nd21
industry
ummies nd
3
group
dummies o
avoid
dummy rap,
which will arise
due to the
multicollinearityrob-
lem. Here
ong-term
everage
changes
are
likely
to
reflect
the
cumulative
ffect of
past
decisions.
Six
regression utputs
rom
equation
3
reported
n
Table
8.
I
canmake
ollowing
nferences rom
heresults f
Table8.
First,
Table
7:
Export
Equations
GMM Estimates
(All
Variables
in First
Differences)
Dependent
Variable:
EXPSLRPit
Sample
Period:
1991-2000
Independent
All
FirmsCombined
Top
50 and
Large
Smaller
Group
Variables Business
Group
and
Private
Stand-alone
(Y1)
0.39***
0.5***
0.22***
EXPSLRPit_1
(19.19) (37.27)
(15.41)
(Y2)
0.78***
0.61***
1.36***
FSIZEt
(2.23) (2.41) (3.86)
(Y3) 1.34 -0.65 2.46***
NRFATAit
(0.89) (-0.95)
(2.06)
(Y4)
-2.72
0.14
-5.26***
CPROFTAit
(-1.44) (0.2) (-3.05)
(Y5)
2.42***
2.53***
2.19***
SHORTLEVi,
(2.33)
(2.52)
(2.33)
No of observations
5339 2953
2386
No of firms
538 296 242
AR1
0.0001
0.0006
0.0168
AR2 0.1865
0.5730 0.1081
Sargan
est 0.39 0.13
0.1
Wald est
500.14
(15)
1665.5
(15) 656.93(14)
Notes:
z
values are
in
he
parentheses.
Time
dummiesand
group
dummies
are
includedbut not
reported.
GMM esultsaretwo
step
estimateswith ne
period
ag
of
the
dependent
variable.
AR1andAR2
are tests for he GMM
stimators,
he P-values
reported
refer o the two-stepGMM stimators.
Sargan
s
a test of the
overidentifying
estrictionsortheGMM
stimators,
theP-values re
only eported
nd
number f nstrumentss
in
he
brackets.
FSIZE s
proxied
by
natural
og
of
total
assets.
***:
ignificant
t
5
per
cent or
better;
**:
Significant
t 5-10
per
cent.
Table 8:
Capital
Structure
and Product Market
Performance:
The Effect of
Long-Term
Leverage
on
Firm's Growth of
Sales
-
Panel Tobit
Regressions
Dep
Var: All
Firms
Top
50
Business
Non-top
50
Group
Business
Group
Sales 2-Year
7-Year
2-Year
7-Year 2-Year 7-Year
Growth
Lagged
Lagged Lagged
Lagged Lagged
Lagged
at
t
LT_LEV
LT_LEV LT_LEV
LT_LEV
LT_LEV
LT_LEV
Intercept
-0.44
-1.04
-0.55***
-0.72*** -0.54 -1.21
(-1.56) (-1.02) (-2.96) (-2.71) (-1.41) (-0.78)
LNSALES
0.07***
0.11***
0.05***
0.07*** 0.09***
0.17***
(6.43)
(5.12) (5.79)
(4.28)
(3.99)
(3.41)
INVEST
0.92*** 1.84***
0.89***
1.68***
0.95***
2.02***
(13.11)
(10.76)
(15.97)
(13.67) (5.91)
(4.86)
CPROFTA
-0.29***
-0.2 -0.27***
-0.13 -0.26
-0.21
(-2.16)
(-0.8)
(-2.46) (-0.69) (-0.92)
(-0.37)
LT_LEV
0.34***
0.38*** 0.39***
0.46*** 0.25**
0.26
(5.88)
(3.1)
(8.55)
(4.7) (1.81)
(0.97)
Observations 5347
2662 2955
1475 2392
1187
Observation 1314 Left
889 left
643
left
437
left
642 left
452 left
Summary
censored
censored
censored
censored
censored
censored
and
4033
and
1773 and 2312
and
1038 and 1690
and
735
un-
un- un-
un- un-
un-
censored
censored censored
censored
censored
censored
obser-
obser-
obser-
obser- obser-
obser-
vations
vations
vations
vations vations
vations
LR
Chi2
statistic
391.47
247.64 455.54
308.89
133.02 81.86
d.f
36
34
31 26
33
31
Prob.>Chi2
0.00
0.00
0.00
0.00 0.00
0.00
Pseudo
R2
0.03
0.04 0.1
0.1
0.02
0.02
Notes:
***:
ignificant
t
5
per
cent
or better.**:
Significant
t
5-10
per
cent.
The
dependent
variable
s
firm
annual
sales
growth
at
time
t,
given by
(Sale,
-Salet,_l)/Sale,t_
The
dependent
variable is
left
censored at
zero. LSALESs
the
contemporaneous
atural
ogarithm
f
total
assets.
INVEST
s
Investmentwhich
is the
growth
in
fixed
assets
minus
revalued
reserves over total
assets at t-1.
CPROFTA
s
Profitability,
which s the
cash
profit
ver
assets
at
t-1. LT_LEV
s the
long-term
ebt
over otal
assets,
and
s
measured
either
at t-2
or
t-7.
