The Relationship Between Firm Investment and Financial Slack

Sean Cleary

A thesis submitted in confomity with the requirements for the Degree of Doctor of Philosophy in the Faculty of Management at the University of Toronto

@ copyright by Seau Cleary, 1998

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1 am gratefd to Donald Brean, Tom McCurdy, Raymond Kan, Paul Halpern,

Varouj Aivazian, Glenn Hubbard, Mike IngLis, Steve Hadjiyannakis, and participants at

the 1996 Northern Finance Association meetings for thei. valuable comments. The study

was also improved substmtially by incorporating comments kom the editor and an

anonymous referee at the Journal of Finance. 1 am especiaily appreciative of the

assistance and encouragement offered by my supervisor, Professor Laurence Booth. Any

remaining errors are my responsibility.

I have dedicated this thesis to my entire farnily, without whose support and

patience, 1 could not have endured. Thanks again to my wife, Helen; my children, Jason,

Brennan, Brigid and Siobhan; and to my parents, Bill and Beryl.

T h e Relationship Between Firm Investment and Enancial Slack''

A thuis submitted in conformity with the requirements for the Degree of Doctor of Philosophy in the Faculty of Management at the University of Toronto

Seau Cleary, 1998

This thesis examines the relationship between investment and financial factors,

with particular emphasis on the role of 'fimancing constraints' in deterrnining investment.

Fazzari, Hubbard and Petersen ( 1988) and several subsequent authors provide strong

support for the signïficance of fuiancial factors among f m s that have been identified as

facing a high level of fuiancial constraiots. Their results suggest investment decisions of

fms that are more fmancially constrained are more sensitive to fm liquidity than those

of less constrained f m s .

Debate over the nature of the relationship between investment decisions and

fimancial constraints has been heled by the recent work of Kaplan and Zingales (1997)

who challenge the generality of the conclusions above. They classify F i s according to

their degree of fimancial cons traint, based on quantitative and qualitative information

obtained from Company annual reports. Contrary to previous evidence, they fmd that

investment decisions of the least financially constrained F i s are the most sensitive to

the availability of cash flow. Kaplan and Zingales are cnticized for the use of a srnall,

homogeneous sample, as weli as for the subjectivity associated with their classification

scheme.

This thesis examines the generality of the Kaplan and Zingales conclusions using

a large, diversifed sample and an objective classification scheme of f i fmancial status.

Fimis are classified using fmancial variables that are related to fiancial constraint. Firm

fimancial status is determined using multiple discriminant analysis, similar to Altman's Z

factor for predicting bankruptc y. This multivviate classifcation scheme effective1 y

captures desired cross-sectional properties of fms. In addition, it allows reclassification

of f i financial status every period and group composition is allowed to vary over time

to reflect changing levels of fmancial constraints at the level of the fm.

The results demonstrate that f i investment decisions are directly related to

financial factors. Investment decûions of fms with high creditworthiness (according to

traditional fiancial ratios) are extremely sensitive to the availability of intemal hnds

while Iess creditworthy firms are much less sensitive to intemal fund availability. This

evidence supports the conclusions of Kaplan and Zingales (1997) using an objective

classification scheme and a large, diversified sample of 1080 U.S. flrms.

iii

ACKNO WLEDGEMENTS

ABSTRACT

TABLE OF CONTENTS

LIST OF FIGUICES

LIST OF TABLES

CHAPTlER 1: INTRODUCTION 1

............................................................................ 1 . The Issue 1

3 . Organization of The Thesis ........................... .. ....................... 4

CHAlPTER 2: LI'IXRATURE REVIEW: INVESTMENT POLICY AND FINANCIAI, FACTORS 6

1 . The Lrrelevance of Financial Factors ......................................... 6 2.1.1 The Basic Irrelevance Argument ........................................ 6 2.1.2 The Q-Theory of Investment ............................................. 7

.... 2 . Capital Market Imperfections and The Relevance of Financial Factors 9 ........................ 2.2.1 'Accekrator' Models of Investrnent Behavior 9

........ . . . ......................... 2.2.2 The Role for Intemal Funds ... .... 1 1 2.2.3 As ymrnetric Information Mo dels .................................... ... 1 4 2.2.4 Agency Models ............................................................ 17

............. 2.2.5 Investment Decisions in an Option Theoretic Frarnework 21

3 . Empuical Evidence .................................... .... ................. 23 2.3.1 Early Empirical Evidence ............................................. 23 2.3.2 Fazzari, Hubbard and Petersen (1988) ................................ 24 2.3.3 Subsequent Studies ...................................................... 27 2.3.4 Kaplan and Zingdes (1997) ....................... .. ................. 30

CHAPTER 3: DATA AND METHODOLOGY 34

1 . O v e ............................................................................ 34

. ......*...........................*................-.........-..--.... 2 Data Sources 35

3 . Classification Scheme ...................................... ..,, ................. 36 3.3.1 General Approach ................... ... .....................-..... 36

......... 3.3.2 Classifyutg Financial Status Using Discriminant Analysis 37

4 . Regression Estimation Techniques ............................................... 42 .......... 3.4.1 PanelDatasets ............................................... 42

3.4.2 Pooled Ordinary Least Squares (OLS) Estimation ................... 43 3.4.3 Random Effects Estimation ............................................ 44 3.4.4 Fixed Effects Estimation .......................... ., ...... ,.. . . . . 46 3.4.5 Estimation in This S tudy ................................................. 48

5 . Empirical Levels of Significance ............................................ 50

CHAPTlER 4: FAZZARI. HUBBARD AND PETERSEN (1988) REPLICATION 52

1 . Sample Characteristics ........................ .. . ... ....................... 52

2- Firm Classification ................................... ., ......................... 54 4.2.1 Group Characteristics ..................................................... 54 4.2.2 Discriminant Analysis .................................................... 57

......................... .......................... 3 . Regression Results .. 64 4.3.1 Original FHP88 Dividend Payout Groups ............................. 64 4.3.2 Financial Constraint Groups Based on Discriminant Analysis ..... 67

............................................. ....................... 4 . Summary .... 73

CHAPTER 5: THE CANADIAN SAMPLE 75

SampIe Characteristics ................... .., ...................................... 75

Firm Classification ............................................................ 76 5.2.1 Group Characteristics .................................................... 76 5.2.2 Discriminant Analysis .................................................... 80

Regressio n Results ................................................................. 86 5.3.1 TotalSampleandDividendPayoutGroups ............................ 86 5.3.2 Financial Constraint and Industry Groups .............................. 90

Summary ............................... .. ................................... 93

CHAPTER 6: THE U.S. SAMPLE 96

Sample Characteristics ............................................................ 96

........................................ ................... Group Classification .. 98 6.2.1 Groupcharacteristics .................................................... 98

................................................... 6.2.2 Discriminant Anaiysis 99

Regression Results .~~............................................................ 106 6.3.1 TotalSarnpleandDividendPayoutGroups ..................... 106 6.3.2 Exchange and Industry Groups .................... .. ........... 110 6.3.3 Financial Co nstraint Groups ..................... ,. ............... 113

........................... ClUPTl3R 7: CONCLUSIONS ....................... .... 131

APPENDICES : 135

1 . Financial Variable Cdculations ................................................... 135 .............................. II . Sample Selection Criteria and Default Sethgs 137

ILI . Discriminant Analysis ............................................................ 138

BIBLIOGRAPHY

LIST OF FIGURES

Figvre # Title Paee #

Figurel. FUmInvestmentandCostofCapital ......................... . .......... 12

vii

LIST OF TABLES

Table #

Table 1.

Table 2,

Table 3.

Table 4.

Table 5.

Table 6.

TabIe 7.

Table 8.

Table 9-

Table 10.

Table 11.

Table 12.

Table 13,

Table 14.

Table 15.

Table 16.

Table 17,

Table 18.

Title

FHP Sample Summary Statistics (1973-84)

Correlations Among Variables (FHP Sample)

Selected Financial Ratio Means (FHP Sam ple)

Group Turnover Statistics (FHP Sample)

Percentage Group Compositions (FHP Sample)

Regression Estimates for the Total Sample and the Original FHP88 Groups (FHP Sample)

Regressio n Estimates for the Financial Constraint Groups (FHP Sample)

Regression Estimates for Financial Constraint Sub-Groups Within FHP Groups (FHP Sample)

Canadian Sample Summary Statistics ( 1988-94)

Correlatio ns Arno ng Variables (Canadian S ample)

Selected Financial Ratio Means (Canadian Sarnple 1988-94)

Group Turnover Statistics (Canadian Sample)

Percentage Group Compositions (Canadian Sample)

Regression Estimates for the Total Sample and for the RIP Dividend Groups (Canadian Sample)

Regressio n Estimates for The-Varying Dividend Payout Groups (Canadian Sample)

Regressio n Es timates for the Financial Co nstraint Grou ps (Canadian Sample)

Regression Estimates for Industry Groups (Canadian Sample)

U.S. Sample Summary Statistics (1988-94)

Paee # - 55

58

60

61

63

viii

Table 19-

Table 20-

Table 2 1 -

Table 22-

Table 23-

Table 24.

Table 25.

Table 26.

Table 27.

Table 28.

Table 29.

Table 30.

Table 3 1 -

Table 32.

Correlations Arnong Variables (U.S. Sample)

Selected Financial Ratio Meaos (US. Sample 1988-94)

Group Turnover Statistics (US Sample)

Percentage Group Compositions (U.S. Sample)

Regression Estimates for the Total Sample and for the FHP Dividend Groups (U.S. Sample)

Regressio n Estimates for Tirne-Varying D ividend Payout Groups (US. Sample)

Regressio n Estimates for Exchange Gro ups (U.S. Sample)

Regression Estimates for Industry Groups (US. Sample)

Regression Estimates for the Financial Constraht Groups (U.S. Sample)

Regression Estimates for Financial Constraint Sub-Groups Withi. FHP Groups (U.S. Sample)

Regression Estirnates for Financial Constrahi Sub-Groups Within the Tirne-Varying Dividend Groups (US. Sample)

Regression Estimates for Financial Constraint Sub-Groups Within Exchange Groups (U.S. Sample)

Regression Estimates for Financial Constraint Sub-Groups Within Industry Group (US. Sample)

Financial Constraint Group Cash Flow Cornparisons

CHAPTER 1

INTRODUCTION

1.1. THE ISSUE

The analysis of fm investment decisions has long been a major concem of

researchers in corporate finance. macroeconornics, public economics and industrial

organization. Research topics range from theoretical debates regarding which models

offer the bes t explanation of inves ment behavior, to polic y questions regarding ho w

changes in monetary or fscal policy affect ùivestment and economic growth,

The theoretical debate regarding the relationship between investment and

financial factors has evolved over several decades. Neoclassical theory, which is based

upon the 1958 work of Modigliani and Miller, argues that a fm's fmancial status is

irrelevant for real investment decisions in a world of perfect and complete capital

markets. The alternative view is that fimancial structure is relevant to the investment

decision for companies facing uncertain prospects that operate in imperfect or incornpiete

capital markets where the cost of extemal capital exceeds that of intemal hinds. For

example, Greenwald, Stiglitz and Weiss ( 1984)' Myers and Majluf ( l984), and Myers

( 19 84) provide a foundation for these market imperfections by appealing to as ymmetric

information problems in capital markets. Altematively, Bernanke and Gertler (1989,

1990) and Gertler (1992) demonstrate that agency costs can also cause a prernium on

extemai finance that increases as borrower net worth decreases. The investment

decisio ns of firms operating in such envkonments wiu be sensitive to the availability of

intemal funds, since they will possess a cost advantage over extemal funds.

An extensive empincal literature deahg with capital market imperfections and

investment has developed, based on the 1988 work of Steven Fazzari, Glenn Hubbard

and Bruce Petersen in the Brookings Papers on Economic Activity. This literature

focuses on the role of 'fmancing constraints' in determining investment. The major

conclusion of these studies is that firrn investment is ~ i g ~ c a n t l y related to interna1 fund

availability, which has several important macroeconomic policy implications. For

exarnple, the existence of this relationship implies the introduction of tax policies that are

biased in favor of intemal Fmancing may produce an amplifïed effect on investment.

Fauari, Hubbard and Petersen (1988) and a number of subsequent empirical

studies provide strong evidence that investment decisions of fmancially constrained F i s

are more sensitive to f i liquidity than those of less constrained f m s . Ernpincal debate

regarding the nature of the relationship between financial factors and investment has been

fueled by the recent work of Kaplan and Zingales (1997) who challenge the conclusions

of previous studies. Kaplan and Zingales classify h s according to their degree of

financial constraint, based on quantitative and qualitative information obtained from

Company annual reports. Contrary to previous evidence, they find that investment

decisio ns of the least fmancially constrained firms are the most sensitive to the

availability of cash flow.

1.2. THE CONTRIBUTION OF THIS STUDY

Kaplan and Zingales (1997)'s classification of firm fuiancial constraint status

according to traditional fmancial ratios has intuitive appeal since it represents a direct

measure of the prernium paid for bank loaos by f i s . The importance of this type of

measure is highlighted by Mayer (1990)'s evidence that bank loans are the prirnary

source of extemal fiance for f m s in developed countries. The Kaplan and Zingales

results are cnticized because the y are based on such a small sample (49 f i s ) and

because of the subjectivity involved in their classification scheme.

This study deviates cnticisms of the Kaplan and Zingales approach by using an

efficient mechanism for determining firm fmancial status, which is able to deal with large

numbers of f m s . This rnuItivariate classification scheme is objective and effectively

captures desired cross-sectional properties of f m s . Firms are classified into groups

according to an index which is determined using multiple discriminant analysis, similar

to Altman's Z factor for predicting bankruptcy. Summary statistics indicate the index is

successful in classifying firm fmancial status. The index also allows reclassification of

fm fmancial status every penod and 1 allow group composition to Vary over time to

reflect changing levels of fimancial constraints, both at the level of the f m and in

aggregate. This represents an improvement over previous studies that did not d o w

group composition to Vary, which implicitly assumes that financial obstacles faced by

fims do not change over tirne.

