Effect of Capital Structureon Firm'SProduct Market,Indian Firm

8/10/2019 Effect of Capital Structureon Firm'SProduct Market,Indian Firm

1/12

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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2/12

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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3/12

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



4/12

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



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



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



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



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



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



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

Effect of Capital Structureon Firm'SProduct Market,Indian Firm

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