The
sample
period
is
1989-2000.
The
regressions
nclude 1
industry
nd 12
year
dummies
and
DTOP50,
DOTHGRP
nd DPVT
hree
group
affiliation
ummies
(not
reported).
The
numbersare the
coefficients of
the Tobit
model.
Figures
nside
brackets
are
the t
values.
872
Economic and
Political
Weekly
Febraury
26,
2005
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9/12
firms
that
nvestedmore
n
the
previous
year
seem to do
better
in the
product
market
ext
year.
This s
supported
y
the
positive
and
significant
stimated
oefficientof INVEST n all six
cases.
Second,
irms hatwere
more
profitable
n the
previous
ear
end
to observe
owersales
growth.
This
is reflected
by
negative
and
significant
estimatedcoefficient
of CPROFTA
or all
firms
combined
and
for
top
50
business
group
category.
More
mportantly,
he
resultsshow that
for both two
period
and seven
period
ag
valuesof
long-term
everage
(LT_LEV),
thereexists a remarkablyonsistentpatternn the way capital
structurenfluences
irm
performance
n the
product
market
ales
for all
firms
aken
ogether.
The results
ndicate
hatan
increase
in the use
of
long-term
ebt
financing ignificantly
boosts
sales
growth
mainly
or
the
top
50
and
arge
business
group
affiliated
firmsafter
wo
years
or seven
years
of
taking
he
oan.
Thus
ong-
term
debt
can commit he firm o
compete
n the
product
market
and
may
n factactivatehe
irm
o take
aggressive
n
output
tance.
Atthesame
ime,however,
ong-termeverage
s
inconsequential
to
performance
or smaller
group
and
private
tandalone irms.
Parametric and
Non-parametric
Univariate Results
Ilookat heresults f univariatearametricndnon-parametric
tests
on real marketvariables. n Table
9,
I see the effects of
short-term ebt
on the
export
ales
ratio,
advertisement,
arket-
ing,
R&D
and
distributionntensities.I
compare
he
average
values betweenbefore and after the short-term
oan has
been
taken.The
parametric
-testshows
(in
panel
A of
Table
9)
that
mainly op
group
irms
on
average
spend
more
on
advertising,
and research nd
development
ubsequent
o
taking
short-term
debt
n order o
gain
strategic
dvantage
n
the
product
market.
Both the
top group
and
private
tand-alone
irms'
average
dis-
tribution
ntensity
s
higher
after
the loan has been takencom-
pared
o the
previous
oan. Firms also
export
more
following
short-term ebt in
comparison
o
the
previousyear
of
the loan.
InpanelB of Table9, wereporthe resultsof Wilcoxon igned
rank eststo find the
importance
f
short-term
apital
structure
to
firm
export
ndother
trategic
eal
market ariables.
Wilcoxon
signed-rank
on-parametric
ests
are conducted
o
evaluate he
significance
of
changes
in
these measures.
Observations re
separated
nto
ituations eforeand
after he oanhave
been aken.
The
null
hypothesis
s thatthe
beforeand aftershort-term ebt
are from
populations
with
the same
distributions nd the
same
medians.
Wilcoxon ests
generally
ndicate hat
the
beforeand
after
hort-term ebt or
thesefirmsarenot
drawn rom
he same
distribution.
see that irm
exports,expenditure
n
advertising,
marketing,
istribution nd research
nd
development
ncrease
with
short-term ebt.
Thus,
findthe
relationship
etween
hort-
termdebtand irmbahaviournthe realmarket. hisrelationship
is
evident for both
types
of firms.
Similarly,
n
establishing
he
importance
f
long-term
debt
financing
on
the real
economy,my objective
s
to
identify
ts
impact
on
various
product
market
trategies
e
g,
advertising,
marketing,
istribution, &D,
etc)
which he
firms
might mple-
ment
given
their
own as
well as their
rivals'
choice
of
financing
instruments.
ccordingly,
look
at the
long-term
mpact
of
debt
financing
on
these
real
market
variables
through
univariate
parametric
nd
non-parametric
ests.I
present
time
series
able
of
R&D
expense,
advertising,
marketing
and
distribution
expenses
orall
firms hat
ook
long-term
ebtfrom
he
current
year
(t)
till seven
years
(t+7)
after
the loan was
taken.The
univariate results are
displayed
in Table 10. The results
from
Panel
A
and Panel
B
confirm
that both the
top
group
and
private
stand-alone
firms,
taking
long-term
debt on
average
(both
mean
and
median),
increase
advertising
expenses, marketing
efforts,
build distributionnetworks
and
improve
R&D infrastructure
ver
time.
All of
these
may
help
the firm to
expand
market
share
in
the
product
market in the
long
run.