The present study provides strong support for the Kaplan and Zingales

conclusions using an objective classification scheme and a large, diversified sample of

1080 US. firrns. Obtaining large sample evidence is important, since the conclusions of

several previous studies are based on smdl sample results'. In addition, large sarnples

reduce the sensitivity of conclusions to the behavior of a few f m s . The importance of

this matter is highlighted by the regression results in chapters 4 and 5, which dernonstrate

the behavior of a relatively s m d number of h s in a sample can have a significant

impact on ove rd conclusions.

This study ais0 makes a methodologicai contribution to this literature. The focus

of previous studies has been the comparison of investment-liquidity sensitivities across

different groups of fms. However, traditional tests designed to detect differences in

coeficients are not appmpriate since the error ternis Wtely violate the required

assumptions. As a result, conclusions regarding the existence of differences across

groups in investment-liquidity sensitivity, have been largely based on obse&g

differences in magnitude and Ievel of significance of the coefficient on the liquidity

variable in regression estimates. 1 employ a bootstrap methodology to determine

significance levels of observed differences in coefficient estimates.

1.3. ORGANIZATION OF THE THESE

The remainder of this thesis is oqanized as follows. Chap ter 2 provides a review of

the existing theoretical and empirïcal literature that motivates the present study. Chapter

3 provides details of the data and methodology utilized. Chapter 4 outlines the results of

-. .

l For example, Fazzari, Hubbard and Petersen (1988) have only 49 fms in one group and only 39 in another. Kaplan and Zigales (1995) use three groups of 19, 22 and 8 fms, tvhile Hoshi, Kashyap and Scharfstein (1991) have only 24 f i s in their group of likely constraured f i s .

a replication of the original Fazzari, Hubbard and Petersen (1988) study, while chapter 5

presents results obtained using a sample of 201 Canadian fms. Chapter 6 presents the

main results of the thesis, which are based on a sample of 1080 U.S. companies.

Conclusions are offered in Chapter 7.

CHAPTER 2

LITERATURE REVIEW: INVESTMENT POLICY AND FINANCIAL FACTORS

This chapter reviews the theoretical and empirical literature dealing with the

relationship between hancial variables and investrnent decisions. Section 2.1 describes

the neoclassical position, which is that fmancial factors will not affect firm investment

decisions. Section 2.2 presents several arguments outlining the relevance of hancial

factors for investment decisio ns due tu the existence of capital market imperfections.

Section 2.3 sumrnarizes the relevant empirical evidence, which suggests that financial

factors are an important determinant of corporate investment policy.

2.1, T m IRRELEVANCE OF FINANCIAL FACTORS

2-1.1, The Basic Irrelevance Argument

Modigliani and Miller (1958) demonstrate that a f m ' s fmancid structure wiU not

affect its market value in a world of perfect and complete capital markets. An important

implication is that reai investment decisions will be made based on available growth

opponunities, with no reference to financial factors such as liquidity, leverage or

dividend payrnenu. This result is the foundation of the neoclassical theory of investment

as postulated by Jorgenson (1963) and Jorgenson and HaII (1967). They demonsuate that

a f m ' s optimal investment policy can be solved without reference to fmancial factors.

This approach assumes that al l fms face a cost of capital that is determined by financial

markets, independent of the firms' particular linancial structure.

2.1.2. The Q-Theory of Investment

The q-theory of investment, based upon the work of Brainard and Tobin (1968)

and Tobin ( 1969)' represents an extension of the basic neoclassical argument. The

underlying principle of this approach, according to Brainard and Tobin (1968)' is that

'the market valuation of equities, relative to the replacement cost of the physical assets

they represent, is the major determinant of new investment. Investment is stimulated

when capital is valued more highly in the market than it costs to produce it."

Aiternatively, one could Say that investment is encouraged when market yields on equity

are low, relative to the real returns on investment in physical assets.

Hayas hi ( 1982) presents an important fomulation of the neoclassical mode1 based

on the q-theory approach. He demonstrates that under the assumption of convex costs of

adjusting capital stock, fm investment opportunities can be sumrnarized by the market

valuation of the f m ' s capital stock. He goes on to prove that under certain assumptions,

the ratio of market value of capital stock to its replacement cost (Le. the Tobin's q value)

will be 'the' underlying variable affecthg investment demand.

Hayashi assumes that: (i) managers maximize the expected present value of fiture

profits from capital; (ii) capital is the only quasi-fmed factor; (iii) convex costs of

adjusting the capital stock; and (iv) new capital resulting fiom investment becomes

productive within the year. Under these conditions, the value of the f i is given by:

s.t. KL=( l -G)Kk- ,+I , .

In this equation, i and t denote the fm and time period; K is the beginning of penod

capital stock; n is the profit hinction; 0 is an exogenous shock to the profit hinction; C

is the cost-of-adjustment hinction: 1 is investment; p, is the tu-adjusted relative pnce of

capital goods; A is an exogenous shock to the adjustment cost function; 6 is the constant

rate of depreciation; and E(mIR,) is the expectations opentor conditional on the

information set Cl available to firm i at time t.

The frrst-order condition for maximizing (1) with respect to investrnent is:

m

rvhere q- = (1 - 6IS[nK (KiJts, 6i,t+S) - CK (Ki,l+s y 11 l t s=O

The right-hand tenn in equation (2) is just marginal q, while equation (3) defines q as the

present discounted value of profits from new futed capital investment. Hayashi then

specifïes adjustment cos& to be linearly homogeneous in investment and capital (so that

marginal and average q will be equal). He uses the following convenient

parametenzation that adheres to these constraints:

C ( I i f , K , ) = ( a / 2 ) ( 1 , / K i ~ - a j - ~ i ~ ] 2 ~ , ~ . (4)

This adjustment cosi function allows for a technology shock, d , which may be correkted

with the production shock, 0 .

Substituting the adjustment cost specification in (4) into equation (2) yields the

following investment specirication:

where is an expectation error. As noted previously, under certain conditions, average

Q constructed from fmancial market data may be used as a proxy for marginal q, and the

relation between investment and Q cm be expressed as:

where b = (1 I a) and Q is the tax-adjusted value of Tobin's q (as in Summers(l98 1)).

This is the central equation of the q-theory of investment that descnbes investment

behavior for f m s O perating in fnc tionless capital markets.

2.2. CAPITAL MARKET IMPERFECTIONS AND THE RELEVANCE OF

FINANCIAI, FACTORS

2.2.1. 'Accelerator7 Models of Investrnent Behavior

The idea that financial structure and output are intemelated has a long histow dating

back as far as the time of the Great Depressioo. The coliapse of the fmancial system

dong with real activity prompted Fisher (1933) to argue that poorly performing fuiancial

markets contributed to the seventy of the economic downturn. He argued that the high

leverage in the economy immediately preceding 1929 had both a direct and an indirect

impact on the economy. In particular, he noted that the large number of bankruptcies

caused by the business downtum, was directly related to aggregate leverage. The

bankruptcies, in nim. f i h e r deepened the recession. In addition, the deterioration in

the economy led to a redistribution in wealth from debtors to creditors which had a

significant indirect impact on the economy. He went on to argue that this indirect effect

had an even greater impact on the downturn, because it affected alI borrowers, not just

those on the verge of bankniptcy. The decline in net worth induced borrowers to reduce

current expenditures and future commiunents, which sent the economy into steeper levels

of deflation, He suggested that the simultaneous deterioration in bo rrower balance sheets

and rapidly falling levels of output and prices, offered support for this 'debt-deflation'

story.

Gurley and Shaw ( 1955) demonstrate the importance of the interaction between

fuiancial structure and real activity. They argue that 'financial capacity', as measured by

borrowers' ability to absorb debt without having to reduce current or future spending

commitments, is an important determinant of aggregate demand. This implies that

balance sheets, which are the key determinants of financial capacity, play an important

role in affecting investment levels. Strong balance sheet positions have the ability to

accelerate business cycles by enhancing spending behavior, while weak balance sheets

will have the opposite effect. Investment models based on this notion that financial

factors can mzgnify initiai shocks to the economy are often referred to as "accelerator"

models of investment. Subsequent theoretical works, which are discussed below, focus

on the contribution of capital market imperfections to this accelerator effect on

investment.

2.2-2. The Role for Interna1 Funds

The heavy reliance of h s on intemal hnds for fïnancing requiremcnts is a weli-

documented fact, dating back as far as the 1961 study by Donaldson. Donaldson

examined the fmancing practices of a sample of large corporations and found that

"Management strongly favored intemal generation as a source of new hnds even to the

exclusion of extemal fùnds except for occasional unavoidable 'bulges' in the need for

funds." He also observed that even if extemal funds were required, issuing new stock

would be the last choice of management.

This section outiines the potential importance of intemal funds for fm

investrnent policy in the presence of capital market imperfections. Figure 1 outlines the

basic neoclassical argument graphicaily, similar to the approach used in most

introductory finance textbooks. Firm investment is plotted along the horizontal axis and

the f m ' s weighted average cost of capital (WACC) is plotted along the vertical axis.

The neoclassicd mode1 depicts the f m ' s supply curve of hnds (S) as a horizontal line at

the f m ' s cost of capital, which is given by the market risk-adjusted red rate of interest.

The demand curve for capital (D) is downward sloping to reflect the fact that a decrease

in the cost of funds wiU increase the fum's desired Ievel of investment, The location of

D is a hnction of the fum's available investment opportunities and an increase (decrease)

in these opportunities will shift D to the right (lefi).

The optimal hvestment in capital asseu (1*) occurs at the intersection of D and S,

where the marginal return on capital investment equals the market interest rate. An

FIGURE 1

Firm Investment and Cost of Capital

WACC

s

O

Investment

increase in desired capital stock may be caused by a decline in market rates, an increase

in available investment opportunities, or both. The opportuniiy cost of internai hinds is

assumed to be equal to the cost of extemal hinds, which equals the rate detemiined in the

market. This approach irnplies the availability of internal funds will have no direct

impact ou fm investment decisions, which are detemiined by the availabiiity of

investrnent opportunities and the leve! of market interest rates.

The neoclassical argument assumes b a t fm managers act in the best interest of

fum stakeholders. It also assumes managers and extemal suppliers of fùnds have the

saine information regarding the quantit y and quality of investment op portuniries available

to the fm. These assumptions serve as a point of departure for models that demonstrate

the potential importance of intemal hnds in the investment decision. These models

argue that fm managers have supenor information regarding fm prospects andor that

their objectives do not always cohcide with those of the F m stakeholders. This implies

the cost of extemal funds will exceed that of internal funds, due to costs associated with

adverse selection a d o r mord hazard. As a result, the fxrn's cost of capital will increase

beyond the point at which internal funds are exhausted (W) and we will observe the

fum's supply curve of hnds (S') to be upward sloping beyond W (as depicted

graphically in Figure 1).

The resulting capital investment level (1') will be less than the optimal level (1*)

that is obtained in fnctionless markets, unless the h ' s intemal resources are

greater than or equal to I*. In addition, higher marginal information costs wilI result in a

steeper upward-sloping portion of the supply curve (S'), which implies increased

investment sensitivity to the availability of intemal hnds.

It is worth noting at this juncture that most empirical research in this literature

attempts to validate the foregoing argument by identifying, a priori, firms that will be

particularly sensitive to the availability of intemal funds. The preceding discussion

suggests these fms will typically exhibit low intemal fund availability (Le. Wd*)

andor great susceptibility to the market frictions which cause the cost of external funds

to exceed that of intemal funds (ie. possess steep S' curves). The expectation is that

investment decisions of these f m s will be very sensitive to the availability of intemal

funds. The behavior of these 'constrained' f m s is then contrasted with f m s for which

we expect the neoclassical result to hold (approximately). This group of 'unconsuained'

f m s consists of those with large arnounts of intemal resources (Le. W > I*) and/or those

facing lower market imperfection costs (i.e. small dopes of S') . The relevant empincal

literature is summarized in section 2.3.

Theoretical justification for the existence of a 'wedge' between the cost of

interna1 and external funds appeals to the existence of capital market imperfections such

as transactions costs, tax advantages, costs of financial distress, agency problems and

asyrnmetric information. The next two sections focus on the arguments pertaining to the

existence of asyrnmetric information and agency problems.

2.2.3. Asyrnmetric Information Models

Asymmetric information models are based on the notion that irnperfect

information is held by external suppliers of fùnds regarding the quaiity o r riskuiess of

F i investment projects. This creates the potential for adverse selection problems that

lead to a higher cost for extemal financing in the form of a 'Iemons' premium. This

notion is addressed by Akerlof (1970) who argues that sellers with inside information

about the quality of an asset wi l l be unwilling to accept the price offered by an

uninformed buyer. The buyer will recognize this fact and realize he has offered too much

for the asset if the seller is willing to accept his offer. In other words, the seiier will only

agree to the buyer's price if the asset is a 'lemon'. This asymmetry of information causes

market prices of assets to be lower than if buyers and seUers had the same information,

and the difference is ofien referred to as the 'lemons' premium.

The potential impact of adverse selection problems is extended to credit markets

by Jaffee and Russe11 (1976). and by Stiglitz and Weiss (1981). Jaffee and Russell

demonstrate that the market interest rate must rise a d o r loan size may be limited, when

creditors cannot determine borrower quality. Stiglitz and Weiss (198 1) show adverse

selection problems can lead to an equilibrium that includes credit rationing. They argue

that lenders cannot determine the quality of borrowers by raishg interest rates, since this

will result in higher quality borrowers dropping out of the market. The probability of

default will increase as a result, and leader profits will be reduced. This leads to an

equilibrium condition where interest rates are set at a level where there is excess demand

for Ioans in the market. The net effect is that credit is 'rationed' for sorne borrowers that

were willing to obtaïn loans at the given market rates. The adverse selection problems

will be the most costly for fms where informational asymmetry problems are the

greatest. These f m s rnay be denied loans during periods of tight credit, or forced to

accept stringent lending agreements in the form of restrictive covenants2.

Debt covenants are also imposed as a method of resnictïng opportunistic behavior by management in response to agency problems. This matter will be addressed in greater demi1 in the next section.

Greenwald, Stiglitz and Weiss (1984) and Myers and Majluf (1984) discuss the

impact of adverse selection problems in equity markets. Both of these studies imply new

shareholders will demand a premium in order to offset the Iosses that arise fiom funding

'lemo ns '. The y assume fm managers have CO mplete information regarding the value of

the firm's existing assets and the retums from new projects, while extemal investors have

incornplete information. When management decides whether or not to issue equity, this

provides outsiders with a 'signal' regarding the value of the fm. Greenwald, Stiglitz

and Weiss (1984) argue that 'good' firms will rely primarily on debt fmancing (as in

Ross (1977)). As a result, when f m s attempt to sell equity, it provides the market with a

strong negative signal about the Fm ' s quality, which will be reflected in its market

value.

Myers and Majluf (1984) demonstrate that investments requiring new share issues

will be undenaken only if they increase the wealth of existing shareholders, at the

expense of new shareholders. Asyrnmetric information costs may make it optimal for

existing shareholders to have management turn down some positive NPV projects, rather

than issuing new equity. This behavior would clearly be sub-optimal in a market of

complete information. An important implication of their argument is that financial shck

has value. This refers to the fact that if a fm has sufficient f3inancial resources, it will

never have to turn down any positive NPV projects3.

These theones imply the existence of a "fuiancing hierarchy" where fums follow

a "pecking order" approach to obtaining financing as described by Myers (1984). He

argues f m s prefer to use intemal fmance frst and foremost, since this will enable f ims

to avoid tuming d o m positive NPV projects andor issuing new shares. Firms will

establish their dividend policy in line with this preference and will attempt to keep their

debt as risk free as possible in order to avoid fmancial distress costs and maintain

'financial slack7. This implies that as interna1 funds are exhausted firms will draw down

liquid reserves fist, then they wilI increase short-tenn loans. As additional extemal

funds are required, the fum will proceed to issue Nkier longer-term debt secunties and

finally, as a Iast resort, they will issue new equity.

1 conclude this section by noting that there is signifcant empirical evidence

suggesting that issues of seasoned equity are interpreted as bad news by the market. For

example, Masulis and Korwar (1986), Asquith and Mullins (1986), Kolodny and Suhler

(1985), and Mikkelson and Partch (1986) examine seasoned equity issues and they ail

observe signifïcantly negative announcement date effects on equity prices. These results

support the daim that intemal funds have a cost advantage over external funds.

2.2.4, Agency Models

Agency models argue that extemal siippliers of funds require higher retums to

compensate them for agency costs. Agency costs include the costs of monitoring

managerial actions and the potential moral hazard associated with management's control

over the allocation of investment funds. This Iine of reasoning was pioneered by Jensen

and MeckIing (1976) in their seminal article regarding principal-agent relationships.

They argue that agency costs are unavoidable because managers will be encouraged to

appropnate corporate resources, in the fonn of perquisites, whenever they are not sole

owners of the resources under their controL The total costs consist of monitoring

This is analogous to the condition tbar WA* in Figure 1.

17

expenditures by the principals, bonding expenditures incurred by the agent, and the

residual loss. The residual loss results due to the inadequacy of the monitoring and

bonding processes in constraining management behavior.

There are also significant agency costs associated with debt financing. These

consist oE (i) sub-optimal investment decisions which are made due to the impact of

debt; (ü) monitoring and bonding expenditures; and (iii) baokniptcy and reorganization

costs. The first two costs arise due to the incentive effects associated with the use of

leverage. Equity holders of highly levered firms will prefer that management engage in

high risk, hi@ return projects, since the benefits will accrue primarily to them while the

brunt of the cost is borne by the f m ' s debt holders. Bondholders will attempt to protect

iheir interests through the use of monitoring procedures and debt covenants in order to

prevent this expropriation of their wealth- The result is that managerid decisions are

likely to be sub-optimal, due to the restricted set of actions that will now be available to

them.

Jensen ( 1986) defmes free cash fi0 w as "cash flow in excess of that required to

fund all projects that have positive net present values when discounted at the relevant cost

of capital." He argues that "managers have incentives to cause f m s to grow beyond

optimal size" since "growth increases managers' power by increasing the resources under

their control." He also notes that management compensation is typically tied to growth.

As a result, managers wiil avoid large payouts to shareholders, since this would increase

the likelihood of having to obtain extemal iünds through capital markets that would

scrutinize their behavior. Agency costs arise because management m u t be motivated to

pay out the cash flows to shareholders, rather tha . investing at rates of retum below the

f i ' s cost of capital

Jensen's model implies that firms will uicrease investment in respoose to the

availability of cash flows. He argues that the behavior of oil f m s during the late 1970's

and early 1980's represents a classic example of the free cash flow story. These f i s

experienced substantial cash fiow increases due to a tenfold increase in oil pnces d u ~ g

the 1970s. Rather than paying out these cash flows to shareholders, most oil f m s

increased their research and development expenditures despite experiencing average

retums below the cost of capital. Many oil f m s also launched large diversification

progains durùig this period with very M e success.

These agency arguments have been extended in recent years. Bernanke and Gertler

(1989) outline a frnancial accekrator mode1 of investment based on the existence of

agency costs. They suggest that "higher borrower net worth reduces the agency costs of

financing real capital investments. Business upturns irnprove net wonh, lower agency

costs, and increase investment, which amplifies the uptum; vice versa for downtums."

This effect is due to the fact that it is easier for f m s to obtain outside fimds when their

balance sheets are healthy, which occurs in a greater proportion of f m s during perïods

of strong economic activity.

Bernanke and Geder (1990) argue that as net worth decreases, the borrower will

have less available hnds to contribute to investment projects. This increases the

divergence of interests between the borrower and potentid creditors, and results in an

increase in agency costs. Their model aüows entrepreneurs to undertake costly

evaluations of investment projects. The evaluations provide them with better information

regarding the quality of these projects than is available to extemal providers of funds.

This informational asymmetry creates an agency problem that increases the cost of

extemal finance and affects the entrepreneurs' ~villingness to evaluate projects in the first

place.

Borrowers face greater opportunity costs of proceeding with a project as the i net

worth increases, which makes them more selective. This increases the expected

profitability of the projects and reduces agency costs. When borrower net worth

decreases, borrowers have less incentive to engage in costly project evaluations. As a

result, the quaiity of investment projects fall and agency costs rise. This leads to an

equilibnum where "both the quantity of investment spending and its expected retum will

be sensitive to the 'creditworthiness' of borrowers (as reflected in their net worth

positions). Indeed, if borrower net worth is low enough, there c m be a complete collapse

of investment".

Gertler ( 1992) extends previous work into a multi-penod setting by allowing

borrowers and lenders to enter into ongoing relationships. This sugpsts that the binding

effects of credit constraints will depend upon expected future cash flo ws in addition to

the fim's existing net worth. As a result, future expectations will govern 'fiancial

capacity', which he defmes as the maximum debt overhang an entrepreneur can cany

without having to suspend the project. Similar to the hancial accelerator arguments

above, he demonstrates that fmancial capacity will have a significant impact on economic

growth.

Recent empirical evidence supports the existence of a positive relationship

between net worth and investment outlays. For example, Lamont (1997) documents a

large decrease in the capital expenditures of non-oil subsidiaries of oil conglomerates, in

reaction to the 1986 drop in oil prices. Lamont concludes that large reductions in cash

flow and collateral value Iead to decreased investment, independent of changes in

available investment opportunities. Kaplan and Zingales (1997) also provide evidence

that fm Uivestrnent increases in respoase to balance sheet strength, which will be

discussed in greater detail in section 2.3.4.

2.2.5. Investment Decisions in an Option Theoretic Framework

A related literature demonstrates that firm investment decisions c m be viewed in

the context of option theory Framework. This approach assumes the output prices

associated with long-term investments may be viewed as stochastic variables, whose

future values are uncenain. These variables impact the appropnate discount rates as well

as the net present value (NPV) of available investment opportunities. In this context,

Brennan and Schwartz (1985) argue that the "dynamic aspect of the investment decision

is closely related to the problem of determining the optimal strategy for exercising an

option on a share of common stock."

In the absence of dividends, it is well known that one should never exercise a cal1

option pnor to expiration. Ho wever, in the real world, companies may face situations

where it will be advantageous to hvest at an early stage. Trigeorgis (199 1) identifies two

possible situations where a fm may "fmd it justifiable to exercise its real option to

invest at an early stage": (i) when the present value of irnmediate cash flows (acting as

dividends), exceeds the value of waiting; and, (ii) when the firm c m preempt cornpetitor

entry-

McDonald and Siegel (1988) consider the option value associated with postponing

irreversible investments. They argue that the appropriate investment nile should compare

the value of investing today with the value of investing at all possible times in the future.

Similar to a fuiancial option, uicreased risk will increase the value of this real option,

which provides greater incentive to delay the iovestment expenditure. Based on their

analysis, f i s that adopt zero NPV projects 'too early', may forgo as much as 10-20% of

the potential value of such investment projects. This argument suggests there is value in

'kaiting to invest."

Trigeorgis (1991) suggests that management tlexibility regarding the optimal timing

of hvesunent project initiations results in an "expanded NPV" framework. This implies

the value of a project can be thought of as the sum of the standard NPV of expected cash

flows, plus an option premium refiecting the value of this timing option. During periods

of greater uncertainty and rising interest rates, it may pay fvms to delay positive NPV

projects. On the other hand, during periods of low uncertainty and low interest rates, the

option value may justify entrance into negative NPV projects.

This Iiterature shows the potential benefit of delaying investment projects is

especially high during periods of high uncertainty. This rationale is consistent with the

familiar theme that € i s increase their investment outlays in response to declines in their

cost of capital. Based on the discussion in sections 2.2.1 through 2.2.4 one would expect

investment tu increase in response to increases in the availability of intemal funds (which

will be less expensive than external funds in the presence of capital market

imperfections). This implies it will ofien be advantageous for firms to defer capital

speriding until interna1 resources becorne available.

2.3. EMPIRICAL EVIDENCE

2.3.1. Eariy Empirical Evidence

Meyer and Kuh (1957) examuied the impact of severai fmancial variables on the

invesunent decisions of approximately 750 fms in tweIve manufactu~g industries over

the 1946-1950 penod. They found that increases in sales, profit levels and depreciation

expense had a significant positive impact on fm investment. These effects were more

pronounced during periods of low fm Liquidity- They also found that these f m s were

reluctant to mise extemal fuiance. These results support the relevance of fuiancial factors

in the investment process. Meyer and Kuh have been criticized for not controllhg for the

availability of growth opportunities, which implies the fmancial variables may appear to

be signifcant because they also serve as proxies for growth potential.

The empirical study of Jorgenson and Siebert ( 1968) contradicted the conclusions of

Meyer and Kuh (1957). They examined 15 large manufacturing Furns and found their

investment decisions to be consistent with the neoclassical rnodel and its emphasis on

real factors. Subsequently, Elliot (1973) contradicted the Jorgenson and Siebert

conclusions based on evidence that the liquidity model outperformed the neoclassical

model in accounting for the investment decisions of his sample of 173 frms. Bemanke,

Bohn and Reiss (1988) added hie1 to the debate by demonstrating that al l standard models

of investment can be rejected in cornparison to at least one other modeL

2.3.2. Fazzari, H u b bard and Petersen (1988)

Fazzari, Hubbard and Petersen ( 1988) make two important contributions to the

empirïcal work in this literature. First, they employ the beginning of period Tobin's q

value for a fm as a proxy for growth opportunities. This alleviates criticisms of

previous studies by reducing the informational content of financial variables that are

designed to proxy net worth. Their second major innovation is based on the evidence

provided by Bernanice, Bohn and Reiss (1988) that al1 models of investment fail under

certain circumstances. Fazzari, Hubbard and Petersen (hereafter FHP88) argue that this

result is not surprising if certain classes of f m s are more susceptible to the market

imperfections that drive a wedge between the cost of interna1 and extemal fiance.

Under these circumstances, EUio t's fmding that financial affects are relevant for a

relatively broad sample of fms need not be inconsistent with Jorgenson and Siebert's

results that red factors best explain investment for a group of weil-known mature f m s .

This notion leads them to depart from previous empincal approaches by focusing

attention on the differences in investment behavior exhibited by groups of f rms that are

formed according to thek apparent susceptibility to capital market imperfection

pro blems.

FHP88 examine two theoretical predictions that arise fiom the discussion in section

2.2.2. and are based on the assumption that equation (6)' as denved by Hayashi (1982)' is

represeatative of the neoclassical model. They fïist hypothesize that the q-theory of

investrnent as specifïed in equation (6), should explain investment relatively weil for

fms with high net worth relative to their desired capital stock. Alternatively, one would

expect this model to fail for firms with low net worth relative to desired capital stock,

who wiU face a high premium for external finance. Secondly, they examine the

hypothesis that F m liquidity should not affect the hvestment decisions of the high net

worth group to the eaent it does for the low net worth group of firms.

FHP88 use Value Line data for 422 large U.S. manufacturing firms over the 1970-

84 period. Their selection criteria are designed to eliminate fms in a fmancial distress

situation in order to focus on the investrnent and fmancing decisions of fvms that have

wealth to distribute. With this objective in mind, they select only fms with a complete

history of available fmancial information fiom 1969 to 1984. In addition, only f m s

experiencing positive sales growth over the entire period were included.