Table 9: Univariate Tests: Effect of Short-Term Debt on
Product
Market
Variables
(1)
(2)
(3)
(4) (5)
Real MarketVariables
t-1
t
t+1 t-stat
for
Difference
Between
Beforeand
After
(Col
2
and
Col
4)
Panel A: means
Export
ales ratio
per
cent)
All
irms 7.23 7.64
7.93 8.7**
Top
50
business
group
6.45
6.79 6.99
6.32***
Non-top
50 firms
8.21 8.71
9.11
6.05"**
R&D
ntensity
All irms
0.0005 0.0006 0.0006 1.87***
Top
50 business
group
0.0005
0.0006 0.0006 2.87***
Non-top
50 firms
0.0005 0.0006
0.0006 0.62
Advertising
ntensity
All irms
0.0058 0.006 0.006
4.27***
Top
50 business
group
0.0065 0.0068
0.007 4.3***
Non-top
50
firms
0.0048 0.0049
0.0049 1.28
Marketingntensity
All irms
0.016
0.016 0.017 5.72***
Top
50 business
group
0.0165
0.017
0.0172 4.86***
Non-top
50 firms
0.015
0.015 0.016
3.18***
Distribution
ntensity
All
irms
0.02
0.02 0.02
4.47***
Top
50 business
group
0.023 0.024
0.024 3.11***
Non-top
50 firms
0.015
0.016
0.016 3.56***
z-stat for
Difference
Between
Beforeand
After
(Col
2
and
Col
4)
Panel B:
medians
Export
ales
ratio
per cent)
All
irms
1.65
1.88 2.06
11.32***
Top
50 business
group
2.24
2.46
2.72
9.26***
Non-top
50 firms
0.88
1.05
1.31
6.64***
R&D
ntensity
All
irms
0.00
0.00
0.00 4.81***
Top
50
business
group
0.00
0.00
0.00
4.16***
Non-top
50
firms
0.00
0.00
0.00
2.43***
Advertisingntensity
All
irms
0.0005
0.0005
0.0005
1.43
Top
50
business
group
0.0006
0.0006
0.0006 2.71***
Non-top
50
firms
0.0005 0.0005
0.0005
-0.89
Marketing
ntensity
All irms 0.0092 0.0095 0.01 8.1***
Top
50 business
group
0.01
0.011
0.011 7***
Non-top
50 firms
0.008
0.0081
0.0082 4.21***
Distribution
ntensity
All
irms
0.012
0.012
0.0123 8.71***
Top
50
business
group
0.013
0.014
0.014
7.94**"
Non-top
50 firms
0.009
0.01
0.01 4.1***
Notes: This
able
compares
he
effectiveness
of short-term
ebt
on real
variables.
***
enotes
significant
t
5
per
cent or
better
and
**
denotes
significant
at
5-10
per
cent;
z-statistic
or
difference
between
paired
eries
denotes
the
outcome of a
"Wilcoxon
igned-rank
est" for
difference in
the
distributions.Both
the
"t-test" nd
"Wilcoxon
igned-rank
ests"
are
paired
univariate
ests that
compare
the
average
values
of common
sample
between
the two
series.
Year
is the
year
of
the
issuance of
short-term ebt
and this
s taken
as
the control
period.
Economicand
Political
Weekly
February
6,
2005
873
This content downloaded from 111.68.103.104 on Mon, 14 Jul 2014 06:07:32 AMAll use subject to JSTOR Terms and Conditions
http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp8/10/2019 Effect of Capital Structureon Firm'SProduct Market,Indian Firm
10/12
V
Concluding
iscussions
The
strategic
use of debt
models
shows
that,
under
imperfect
competition,
firms
may
have
strategic
incentives
to take debt.
Financially
healthy
firms could
use their
deeper
pockets
or
may
strategically spend
on
building
distribution
networks,
increase
marketing
efforts
and
advertising
for
product promotion
to
prey
on rivals
or
deter
potential
entrants.
My
results
suggest
that the
strategic
consideration in the
output
market induce firms to take
higher
debt
in order to
gain
strategic
advantage.
This establishes
a link
between
debt and firm
competition
in the
product
market.
I
distinguish
between
short-term
debt and
long-term
debt to
examine their
impact
on a firm's
product
market outcomes.
I
compare
the
top group
affiliated
firms with their
smaller
group
or unaffiliated
counter
parts.
I find that short-term
debt induces
the
firms to do
well in
exports.
I also discover that
short-term
debt influences
a firm's
R&D,
advertising,
marketing,
nd dis-
tribution
trategies.
n case of
long-term
debt,
firms take
time
to build nfrastructure
hrough
ncreased
marketing
nd
promo-
tions.
R&Dwhichhave
ong-term
mpact
n their
product
market
performance. onsidering longer
ime
horizon,
find
hat
ong-
termdebtboosts otal ales
growth
or
op
50 and
arge
businesses
group
affiliated
irms.
However,
or the unaffiliated
irms,
t
is
inconsequential
n total
growth
of sales.
Thus,
debt can
shape
industry ompetition.
Consequently,
find
empirical
vidence
on the existenceof a linkagebetween irm'schoiceof
capital
structural
nd its
product
market
performance.
Basedon
my empirical
indings,
propose
hat
developments
in the debt market ould
be
an
important
eterminantor
cor-
porateperformance.
n
this
context,
credit
rating
agencies
have
an
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