They analyze differences in investment behavior by f m s classified accordhg to

earnuigs retention". According to FHP88, f m s with higher retention ratios face higher

informational asyrnmetry problems and are more likely to be liquidity consuained. They

argue that if the cost disadvantage of external Fiance is small, retention practices should

reveal little or nothing about investment. Under this scenario f m s will simply use

extemal fmancùig to smooth investment when inremal finance fiuctuates, regardless of

their dividend policy. However, if the cost disadvantage is signif~cant, fms that retain

in particuiar, FHP88 dassify f m s into the following three groups based on their dividend bebavior over the 1970-84 period: (1) those that have a ratio of dividends to income of less than 0.10 for at least ten years; (2) those that have a dividend-incorne ratio between 0.10 and 0.20 for at Ieast ten years; and (3) al1 other fms.

and invest most of their income, may have no low-cost source of investment fmance, and

their investrnent should be driven by fluctuations in cash flow.

FHP8 8 nin the following regression for the 'q' , neoclassical, and sales accelerator

models of investment5:

( I I K ) , =ah + PiIf ( X I K ) i r ] + P 2 [ g ( C F f K ) , l + ~ i t . (7)

where 1, represents investment in plant and equipment for fm i during period t, K is

the beginning of period book value for net property, plant, and equipment, g(CF / K) is a

function of current cash flow which mesures f i liquidity, f (X / K) is a function of

variables related to investment opportunities, and E, is an error term. For example,

accordhg to the q-theory of investment, f (X / K) is represented by a fm ' s Tobin's

marginal q value.

Contrary to the predictions of neocIassica1 models, FHP88 determine that the

coefficients on the liquidity variable are no t insignificant. More importantiy, investment

of f m that exhaust all their interna1 finance is found to be much more sensitive to

fluctuations in cash How than that of mature, high dividend f m s . They attribute these

results to a fmancing hierarchy in which intemal funds have a cost advantage over new

equity and debt. FHP88 also document a diff'erence across f m s in the sensitivity of

investment to balance sheet variables measuring liquidity. Financial effects on

investment appear to be greatest at times wheo capital market information problerns are

lïkely to be most severe for high-retention f m s , reiriforcing their thesis that fmancing

coostraints in capital markets affect investment. These results are robust to a wide

Q mOde1s emphasize market valuations of fm assets as the determinant of investment, sales accelerator models suggest fluctuations in sales or output motivate changes in capital spending, whiIe neoclassical rnodds combine maures of output and the cost of capital to explain investment demand

varîery of estimation techniques and specifkations. FHP88 suggest their resub probably

understate the me effect of cash flows on investment, since large mature F i s constitute

a greater proportion of their Value Line sample than they do in the aggregate economy.

Major cnticisms of their work have been dong two lines. First, whiie using

Tobin's q to adjust for growth oppominities represents an improvement, q is an imperfect

and insufficient statistic for future cash flows, which may cloud the results. Problems

may arise because: (i) it is difficult to estimate replacement cost of a f m ' s assets; (ü) the

use of an average q may not be a good proxy for marginal q; and (iii) there remain

questions regarding the informational content of stock prices themselves. Secondlyo they

have been criticized for the potential endogeneity of their a priori classification scherne

based on dividend behavior, since it is likely that f m s that fuiance a large portion of

their investment internally, will have lower payout ratios6.

2.3.3. Subsequent Studies

Despite the criticisms, FHP88 remains the most influential study of this issue in

the existing literature. Subsequent studies have c o n f i e d their central result by dividing

samples according to other a priori measures of fmancial constraint for cornparison

purposs. For example, Hoshi, Kashyap and Scharfstein (1991) examine the behavior of

145 Iapanese manufacturing f m s that were continuously listed on the Tokyo Stock

Exchange between 1965 and 1986. They compare the investment-cash flow sensitivity of

24 firms that are not members of a 'Keiretsu' to 121 fms that are members of a

6 These criticisms were raised by Poterba (1988) and Blinder (1988) in their original discussions of the paper. Refer to Schaller (1993) for an insigbtful discussion of these issues.

'Keiretsu' and are presumed to be less fmancially coastrained. They conclude that the

investment of the coastrained (non-Keiretsu) firms is much more sensitive to £ïrm cash

flow, which supports the conclusions of FHP88.

Oliner and Rudebusch (1992) use two pa rae l panel sets covering the 1977-83

period. The fnst panel consists of 99 firms, virtually dl of which were listed on the

NYSE for the entire sample period, wMe the second panel consists of 21 over-the-

counter fms fkom the 1978 and 1984 volumes of Moody's OTC Industrial Manual.

They run the basic FHP regression after preclassification of f m s according to a varïety

of criteria- Their results suggest that investment is most closely related to cash flow for

f i s that are young, whose stocks are traded over-the-counter, and exhibit insider

trading behavior consistent with privately-held information. Schdler ( 1993) categorizes

212 Canadian f m s , over the 1973-86 penod, according to: age; ownership

concentration; manufacturing versus non-manufacturing; and group (e-g. Bronfman and

Reichman groups) versus independent f m s . His regression results indicate investment

for young, independent, manufacturing fums, with dispersed ownership concentration are

the most sensitive to cash flow,

Fazzari and Petersen (1993) add changes in working capital to the basic FHP88

specifcation. Since changes in working capital are positively correlated with sales and

profits, one would expect h e m to have a positive coefficient in the investment regression.

However, the existence of financial constraints may cause firms to draw down working

capital to mitigate temporarily the effect of an adverse shock to cash flow on investment.

Using the W 8 8 panel data, Favari and Petersen fmd that the estimated working-

capital-investment coefficient is negative for the Iow-payout f m s , which hpl ies that

liquid assets perform a "buffer stock" role for fmancially constrained f m . They

suggest these results casts doubt on the notion that the estimated effect of cash flow on

inves tment largely reflects omitted shifts in investment demand.

In a related inquiry, Calorniris, Himmelberg and Wachtel(1995) use bond ratings

or access to bond and commercial paper markets to sort f m s according to fmancing

costs. They fmd that f m s with no ratings or with lower credit ratings (which tend to be

srnaDer f m s with lower dividend payout), hold larger stocks of liquid assets and display

much more cash flow sensitivity of hvestment in working capital. These findings

support the existence of a "buffer stock" role for liquid assets for fmancially constrained

fiims.

An alternative ap proach for testing the relations hip between investment and

liquidity is utilized by Whited (1992), and Bond and Meghir (1994). They employ an

Euler equation approach to directly test the f ~ s t order condition of an intertemporai

maxirnization problem, which does not require the measurement of Tobin's q. It is

implemented by imposing an exogenous constraint on extemal fiance and testing

whether that constraint is binding for a pa~ticular group of F i s . Whited uses a sample

of 325 U.S. manufacturing fms for the 1972-86 penod, while Bond and Meghir use an

unbalanced panel of 626 U.K manufacturing companies for the 1974-86 period. Both of

these studies fud the exogeneous h a n c e constraint to be particularly binding for the

constrained groups of f m s which supports the existence of a fmancing hierarchy arnong

constrained f i s .

A related body of empirical literature, deahg with fm capital structure

decisions, is ais0 supportive of the existence of fmiancing heirarchies. For exarnple,

Mayer (1 990) examines the sources of indusuy finance of eight developed countries f%om

1970 to 1985 and reveals the following stylized facts regarding global corporate fïnancing

behavior: (i) retentions are the dominant source of fmancing in al l countries; (ü) no

countries raise substantial amounts from securities markets in the form of short-term

securities, bonds, or equities; (G) the majority of external fmancing cornes from baok

loans in all countries; and (iv) s m d - and medium-shed f m s rely more heavily on bank

fmancing than larger f m . Shyam-Sunder and Myers ( 1995) analyze COMPUSTAT

flow of funds data for 157 U.S. fums from 1971 to 1989 and find evidence that f m s

foilow a pecking order approach to obtaining funds. Booth, Aivazian, Dernirguc-Kunt

and Maksimovic (1 997) present empincal evidence fro m developing countries over the

1980-1990 period that also supports the existence of a pecking order approach to

O b t aining finance.

2.3.4. Kaplan and Zingales (1997)

The foregoing discussion implies consent regarding the existence of a fiiancing

hierarchy that is mos t prevalent among constrained frrns. However, Kaplan and Zingales

(1997) challenge the generality of this conclusion. They perform an in-depth analysis of

the 49 low-dividend paying fums identified by FHP as having extremely high

investment-cash flow sensitivity. Kaplan and Zingales (hereafier KZ) use a combination

of qualitative and quantitative information fkom annual reports to rank F i s in terms of

the? apparent degree of fimancial constraht. In particular, they use data from letters to

shareholders, management discussions of operatioos and liquidity (when avaiiable),

fiancial statements, notes to those statements for each fm-year, and hancial ratios

obtained from the COMPUSTAT database,

A fm is classified as hancially constrained in a particular year, if the cost or

availability of extemal funds precludes the Company from making an investment it would

have chosen to make had intemal funds been available. Firms are categorized as not

fimaacially constrained if they "initiated or increased cash dividends, repurchased stock or

explicitly indicated in its annual report that the fm had more liquidity than it would need

for investment in the foreseeable future." Firms were "more likely" to be classified as

not constrained if they had a large cash position (relative to investment), or if the f m ' s

lenders did not restrict the fum fiom making large dividend payments (relative to

investment). This classification scheme suggests unconstrained f m s tend to include

fïancially heaithy companies with low debt and high cash. Despite the fact they

determine fuiancial status every year, KZ allocate fms according to one of three groups

for the entire penod for purposes of regression analysis.

KZ provide cross-sectional evidence that suggests their classification scheme

successfully captures the financial constraint characteristics of fims. For example, they

categorize a higher percentage of f m s in the fmancially constrained category during the

recessionary 1974-75 years. In addition, variables such as median cash Bow, Tobin's q,

interest coverage, and 'slack' (cash plus unused h e of credit) decrease monotonically

across their categories. A criticisrn of the onginal FHP paper Fist raised by Poterba

(1988), and examined in greater detail by Gilchrist and Hirnmelberg (1 995), is that their

sorting cnterion is correlated with mismeasurement of Q. KZ suggest their research

design is less subject to this criticism, since their classification scheme is based on direct

observation, which should more accurately rneasure the unobservable variable.

Contrary to FHP88's prediction that this entire group would face severe fmancial

constraints, KZ fmd "in only 15% of f i - y e a r s is there some question as to a firm's

ability to access internal or external funds to increase investment. In fact, alrnost 40% of

the sample frms could have increased investment in every yea. of the sample penod."

Contrary to previo us researc h, the Ieas t fmanciaily constrained f m s exhibit the greatest

investment-cash flow scnsitivity. This pattern is found to persist for the entire sample

penod, for sub-penods, and for individual years. They suggest these controversiai results

"capture general features of the relationship between corporate investment and cash

flow", and are not specifc to the sample or techniques utilized. They c o n f m the

robustness of their results by repeating the analysis usine: (i) alternative definitions of

degree of fiancial constraint based on variables such as interest coverage, dividend

restrictions, debt covenants, and 'sIack'; (ii) four alternative definitions of investment; and

(üi) the Euler equation approach used by Bond and Meghir (1994).

KZ's results suggest policies designed to make credit more avadable during

recessions may not lead to an increase in investment by F i s with high investment cash

flow sensitivities, which has been a policy implication of the existing literature. The

observed high sensitivities appear to be driven by managers choosing to rely primarily on

internal cash flow for investrnent despite the availability of additional low cost extemal

funds. This suggests important policy implications of being able to identiQ the

motivation behind Tum behavior. If the € m s categorized as not fmancially constrained

are t d y unconstrained, then their investmentlfmancing policies can be interpreted as

irrationd, overly risk averse, or the resuIt of a behavioral rule which drives firms to

invest only w hen they are generating cash. On the O ther hand, if f m s are comained in

an intertemporal sense, then we can interpret their policies as value rnaximizing choices

based on the costs of becoming financially constrained in the future.

The KZ conclusions contradict a large body of ernpincal literature, which implies

the importance of scrutinizing their resulrs. Faaari, Hubbard and Petersen ( 1996) and

Schiantarelli (1995) criticize the KZ results because their sorting criteria is somewhat

subjective and relies on possibly self-senring managerial statements. A greater concem

regarding the generality of their conclusions is their use of such a smali, homogeneous

sample. They examine 49 manufacturing f m s that could be considered fairly high

quality f m s , or they would not have been included in the Value Line database. They

further subdivide this sample into three groups of 22, 19 and 8, leaving very few f m s in

the groups for cornparison purposes. This implies the behavior of a very few f i s could

be driving their results, and it seems ambitious to make generd conclusions based on

these observations.

DATA AND METHODOLOGY

Kaplan and Zingales (1997)'s classification of fm financial constraint status

according to traditional financial ratios has intuitive appeal since it represents a direct

measure of the premium paid for bank loans by f i s . Mayer (1990)'s observation that

bank loans are the primary source of extemal finance for firms in developed countries

highlights the importance of this measure. However, the Kaplan and Zingales results

have been criticized because they are based on such a small sample (49 fums) and

because of the su bjectivity involved in their classification SC heme.

This chapter describes how the present study aileviates criticisms of Kaplan and

Zingales by using an efficient mechanism for determinhg fm fimancial status that is

able to deal with large nurnbers of f m s . This multivariate classification scheme is

objective and effectively captures desired cross-sectional properties of f m s . Sumrnary

statistics indicate the index is successful in classifying fxm fmancial status. The index

also allows reclassification of firm fimancial status every period and 1 dow group

composition to Vary over time to reflect changing levels of fmancial constraints, both at

the level of the fm and in aggregate. This represents an improvement over previous

studies that did not allow group composition to vary, which implicitly assumes that

fmancial obstacles faced by f i s do not change over tirne.

3.2. DATA SOURCES

Data was obtained fiom the 199 1 and 1994 COMPUSTAT annual and historic

tapes for US. industrial firms for the purpose of replicating the original FHP88 study in

chapter 4. Details regarding the sample used for purposes of this replication are included

in section 4.1 of chapter 4. Details of the calculation of all fmancial variables used in

chapters 4,s and 6 are included in Appendix 1.

Data for the 201 Canadian f m s and the 1080 US. f m s used in this study were

obtained from the SEC Disclosure Worldscope Database. Only f m s with complete

fuiancial statement information available for the 1987-94 tirne period were included.

Since the majority of f m s have a December fiscal year end, f m s were included only if

theïr last available fmancial statements were reported for fscal year ends occurring

between July of 1994 and June of 1995. The sample includes both manufacturing and

non-manufacturing companies fro m a variety of industries including mining, reso urces,

forestry, transportation, retailers and industrial manufacturers. Banks, insurance

companies, other fmancial cornpanies and utility companies were deleted from the

sample. In addition, several f m s were deleted based on the sample selection cnteria

described in Appendix II, which are designed to eliminate extreme observations. The

imposition of these selection cnteria is consistent with the approach of previous studies,

including FHP88 and KZ, which is to focus on the investing and fmancing behavior of

h s that have wealth to distribute.

3.3. CLASSIFZCATION SCHEME

This study follows the general approach of Kaplan and Zingales (1997) in

focusing on financial variables to determine the financial status of firms in the sample.

This provides useful insight into fm investment decisions since a strong fimancial

position should reduce the premium on external hnds for fms operathg in imperfect

capital markets as argued by Bemanke and GertIer (1989, 1990) and Geder (1992). My

classification scheme is objective and is able to deal with a large sample of fms, which

addresses the two major criticisms of the KZ study.

Firms are classified into groups according to a begùining of period fmancial

constraint index (2, ). Firm classification is allowed to change every penod to reflect

the fact that fmancial status changes continuously7. This point is acknowledged by

Fauari, Hubbard and Petersen (1996) who suggest that assuming fms are in one group

for the entire penod is an empiricd convenience. Schiantarelli (1995) argues that studies

which assign a f ~ m to one group for the entire period are "neglecting the information that

the fmancial constraints may be binding for the same fm in some years but not in others.

It would be more advisable in these cases to allow f i s to transit between different

fmancial states."

7 Empirical evidence supporthg this clah is found in chapters 4,s and 6.

36

3.3.2. Classi fying Financial S tatus Using Discriminant Analysis

The index used to classify firm Fiancial status is detennined using multiple

discriminant andysis, similar to Altman's Z factor for predicting bankruptcy. Altman

(1968) applies this statistical technique to his sample of 66 fïrms over the 1946-65 period

for the purpose of distinguishing f m s that are likely to go bankrupt from those that are

Iikely to avoid bankniptcy. During his sarnple period, 33 f m s go banknipt, while the

other 33 are still in existence at the end of the period. Using discriminant analysis, he is

able to predict with 95% accuracy, which f m will go bankrupt and which f m s will

not. Altman, Haldeman and Narayanan (1977) are able to achieve similar success for 1 1 1

f m s over the 1969-75 penod using a modified set of independent variables in the

discriminant analysis specification.

Discriminant analysis Uivolves choosing mutually exclusive groups with regards

to some qualitative trait (e.g., bankrupt versus non-bankrupt f m s ) . The next step

involves denving a linear combination of characteristics that 'best' discriminates between

the two groups8. The analysis considers an entire profile of characteristics common to

the relevant firrns, as well as the interaction of these properties, and transforms them into

a univariate statistic. The advantage of this technique is that it allows the analysis of the

entire variable profile of a fum simultaneously, rather than sequentiaIly examining the

individud charactetistics.

Altman (1968) uses the following fiancial statement variables to determine the

discriminant score (2) : (i) working cap italhotal assets (WUTA); (ii) retained

earningsl total assets (RVTA) ; (Si) earnings before interest and taxedtotd assets

(EBITTTA); (iv) market value of equityhook value of debt (MVE/BVD); and saledtotal

assets (SaledTA). His fmal discriminant function, including coefficient estimates is:

Z = O.O12(WC 1 TA) + 0.0 l4(RE 1 TA) + O.O33(EBITITA) + O.OOO(MVEI BVD) + 0.999(Sales / TA)

Altman thds the first four variables are univariately signifïcant at the 0.001 level.

Sales/TA assets is not univariately sigificant but has a high 'relative contribution' due to

its high negative correlation with (EBIT;A) Ui the bankruptcy groupg. Altman,

Haldeman and Narayanan (1977) use the following independent variables: (1) return on

assets (ROA); (2) stability of earnings (a normalized measure of ten year standard error

of estimate of ROA); (3) the logarithrn of interest coverage; (4) RUTA; (5) current ratio;

(6) book value of common equitylTA (five year average); and (7) the logarithm of TA.

in order to use discriminant analysis to determine fmancial constraint status, it is

necessary to frst establish two or more mutually exclusive groups according to some

explicit group classification. Unlike Altman, it is difficult, if not impossible, to

categorize explicitly which fims are fmancially constrained without making reference to

a number of variablzs. However, it is still possible to establish two mutually exclusive

groups by making use of knowledge that f i s do not like to cut dividends and are

hesitant to increase them unless they can be maintainedlO. This suggests dividing our

sample into three categones: group 1 f i s which increase dividends and are likely not

fmancially constrained; group 2 f m s which cut dividends and are likely fmancially

8 For a technical description regarding the determination of the discriminant score refer to Appendix III. 9 n ie univariate significance levels are determined using F-tests that examine the individual disniminating ability of each variable by relating the ciifference between the average values of the ratios in each group to the variability (or spread) of values of the ratios within each group. The cornmon F-value tests the nui1 hypthesis that the observations corne &om the same population. if the nul1 is rejected, then it makes sense to move fonvard and use the variable to try to discriminate between the two groups.

constrained; and group 3 f m s which do not change dividend payments. Group 3 h s

wïU not be utilized for purposes of the discriminant analysis, however, they are assigned

discriminant (Z) scores aad are used in the subsequent regression analysis. This group of

f m s represents a sipifkant portion of the samples. For example, it represents about

54% of the U.S. sample (4109 out of 7560 fm year observations) and 60% of the

Canadian sample (8 13 out of 1417 fm year observations). These f m s can be

categorized by reference to their discriminant score as those that 'fit the profile' of

constrained or unconstrained f m s . This enables use of an increased sample size and

requires less reLiance on fum dividend policy for the purpose of a priori classification.

Summary statistics reported in chapters 4, 5 and 6 c o n f m that fms reducing

dividends appear to be more fmancially constrained according to traditional fmancial

ratios. Firms which cut dividends exhibit Iower current ratios, higher debt ratios, lower

futed charge coverage, lower net income margins, lower market-to-book ratios, lower

sales growth, and have lower SLACWK values than f m s which increased dividendsl1.

The standard ratio performance for firms that did not increase or decrease dividend

payments was between the other two groups.

The Canadian and U.S. studies use the following beginning of penod variables:

curreot ratio, debt ratio, fuced charge coverage (FCCov), net income margin (NI%), sales

'O This point is established by Linma (1956). and by Fama and Babiak (1968). It is a well h o w n result ihat has been confmed by several subsequent studies. 11 SLACK is calculated as Cash + Short Term Investmen ts + (0.50*hven tary) + (0.70*Accounts Receivable) - Short Term Loans. It is included as a proxy for cash + unused h e of credit, which is a measure of liquidity utilized by Kaplan and Zingaies (1997). The calculation is based on traditionai aedit line arrangements that enable finns to establish loans up to 50% of inventory and 70-75% of gwd accounts receivabie. 'K' represents the net property, plant and equipment figure obtained fiom the fm's balance sheet, and is used for sctiing pqoses .

growth, and SLACWK'*. The variables were chosen to proxy for fmancial factors such

as liquidity, leverage, profitability and growth, which are likely to impact f m investment

decisions. The hypothesis is that these variables will enable us to predict if h s wiii

increase or decrease dividend payments in the subsequent period. Coefficient values are

estimated for each independent variable which best distinguish between the two groups

according to the foUowhg 2, value:

Zn = p, Curent + P, FCCOV + P,SLACK K + &NI % + &SalesGrowth + &Debt- (9)

The FHP88 replication uses the following variation of equation (9):

2, = &Crcrrent + Pz7i?E ++P,NI% + PJalesGrowth + &Debt. (1 0)

In this equation, times interest eamed (TE) is used in place of fixed charge coverage,

while SLACWK is eliminated from the specification.

Univariate significance levels indicate net income margin, sales growth and debt

ratio are ail significant at the 1% signif3cance level for the Canadian and U.S. studies,

while fuced charge coverage is also significant at the 1% level for the U.S. study. All the

variables excep t sales growth are significant at the 1 % level for the FHP88 replication.

Overall, the variables do a good job of successfülly predicting which firms will cut or

increase their dividends, with group 1 and group 2 f m s being properly classifïed 57% of

the time in the FHP88 replication, 64 9% in the Canadian study, and 77% of the time in the

U.S. study. Despite the practical importance of being able to accurately predict dividend

l2 Alternative speciricalions, including one using the variables in Altman (1968) were also employed. They produced simiIar resdts, but had a slightiy lower success rate in predicting which firms will çut or increase dividends.

changes, it is not the primary concern of this paper13. The focus here is to classify f m s

accordhg to thei financial status, and summary statktics for the predicied group

classification of f m s presented in chapters 4,s and 6 confirm success in achieving this

objective. In particular, f m s that have been classified as group 1 (likely to increase

dividends) appear more solid in terms of the reported fiancial variables.

Firms are classified every year according to their 2, value to reflect the fact that

their fmancial constraint status is changing continuously. The top third of the fims each

year are categorized as not fmancially constrained (WC), the next third as partially

fmancially constrained (PFC), and the bottom third as financiaily constrained (FC) 14.

Summary statistics for these groups presented in chapters 4,s and 6 indicate the

classification scheme has successively captured the desired cross-sectional properties.

The fmancial ratios are superior for the NFC group, inferior for the FC group, with the

PFC group lying somewhere in between15.

The importance of classifying f i fmancial status every year is highlighted by

the observed tumover rates for the groups that are reported in chapters 4, 5 and 6. For

example, tumover in the U.S. study for the MC, PFC and FC groups averages 40.0%,

55.4% and 42.7% per year. In fact only six fums would be classifed as PFC for all

- --

13 In fact, if the purpose was to predict changes in dividend behavior, it would be incorrect to use 'in- sample' observations for the discriminant analysis. 1 * Several alternative grouping schemes were also employed, without resuitïng in an y material changes in the overail results. One of these rneasures used 'fixed' cut-off discriminant scores, which were applied to al1 discriminant scores throughout the sample period, rather than ranking firms every year. F m s were then categorized as constraùieà, partiali y constrained or unconstraineb The resul ts did not vary substan tially 6rom those for the reported groups. This aileviates concems that the nature of the groups changes significantly from one period to the next. This conclusion is supported by annual suuunary statistics, which have &O not ken reported here, but are available upon request, These statistics indicate that financiai ratios do Vary somewhat Eiom one year to the next, however, the NFC group always displays superior ratio performance to the other two groups. l5 This trend persists for sirniiarly formed subgroups within dividend payout categories, exchange groups and industry classifications, aithough the results have not been reported here.

seven years, while only 23 and 65 would be classified as FC and W C for the entire

period. This supports the claim that individual firm financial siatus does change

signifcantly from one year to the next-

3.4. REGRESSION ESTIMATION TECHNIQUES

3.4-1. Panel Data Sets

Panel data provides multiple observations for several hdividuals over time. As a

result, it has both a cross-sectional and time series component. Blundell, Bond and

Meghir (1992) suggest that using panel data for individual f m s to examine investment

behavior has several advaotages over aggregate t h e senes studies including: " biases

resulting from aggregation across fms are eliminated; cross-sectional variation

contributes to the precision of parameter estimates; several variables of interest cm be

measured more accurately at the fm level; and heterogeneity across fvms in, for

example, effective tax rates can be explicitly taken into account." More irnportantly, it

allows the examination of c r o s s - f i differences in investment behavior.

Panel data sets in general are susceptible to two important sources of bias:

selectivity bias -and heterogeneity bias. Selectivity bias arises due to the selection critena

imposed by the researcher in forming his sample. The use of such critena implies that

the sample is not randomly selected from the population. This bias is unavoidable for

studies of fm level investment behavior, since the available data sets tend to include

large, welI-known fms. In addition. severai empincal studies, including this one,

attempt to focus on f m s that have cash to allocate by imposing selection criteria to

e h i n a t e extreme observations. This bias cannot be remedied using existing econometric

techniques, however, the use of similar selection criteria across the studies implies that

cornparison of their results is reasonable16. Heterogeneity bias results fiom differences in

regression parameters among cross-sectional unis (fms) and Me-series units. The

discussion below focuses on methods for dealing with this type of b i s .

3.4.2. Pooled Ordinary Least Squares (OLS) Estimation

This approach pools observations from all 'N' cross-sectional units and from d l

'T' tirne periods, resulting in N*T total observations. The estimates are then obtained

using ordinary Ieast squares (OLS), according to the follo wing specification:

y, =a+p.r, +r i i t , i=1, ........, N und t =1, ......., T. (1 1)

This approach is easy to apply and makes use of all available observations. However, it

is based on the assumption that all cross-sectional units have the sarne intercepts and

slope coefficients, which do not change through t h e .

Biases arise in OLS estimates due to differences in intercepts and slopes across

individu&, and across time. This highlights the importance of accounting for these frm-

specific and tirne-specfic effects in the present study, since theory predicts that

invesunent behavior will differ across firms and will change through tirne in response to

l6 1 WOUM note bat my sample is less subject to this aititism than FHP88 and KZ, since it is mudi larger and is diversifieci aaoss indusmes and by exchange listing. This matter is addressed in greater detail in chapter 6.

changes in ecooomic conditions. The next two sub-sections describe the two basic

specifications used for panel data to account for differences across individuals and

through Ume units. These approaches both maintain the assumption of common dope

coefficients across individu& and t h e . however, they allow for variation in the

intercepts. As such, they provide a simple, yet reasonably general alternative to the

assumption of common parameters across the sample. They are based on the wumption

that the effects of numerous ornitted individual tirne-varying variables are unimportant

individually, but the sum of these effects may be significant.

3.4.3. Random Effects Estimation

Random effects models treat individual and time specific effects as an additional

source of random variation. It is assumed that: some of the omitted variables represent

factors that are unique to both the individual unîts and time periods associated with the

given observations; other factors affect certain individuals in similar fashion tbrough

tirne; and, other factors are unique to a given time period and affect all individuals

similarly. Random effects rnodels assume the individual intercepts are randomly

distnbuted around a mean value ( p ), with the random fluctuations consisting of both an

individual component (ai ) and a the-v-g component (4). These fluctuations are

assumed to have an expected value of zero, with fi'ied variance and are assumed to be

uncorrelated with each other and with other error terms.

The residuals ( v , ) consist of three components and can be represented as:

Cr: ifi= j , t = s , E U , ~ , = and

O othenvise,

In short, the residuds are assumed to have fixed variances (a' ) that are independent

from each other. These models are sometimes referred to as variance components or

error-components modek to reflect the fact that the variance of y, conditional on x, is a

sum of the three individual variances, as given by:

The random effects regression model is given by:

y, = p + pxir +ai +A, + r i , . (1 4)

Generalized l e s t squares estimation provides the best linear unbiased estimates for this

model". The use of the random effects model is appropnate, when the effects c m be

viewed as random drawings from a population, the researcher is interested in population

characteristics and when the number of cross-sectional units (N) is large. However, it

provides inconsistent estimates when there are omitted variables, which is likely the case

l7 Refer to pages 47û475 of Greene (1 993) for computational details of the random effects estimacor.

for the regressions used in the investment literature. In addition, investment theory does

no t predict that residuals of the basic FHP regression equation will be uncorrelated with

the regressors. For these and other reasons, the empirical investment literature has

focussed on the use of fixed effects regression estimates, which are discussed below.

3.4.4. Fked Effects Estimation

Fixed effects estimation generalizes the constant intercept and slope mode1 (OLS)

by allowing the intercept to Vary across individu& and through tirne. This is

accomplished by introducing dummy variables to account for the cffects of those omitted

variables that are specifc to individual units but rernain constant across time (ai), and

for variables that are speciCic to each time period but affect a l l cross-sectionai units the

same (A, ). The fuced effects regression mode1 can be expressed as:

The only required distributional assumption is that the error terms are independently and

identicdl y distnbuted. More irnponantly, these estimates are designed to annihilate the

effects of omitted individual-specific and tirne-specfic variables.

It may be very cumbersome to maintain N individual dumrny variables and T time

dummy variables, particularly when there are a large number of cross-sectional units

included in the panel data set. As a result, there are two cornmonly used f ~ e d effects

estimation techniques, both of whic h transfo rm the actual observations before running

regressions using the transformed variables. The 'within' or 'demeaned' estirnator

subtracts individuai means and time period means kom the actud obsenrations, and then

performs OLS on the transformed variables. Altematively, one can emplo y the 'first

dit3erence9 estimator, which eliminates individual cross-sectional effects by taking first

dïfferences of the observations, and uses t h e durnmy variables to account for t h e -

specific effects.

F i ed effects estimation is very costly in terms of degrees of fieedom lost, and it

ignores between unit information. However, it also has several advantages that make it

well suited for estimating coefficients related to firm investment panel data Decisions

regarding levels of investment in capital equipment depend cntically upon initial

conditions and expectations of future conditions, due to the magnitude of the associated

capital adjustment costs. Modeling expectations, which cannot be directly observed,

implies omitted variables may be important. Further, there is no reason to believe that

individual effects will be uncorrelated with regressors, a s assumed in random effects

estimation. In addition, the prirnary source of available Company information is su bject

to the measurement problems associated with using accounting data to measure capital

stock and determine Tobin's q. Estimating Tobin's q requires the use of market values of

equity, which relies on the implicit assumption of efficient capital markets and this may

introduce additional measurement problems.

The basic investment regression equation used by W 8 8 and several subsequent

studies, is given by:

( Z I K ) , =a, + &Q, +&(CFIK), + E , . (1 6 )

where Q, is the fïrm's beghning of period Tobin's q ratio and CF I K represents firm

cash flow during the period divided by its beginning of period book value of capital

assets. Given the nature of the investment process, it is like1y that the residual term will

contain fm-specific and tirne-specific components. In addition, theory predicts that the

current value of Q wiU be correlated with current shocks (residuals). This irnpiies that

estimators that rely on strïctly exogeneous regressors, such as the random effects

estimator, should be avoided. The importance of this matter is enhanced by the entry and

exit of fkms from available data sources. Entry into these databases is usually reserved

for companies with public stock listings, while exit generally occurs as the result of

bankniptcy or takeover. Both entry and exit processes will therefore be related to fm

investment decisions, and are likely to be correlated with 'shocks' to the investment

equation.

3.4.5. Estimation in This Study

The present study estimates the basic FHP88 regression equation using f i e d fm

and year effects to account for unobserved relationships between investment and the

independent variables, and to capture business-cycle influences:

( I I K ) i t =orit + p M , , ( M / B ) , +PCFlR(CFIK)ir + & i f - (17)

1 represents investment in plant and equipment during penod t, K is the beginning of

period book value for net property, plant, and equipment, CF represents current penod

cash flow to the fxm as measured by net income plus depreciation plus the change in

deferred taxes; and MIB represents the f m ' s common equity market-to-book ratio based

on the previous year's actual market value at year end and is used as a proxy for growth

op portunities.

The use of market-to-book ratio to proxy for growth o p p o d t i e s follows the

approach of KZ . This differs fiom FHP88 who calculate 'Q' based on replacement costs

and the average market value over the last quarter of the previous year, however, Perfect

and Wiles (1994) indicate improvements obtained from the more involved computation

of Q are limited. In addition, KZ point out that using year end market values c m only be

regarded as a methodological improvement, since "the FHP88 rneasure will not

distinguish between a f i whose stock price declines from 20 to IO and a fm whose

stock price increases from 10 to 20 in the last quarter." Current period cash flow (CF),

scaled by 'K', is used to measure the liquidity variable. This follows the specification of

most previous studies including FHP88 and KZ, and facilitates comparison of resulis

with previous evidence.

The equation is estirnated using f ied effects, which is consistent with the

preceding discussion, and facilitates comparison with previous studies, whose estimates

were obtained using this approach. Results are reported for the 'demeaned' or 'within'

fixed fm and year estimates, which coincides with estimates presented by FHP88 and

KZ. 'First daerenced' fixed effects estimates were detennined, but are not reported

here- Generaily, the estimates are consistent with the 'within' estimates in terms of

magnitude and observed patterns across groups18. Hsiao (1 986), Griliches and Hausman

(1986), and Schaller (1993) suggest that obtaining consistent estimates from alternative

panel data estimation techniques, provides evidence of no senous errors in variables

problems.

'' OLS estimates have dso not been reported, however. they are also consistent with the reported fixed effecis estimates in t m s of magnitude and observed patterns across groups.

3.5. EMPIRICAL LEVELS OF SI[GNI[FICANCE

A major fucus of previous studies has been to compare the investment-liquidity

sensitivities across different groups of firms. However, traditional tests designed to

detect differences in coefficients are not appropriate since the error terms likely violate

the required assumptions. Traditional tests are generally designed for testing changes in

parameters across tirne series data, where it may sometimes be reasonable to assume no

heteroscedasticity in the resulting residuals. Panel data, with its emphasis on cross-

sectional data, likely violates the required assumptions. For example, the Chow test

requires that the disturbance variance be the same for both regressions, while the standard

Wald test requires independence of the error terms. These conditions are unlikely to be

satisfied by panel data residuals.

Due to the inadequacy of existhg tests, conclusions regarding the existence of

differences across groups in investment-liquidity sensitivity, have been largely based on

observing differences in magnitude and level of significance of the coefficient for the

liquidity variable in regression estimates. The present study uses simulation evidence to

determine the significance of observed differences in coefficient estimates. The process

uses a bootstrapping procedure to calculate empirical p-values that estimate the

likeliho od of O btaining the O bserved differences in coefficient estirnates, if the tme

coefficients are, in fact, equal.

Observations are pooled fkom the two groups whose coefficient estimates are to

be compared. Denoting 'nl' and 'n2' as the number of annual observations available

from each group, we end up with a total of 'nl+n2' observations every year. Each

simulation randomly selects 'nl ' and '132' observations each year from the pooled

distribution and assigns them to group 1 and group 2 respectively. Coefficient estirnates

are then detennined for each group using these observations, and this procedure is

repeated 5000 times. The empuical p-value is the percentage of simulations where the

difference between coefficient estimates (d i ) exceeds the actual observed difference in

coefficient estimates (dsmple ). This p-value tests against the one-tailed alternative

hypothesis that the coefficient of one group is greater than that of the other group

( H 1 : d >O). For example, a p-value of 0.01 indicates only 50 out of 5000 simulated

outcomes exceeded the sample result, which implies the sample difference is significant,

and supports the notion that d > 019.

l9 Ernpirical p-values, denoted as P(absolute), were J so obtained by testing the nuU hypothesis of equal coefficients ( Ho :d = O ) againsr the Iwo-tailed alternative hypothesis of non-equality of coefficients

( Hl : d t O ). This test is approptiate when theory does not predict which coefficient should be large, and

would be appropriate according to the neoclassical theory of investment A p-value of P(absolute)=0.02

indicates that only 1% (or 50 out of 5000) of the sîmulated absolute value ciifferences (Id ) e x ~ e d e d the

absolute value of the sample différence ( 1 dsample 1). This irnpiies d . O . since it is highly unlikely that

the observed difference is a random occurrence. These pvalues have not ken reported, however, they su bstan tiate the reported 'one-tailed' p-vaiues. In particular, these 'two-tailed' pvalues were found to be significant in every case when the one-tailed p-values were found to be significant.

FAZZARI, HUBBARD AND PETERSEN (1988) REPLICATION

4.1. SAMPlLE CHARACTERISTICS

Data was obtained fiom the 1991 and 1994 COMPUSTAT annual tapes for

industrial f i s for the purpose of replicating the original FHP88 study. These tapes

include information for the previous 20 years, which meant I was unable to obtain

financial information pnor to 1972, since the 1991 tapes were the oldest available to me.

Since the 1972 year-end items are required for regression purposes, my analysis period

was reduced to 1973-84 versus the 1970-84 penod used by FHP88. In addition, I was

only able to obtain data for 245 frms out of their original sample of 422 f m s (Le. 58%

of the original number of F i s ) . Combining these two factors, 1 ended up with only 464

of the original number of fmn year observations. Data availability dso imposed a great

deal of survivorship bias on the sample, since alrnost all of the F i s 1 was able to locate,

had a complete history up until the end of 1991. Not surprisingly, my results were not in

complete agreement with those of the original study.

Group 1 of the original FHP88 study included 49 h s (12% of their total sample

of 422), wbose dividend payout ratios were between O and 10% for 10 of the 14 years

exarnined. 1 was only able to obtain 27 (55%) of these 49 f m s , which resulted in only

44% of the original FHP88 observations (324 versus 735). Group 2 in the original study

included 39 furns (9% of their total sample) whose payout ratios were between 10 and 52

20% for 10 of 15 years, while 1 obtained 19 of these f m s (49%), resulting in 39% of the

observations (228 versus 585). Group 3 of the original study consisted of the 334

remaining f m s (79% of their total sample) with higher payout ratios, while 1 obtained

199 of these f i s (60%), resulting in 48% of the original observations (2388 versus

5010).

Aside from, the small number of f m s (245 versus 1080), the nature of the sample

obtained for the replication has several other features that make it distinct from the US.

sample I examine in chapter 6. First, it de& with a completely different tirne period.

Second, this sample consists of large, well-known companies. This is illustrated by the

fact that the average net fixed assets figure for the f m s in this sample was $1.88 billion

at the end of 1994. This is over twice as large as that of my sarnple of IO80 U.S. f m s ,

which had a mean net fixed asset figure of $779 million at the end of 1994. In addition,

the FHP88 sample is not diversified by industry, consisting of all manufacturing fums,

and the majonty of f m s listed their stock on the New York Stock Exchange.

The frms in the replication study displayed much higher average annual growth

in net fïxed assets than my U.S. sample (8.7% versus 3.7%). They also had much higher

debt ratios (4 1 Q versus 22%), which is consistent with the larger average fm size. Not

surprisingly, the replication sample consisted of a much higher proportion of fnms that

increased dividends (6 1 W versus 39%). It also consisted of a higher proportion of fiims

that decreased dividends (16% versus 7%), which appears curious at Fust glance.

However, this is consistent with the fact that rhis sample contained a higher proportion of

F i s in FHP Group 3 ('high' payout) category (79% versus 47%), and a much lower

percentage in the FHP group 1 ('low' payout) category (12% versus 38%). This implies 53

the h s in the replication sample would have more opportunhies to cut dividends, since

they are generally higher in the frst place.

In summary, the sample used for the RIP replication contaios only 46% of the

observations of the original FHP88 study, and is subject to a great deal of survivorship

bias. It contains a much smailer number of f i than the U.S. sample examined in

chapter 6 and is more homopneous in nature. The FKP88 sample consists primarily of

large, manufacturing f i s that were listed on the New York Stock Exchange, many of

who maintain large dividend payouts. This suggests these f m s are generally iess

susceptible to informational asymmetry problems than the f i s in my U.S. sample.

Summary statistics for the replication sample over the 1973-84 period are included in

Panel A of Table 1.

4.2. FIRM CLASSIFICATION:

4.2.1 Group Characteristics

Firms are classifed using two approaches for purposes of this study: (il according

to their original classification by FHP88 based on dividend behavior during the 1973-84

period; and (ii) according to the approach descnbed in section 3.3.2". The second

approach uses discriminant analysis to determine financial consuaint statu, and requires

two or more mutually exclusive groups according to some explicit group classification. I

" 1 thank Bruce Petersen for providing me with a List of f i s used in the original study, includïng the original group classification.

54

TABLE 1

5 P Sample Summary Statistics (197344)

AI1 financiai variables are for the beginning of fiscal year, except for cash flow and invesunent which represent 6rm cash flow and capitai expenditures during period 't'. The discriminant score [Z) is calculateci using discriminant analysis according to equation (10). A Eûii description of the variables is included in Appendix I Dividend Group I includes fimis whose dividend per share (DPS) increased in year 't', Dividend Group 2 includes firms whose DPS decreased in year 't', while Dividend Group 3 includes hrms that had no change in DPS in year 't'.

PANEL A FHP Selected Financial Ratio Means (1973-84)

Total Sample Dividend Group 1 Dividend Group 2 Dividend Group 3 (increased dividend (decreased dividend (no change in per share) per s h are) dividendpet shxe)

Net Fixecl

Current Ratio 2.53 255 2.4 1 2.58

Times Interest 15.15 Emed

Net Incorne 6 Margin (%)

Market-to- 1.70 Book Ratio

Cash FlowK 0.44 0.46 0.32 0.39

Discriminant -0.0 1 O. 18 -0.33 -0.39 Score (2)

PANEL B Number of Firms per Dividend Group

DIVIDEND 1973-83 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 GROUP 1 (incrcased DPS) 1806 133 160 149 161 188 183 178 168 136 136 54 130

(61%) (54%) (65%) (61%) (66%) (77%) (754) (73%) (6Wo) (56%) (56%) (34%) (53%) 2 (decreased DPS) 468 37 22 28 35 23 30 38 40 57 37 81 40

(16%) (15%) (Wo) (11%) (14%) (9%) (12%) (16%) (16%) (23%) (15%) (3340) (16%) 3 (no change DPS) 666 75 63 68 39 34 32 29 37 52 72 80 75

(23%) (31%) (26%) (28%) (20%) (14%) (13%) (12%) (15%) (21%) (2%) (33%) (31%)

make use of the well-known result that finns do not like to cut dividends and are hesitant

to uicrease them unless they can be maintained. The sample is divided into three

categories: group 1 fvms which increase dividends and are Likely not hmcial ly

constrained; group 2 f m s which cut dividends and are likely fmancially constrained; and

group 3 f m s which do not change dividend payments. Group 3 f i s will not be

utilized for purposes of the discriminant analysis, however, they are assigned

discriminant (2) scores and are used in the subsequent regression analysis. This group of

f m s represents a significant portion of this sample (23%), and they are categorized by

their discriminant score as those that 'fit the profile' of constrained or unconstrained

f i s . This enables full use of the sample for regression purposes and requires less

reliance on firm dividend policy for the purpose of a priori classifcation.

Summary statistics reported in Table I suggests that f m s which reduce dividends

appear to be more fmancially constrained according to traditionai fuiancial ratios,

dthough the evidence is not overwhelming. Firms that cut dividends exhibit lower

current ratios, higher debt ratios, lower interest coverage, lower net income marghs, and

have lower saIes growth than f m s which increased dividends. The standard ratio

performance for f i s that did not increase or decrease dividend payments, was very

close to that of the other two groups. The difference in the ratios is not very pronounced,

unlike the other two samples examined in this study. This is likely attributable to the

homogeneous nature of this sample, which was discussed in the previous section.

Panel B of Table 1 indicates the number of firms increasing (or decreasing)

dividends changes through the years in response to changing economic conditions. The

number of f m s increasing dividends averaged 61% per year over the entire sample 56

period, which is a much higher proportion than found in the other two samples, and more

than one would expect from a random sample of f m s . The yearly percentage of f m s in

this group varied from a low of 34% in 1983 to a high of 77% in 1977. The number of

f i s cutting dividends averaged 16% per year over the entire period, but varied from a

low of 9% in 1974 and 1977, to a high of 33% in 1983. This evidence supports the

notion that f m s face changing levels of fmancial constraints every year, which is the

basis for classifying fm fmancial status every period.

4.2.2. Discriminant AnaIysis

The variables used in discriminant analysis are chosen to proxy for fimancial

factors such as liquidity, leverage, profitability and growth. The hypothesis is that these

variables will help predict if fims will increase or decrease dividend payrnents in the

subsequent period. The replication estirnates the discriminant score (2) using the

following beginning of period variables as outlined in equation (10) of section 3.3.2:

current ratio, debt ratio, times interest earned (TE), net incorne margin (NI%) and sales

growth. Univariate signifcance levels indicate all of these variables, except sales

growth, are significant at the 1 % level.

Table 2 displays correlation coefficients among these variables, as well as those

used in the subsequent regression analysis. There are several large correlations between

Z , and the independent variables including: 0.92 with net income margin; -0.64 with

debt ratio; 0.52 with current ratio; and 0.3 1 with times interest earned, The observed

TABLE 2

Correlations Among Variables ( F W Sample)

AU f i n a n d variables are for the beginning of fiscal year. except for cash flow and investment which represent firm cash flow and capital expenditures during period 't'. Cash flow, investment and slack are al1 scaied by net h e d assets at the beginning of fiscai year 't'. The discriminant score (Z) is calcuIated using - - - dmmmmant anatysis according to equatiw (10). A hdl description of the variables is inciuded in Appendix 1,

Cash Flow/ Fixed Assets

Cunent Ratio

Debfloiai Assets

Times ineterst Eamed

Invesunent/ F i e d Assets

Market-to- Book Ratio

Nec income Margin (96)

Sales Growtù (W

Discriminant Score (2)

Net Fixed Assets

Cash Curent Debu Times Invest Market Net Flow/ Ratio Toiai Interest ment/ -to- Income Fiied Assets Earned Fixed Book Mar@ Assets Asseis Ebûo (%)

Sales Discri- Growth minant

(%) Score (z)

Net Fixed Assets

1 .O0

** Signifiant at the 1% lewl. * Significan~ at the 5% level.

relationships support the importance of firm profitability, liquidity and leverage on the

dividend decision.

The variables do a reasonable job of successfully predicting which fïrms wilI cut

or increase their dividends, with group 1 and group 2 f i s being properly classified 57%

of the tirne. This figure is lower than the success rate in the other two samples, which is

not surprising, given the homogeneous nature of this sample. The primary purpose of

this study is not to accurately predict dividend changes, but to classify f m s according to

their financial status. Summary statistics for the predicted group classification of fms

according to discriminant analysis, confirm success in achieving this objective. Table 3

presents average fmancid ratios for fums that have been classified as group 1 (likely to

increase dividends) versus those classified as group 2 (likely to cut dividends). Predicted

group 1 finw exhibit substantially higher current ratios, net income margins, times

interest e m e d ratios, and have much lower debt ratios.

The next step in the classification process is to classify f i s every year according

to their 2, value to reflect the fact that their fmancial constraint status is changing

continuously. The top third of the f m s each year are categorized as not fuiancially

constrained (WC) , the next third as partially fmancially constrained (PFC), and the

bottom third as fuiancially constrained (FC). Summary statistics for these groups are

presented in Table 3 and indicate the classification scheme has successively captured the

desired cross-sectional properties. The fuiancial ratios are clearly superior for the NFC

group, infenor for the FC group, with the PFC group lying somewhere in between.

The importance of classifying fm fuiancial status every year is highlighted by

the observed turnover rates for the groups that are reported in Table 4. Turnover for 59

TABLE 3

Seltxted Financial Ratio Means (FEltP Sampie)

Al1 financial variables are for the beginning of fiscal year, except for cash flow and investment, which represent f m cash flow and capital expenditures during period 't'. The disaiminant score (2) is caiculated using discriminant analysis according to equation (10). A hl1 description of the variables is included in Appendix 1. Redicted Group 1 includes fSnns that are classified as likely to inaease dividends in year 't' according to discriminant analysis, while Redicted Group 2 includes h n s that are classified as likely to decrease dividends per share @PSI in year 't', The FC, PFC and NFC groups are formed by sorting al1 firms according to th& disniminant scores. Every year, the firms with the lowest discriminant scores (the bottom third) are categorized as fmancially c o n s h e d 0; the next third are categorized as partially finmcïaily consuained (PFC); and the top third are categorized as not fmancially constrained WC).

Predicted Redicted FC f m s P F C f m s NFCfrnns Group 1 Group 2 (financially (partidly (not (IikeIy to (likely to constrained) financiaiiy financially increase decrease constrained) constrauied) DPS) DPS )

Net Fixed Assets (K) SS23m S730m S728m S670m W 6 m

Current Ratio 2.94 2.08 1.97 2.43 3.20

Times 21 .84 8.32 7.22 13.U 26.04 In teres t Earned

Net income 8 3 2 5 9 Margin (95)

Market-to- 2.1 1 1.25 1 -21 1.48 2.4 1 Book Ratio

Sales Growth 15 13 13 15 15 (W

Cash FlowK 0.51 0.36 0.34 0.42 0.56

Discriminant 0.79 -0.90 -1.16 -0.08 1.20 Score (2)

Overali annual average

(1973-84)

1973-74

1974-75

1975-76

1976-77

1977-78

1978-79

1979-80

1980-81

1981 -82

1982-83

1983-84

Number of Firms in

group at least once

# firms in group for al1

12 vears

# fwms in group for 11 of 12 vears

# fvms in group for 10 of 12 vars

# firms in group for 9 of

12 vears

TABLE 4

Group Turnover Statistics (FHP Sarnple)

PFC - 32.2%

3 1.3%

36.1%

32.3%

36.1%

28.9%

27.7%

30.1%

33.7%

32.3%

32.5%

32.5%

187

2 (1.1%)

3 (1 -6%)

13 (7.0%)

9 (4.8%)

6 1

# frrms in group for 8 of

12 vears

# fiirms in group for 7 of

12 vears

# fiirns in group for 6 of

12 vears

# fwm in group for 5 of

12 v a r s

# frrms in group for 4 of

12 vmrs

# fvms in proup for 3 of

12 vars

# firms in group for 2 of

12 vears

# fwms in group for 1 of

12 vears

for the WC, PFC and FC groups averages 17.3%, 32.2% and 18.8% per year. Only 30

f i s would have been classified as NFC for al l 12 years, while only 2 and 23 would

have been classified as PFC and FC for the entire penod. This indicates that individual

fxrn fimancial status does change signifïcantly from one year to the next, and the turnover

is much more drastic in the other two sarnples.

Table 5 indicates the composition of the fimancial constraint groups with respect

to other classifications. It confirms the eficiency of discriminant score classification

Group 1 (hcrease) 2 (Decrease)

3 (No Change)

Predicted Group

1 (Predict Dividend Inaease)

2 fPredict Dividend Deaease)

FHP Group 1 (Pay<lO)

2 (IO<Pay<20) 3 (Pap20)

TABLE 5

Percentage Group Compositions (FEIP SampIe)

Total Sample

PFC -

with respect to dividend changes. In particular, the NFC group consists of 70.2% of

f ~ m s that increase dividends and only 13.5% of f m s that decrease dividends, while the

FC group consists of only 5 1.1 % of fms increasing dividends, and 19.2% of f i s

reducing dividends. Based on the FHP88 method of detennining fum financial constra.int

status, one would expect a larger proportion of high dividend payout T i s and a smailer

63

proportion of low payout firms in the NFC group. The NFC group does contain the

lowea proportion (9.1 %) of the low payout fums (FHP Group 1 firms), however, the

PFC group contains the largest proportion (84.6%) of high payout f m s (FHP Group 3

f i s ) . The FC grûup, as expected has the highest proportion of low payout firms

( 14.3%) and the lowest proportion of high payout firms (78.1 %). Overall, the

composition with respect to the original FHP88 classification does not Vary a great deal

across the groups, which suggests the two fmancial constraint classification schemes

could easily classzy the same fm differently.

4.3. REGRESSION RESULTS

4.3.1. Original FHP88 Dividend Payout Groups

Fixed fm and year effects estimates are determined using the within estirnator

described in chapter 3, based on equation (17) of chapter 3. Results for the entire FHP88

replication sample are presented in Table 6. The evidence suggests that fm investment

decisions are sensitive to investment opportunities as proxied by market-to-book, but are

even more sensitive to liquidity variables. This is consistent with evidence from previous

studies.

Table 6 &O reports estirnates obtained for the onginal W B 8 groups. FHP

Group 1 includes f m s whose payout ratios were between O and 10% for 10 of 15 years,

FHP Group 2 includes fîrms whose payout ratios were between 10 and 20% for 10 years,

while FHP Group 3 contains al1 remaining f m s . The adjusted R-squared values range 64

TABLE 6

Regression Estimates for the Total Sample and the Original FHPûû Groups (FHP Sample)

Reported coefficients are the 'within' fixed Erm and year estirnates over the 1973-84 sample peciod. T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable. The fmn's market-to-book ratio and cash flowlnet fïxed assets are the independent variables. The groups are formed according to the original FHP88 classification, where: FHP Group 1 includes fims whose payout ratios were between O and 10% for IO of 15 years; FHP Group 2 inciudes fimis whose payout ratios were between 10 and 20% for IO years; and FHP Group 3 contains aii remaining fims. The empirical p- values are determined using the simulation procedure describeci in chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. The alternative hypothesis is that the coefficient for the first group îs greater than that of the second group. For example, the pvaiue of 0.7614 in the market-to-book cotumn for FHP Group 2 versus FHP Group 1. suggests the market-to-book coefficient for FHP Group 2 is greater than tbat for FHP Group 1 at the 76.14% si,anincance level. The 0.0028 p-value in the next colum suggests that the coefficient estimate for Cash FlowiNet Fixed Assets is greater for FHP Group 2 than for FHP Group 1 (at the 0.28% level of significance). P-values in bold indicate a signifiant difference in coefficient estimates at the 5% level,

Market-to-Book Cash Flow/Net Adjus ted Num ber of Fuced Assets R-squared Observations

Regression Estimates

Total Sample 0.012 (5.2) 0.3 10 (20.5) 14.88% 2940

FHP Group 1 0.016 (1.4) 0.24 (6.5) 13.û24c 324

FHP Group 2 O.Oû4 (0.5) 0.703 (8.0) 24.74% 228

FHP Group 3 0.010 (5.0) 0.306 (18.0) 14.50% 23 88

FHP Groups 1 and 2 0.016 (2.2) 0.311 (8.9) 15.03% 552

FHP Group 2 versus 0.7614 0.0028 FHP Group 1

W Group 3 versus 0.69û4 0.2534 FHP Group 1

FHP Group 3 versus 0.2420 0.9880 FHP Group 2

FHP Group 3 versus 0.7378 0.4730 FHP Groups 1 and 2

from 13.02% to 24.74%, which is consistent with previous studies. As a result of the

limitations of my sample discussed above, 1 only have 324 observations available for

FHP Group 1 and only 228 observations available for FHP Group 2. In order to forrn a

'reasonableT sample size, 1 &O include a group consisting of both FHP Groupl and FHP

Group 2 fms2'.

The estimates for the 3 groups change drastically from those reported by FHP88,

especially for groups 1 and 2. In particular, the cash flow coefficients for groups 1,2 and

3 are estimated at 0.240,0.703 and 0.306 versus the m 8 8 estimates of 0.46 1,0.363 and

0.230. Part of this may be attributable to the use of market-to-book rather than Tobin's q

in the regression equation, however, the main cause is likely attributable to the fact that 1

only included 46% of their original sample. The effect is greatest for the 'smaller'

groups, since they had a limited number of observations available in the original study.

Based on these results, it appears FHP Group 2 is more sensitive to liquidity than FHP

Group 1 at the 0.28% significance Ievel, and more sensitive than FHP Group 3 at the

1.20% significance levelz2. FHP Group 3 appears to be more sensitive than FHP Group

1, however, the difference is only significant at the 25.34% level. The cash flow

coefficient for the combined FHP Groups 1 and 2 is 0.3 1 1 which is very close to the

estimate for FHP Group 3. This suggests there is very Little difference in sensitivity to

liquidity if we broaden the low dividend payout categories and compare the behavior of

" I would note that KZ forrn conclusions based on such small numbers of observations as 113,279 and 327. " The observed empirical pva iue of 0.9880 tests against the alternative hypothesis that the cash flow coefficient estimate for group 3 is grtater than the coefficient estimate for group 2. This impties that the empincal pvalue would have ken 0.0120 had the alternative hypothesis been respecified to test that the coefficient for group 2 exceeded that of group 3.

fms ~ i t h p a y ~ u t ratios pnerally below 20W, with f i s whose payout ratios are

geuerally above 20%.

nese results do not support the original FHP88 results. Apparently, the 27 f m s

from Group 1 that were available for my sample were not as sensitive to liquidity

over the 1973-84 period, as the original 49 firrns were over the 1970-84 period.

Siniilarly, the results indicate the 19 FHP Group 2 f i s in my sample were much more

sensitive to iiqbidity than the original 39, while the 199 FHP Group 3 f m s were slightly

mare seasitive to liquidity than the original 334 f m s . The change in coefficients is very

d-tic For Gr~ups I and 2, which demonstrates the importance of h a h g an

adequate number of Iirms in a group for cornparison purposes, since it appears the

behaviot of a f c ~ F i s can bave a significant effect on overail conclusions. The

iap~rtahce of tbk matter is highlighted by the fact that a slight variation in the

cki$iîicatio~ approach süggests there are no signifïcant differences in cash fiow

estimates acfoss the two groups (FHP 1 and 2 versus FHP Group 3).

4.3.2. Rj'iriaocial Constraint Groups Based on Discriminant Analysis

k e g ~ ~ i o n results for the FC, PFC and W C groups presented in Table 7 indicate

the market-to-book coefficients are fairly similar across the groups, however, the

estflates are significant For the FC and PFC groups, but not for the NFC group. The

coefficient esthates for the Liquidity variables are positive and significant at the 1 % level

for ail &ee groups, which suggests f i investment decisions are sensitive to the

availability of uiten*l hnds. The estirnates indicate that the NFC frms are more 67

Regression Estimates for the Financial Constraint Groups (FHP Sample)

Reported coefficients are the 'within' fked frnn and year estimates over the 1973-84 sample perïod. T- statistics are in parentheses. Capital expenditures divided by net fmed assets is the dependent vafiable. The fum's market-tebook ratio and cash flowinet fixai assets are the independent variabIes. The FC, PFC and NFC goups are formed by sorthg ail f m s according to their discriminant scores. Every year, the f m s with the lowest disaiminant scores (the bottom third) are categorized as financially constrained (FQ; the next third are categorized as partially financiaily constrained (PFC); and the top tbnd are categorized as not fmancially constrained (NFC). The number of observations for the PFC group is Iarger than the orher two because the 'left' over Eixms are assigneci to the PFC group because the total number of finns is not a multiple of three. The ernpirical pvalues are detennined using the simulation procedure describeci in chapter 3. They are estiaiated based on the nul1 hypothesis that the coefficients are equai for the two eroups under consideration. The alternative hypothesis is that the coefficient for the f ~ s t group is greater C

than that of the second group. For example, the p-value of 0,7166 in the market-to-book column for NFC versus FC. suggests the market-tebook coefficient for the NFC group is greater than that for the FC group at the 71 -66% significance levei. The 0.0130 pvalue in the next column suggests that the coefficient estirnate for Cash FlowINet Fixed Assets is greater for the NFC group than for the FC group (at the 1.30% level of significance). P-dues in bold indicate a significant difference in coefficient estirnates at the 5% level.

Market-to-Book Cash FlowMet Adjusted Num ber of Fixed Assets R-squared Obsemtions

Regression Estimates

FC fms (financially 0.0 1 O (2.8) 0.320 (10.2) I 1.06% 972 constnined)

PFC f i s (partially iinancialiy 0.023 (3.5) 0.325 (10.8) 12.89% 996 constrained)

M.% f m s (not fmancially 0.006 (1.8) 0.456 (14.7) 2 1.20% 972 consuaincd)

Empirical P-values

PFC versus FC 0.1056 0.1136

NFC versus FC 0.7166 0.0130

NFC versus PFC 0.9848 0.1 126

sensitive to liquidity than that of PFC and FC f m s , with coefficient estimates on CFlK

of O.456,0.325 and 0.220 respectiveiy. These observations offer support for the KZ

results based on a Iarger sample and using an objective classification criterion. The

empincd p-values indicate the difference between the W C and FC fum estimates is

signifcant at the 1.30% level.

The dBerence betwecn the NFC and PFC estimates is only significant at the

1 1.26% level, despite a 0.13 1 difference in coefficient estimates. The PFC F i s appear

to be more liquidity sensitive than FC fms, however, the difference is only significant at

the 1 1.36 % level. This occurs despite a large observed difference in coefficient

estimates (0.325 versus 0.220), and despite the fact that these estirnates are based on

reasonably large group observations of 972 and 996. ï'hese points highlight the benefit

of calculating empincal values, as well as the potential impact of using smali groups for

cornparison purposes. For example, FHP88 conclude their group 1 f m s are more

liquidity sensitive than their group 2 F m s based on observed liquidity coefficients of

0.461 and 0.363, and based on total observations for each group of 735 and 585. The

results above suggest that such differences may not be significant due to the small

numbers of observations involved.

The evidence above offers some support for the KZ conclusions, as it appears that

unconstrained f m s are more sensitive to liquidity than fums facing greater constraints

according to traditional financial ratios. In order to examine the generaiity of this result

across different categories of fûms, 1 divide the entire sample into the original FHP

TABLE 8

Regression Estimates for Fuiancial Constralnt Sub-Groups Within FXP Groups (FHF' Sampie)

Reported coefficients are the 'within' f i e d fnm and year estirnates over the 1973-84 sarnple period. T- statistics are in parentheses. Capital expenditures divided by net fked assets is the dependent variable. The fmn's market-tebook ratio and cash flowlnet fixed assets are the independent variables. The groups are formed according to the original FHP88 classification, where: FHP Group 1 includes h s whose payout ratios were between O and IO% for 10 of 14 years; FHP Group 2 includes firms whose payout ratios were between 10 and 20% for 20 years; and FHP Group 3 contains aU remaining fm. The FC, PFC and NFC groups are fonned by sortïng firms within a given FHE' group according to their disaiminant scores. Every year, the fms in the group with the lowest discriminant scores (the bottom thitd) are categorized as financially constrained (FC); the next third are categorized as partially financially constrained (PFC); and the top third are categorized as not f inandiy constrained WC). The number of observations for the PFC group may be larger than the other two because the 'left' over firms are assigned to the PFC group when the total number of f i in a group is not a multiple of tIuee. The empmcal p-values are determined using the simulation procedure described in chapter 3. They are estimated based on the nuU hyputhesis that the coefficients are equd for the two groups under consideration. The alternative hypotbesis is that the coefficient for the fist group is greater than that of the second group. For example, the pvaiue of 0.7374 in the market-to-book column for NFC versus FC in FHP Group 1, suggests the market-to-book coefficient for the NFC group is p a t e r than that for the FC group at the 73.74 % significance level. The 0.0716 p- value in the next coluaui suggests that the coefficient estimate for Cash Flow/Net Fied Assets is greater for the NFC group than for the FC goup in FHP Group 1 (at the 7.16 % level of significance). P-values in bold indicate a significant difference in coefficient estimates at the 5% level.

Market-to-Book Cash FlowMet Adjusted Number of Fixed Assets R-squared Observations

PANEL A - FEIPGroup 1

Regression Estimates

FC f m s 0.027 (1.5) 0.177 (4.8) 20.39% I O8

PFC finns -0.004 (-0.2) 0.199 (2-3) 3.00% 108

NFC Cirms 0.009 (0.6) 0.476 (5.2) 22.80% 108

PFC versus FC 0.8666 0.4358

NFC versus FC 0.7374 0.0716

NFC versus PFC 0.3 1 14 0.1588

PANEL B - FEiP Group 2

Regression Estimates

FC finns

PFC f m s

NFC fms

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

Regression Estimates

FC f m s

PFC F i s

W C finns

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

Regression Estimates FC firms

PFC fms

NFC fms

PANEL C - FEIP Croup 3

PANEL D - FHP Group 1

and 2

Empirical P-values

PFC versus FC 0.6288 0.2790

W C versus FC 0.8336 0.0342

NFC versus PFC 0.7204 0.1 132

groups, and then sub-divide these groups according to discriminant scores every year as

above to determine the FC, PFC and W C groups within each dividend payout category.

This procedure results in several sub-groups that are very homogeneous in nature with

respect to dividend behavior, and isolates the impact of financial health from dividend

polic y. Table 8 presents regression results for these su b-gro ups.

The results in Table 8 lend some support to the generd results above. The

investment of the NFC fms are the most sensitive to Iiquidity in ail payout groups,

however, the PFC frms are the second most sensitive only for FHP Group 1 and FHP

Groups 1 and 2 (combined). Despite some very large differences, there is only one

significant difference in liquidity coefficient estimates - the W C estirnate of 0.599 in

FHP Groups 1 and 2 (combined) versus the FC estimate of 0.200. This is consistent with

previous evidence that comparison of coefficient e s h a t e s across 'small' groups will not

provide conclusive evidence.

The replication study was severely limited by data availability. My analysis

period was reduced to 1973-84 versus the 1970-84 period used by FHP88 and I was only

able to obtain data for 245 f m s out of their original sample of 422 f m s (i.e. 58% of the

original number of fms). As a result of these two factors, my sample contained only

46% of the original number of f i year observations. Data availability also imposed a

great deal of s u ~ v o r s h i p bias on the sample, since almost dl of the f m s I was able to

locate, had a complete history up until the end of 1991. Not surprisingly, my results do

not coincide with those of the original FHP88 study.

Despite the limitations, several important results are determined in this study.

Tables 1 and 4 provide evidence that f rm Fiancial constraint status does change through

tirne, as reflected in the number of fms increasinp and decreasing dividends, and by

changes in discriminant score group classification. Table 3 provides evidence con fuming

the effectiveness of the discriminant score classification scheme in distinguishing

between f m s that are more or less constrained with respect to traditional fuiancial ratios.

Regression results do not support those of RIP88, and suggest there is no

significant pattern in cash flow sensitivity across different dividend payout categories of

f i s . The evidence does support the results of Kaplan and Zingales, suggesting that

f i s that appear to be Iess constrained according to traditional financial ratios (NFC

f i s ) , are more sensitive to cash flow than f m s which are more distressed. Finally, the

use of p-values indicates that some large differences in group coefficient estimates may

not be statistically significant, paxticularly when one or more of the groups involved is 73

small. The problems associated with the use of small groups for cornparison purposes is

&O highlighted by the drastic difference in coefficient estimates h m the original FHP88

study for the 'smaller' groups (i-e. FHP Groups 1 and 2).

THE CANADIAN SAMPLE

5.1. SAMPLE CHARACTERISTICS

This chapter examines annual data for 20 1 Canadian corporations with complete

Financial statement information available over the 1987-94 period obtained from the SEC

Disclosure Worldscope Database. Details of the calculation of fuiancial variables utilized

are included in Appendix 1. Since the majority of fvms have a December fiscal year end,

f i s were included only if their 1 s t available fmancial statements were reponed for

fiscal year ends occurring between July of 1994 and June of 1995. Banks, insurance

companies, other fmancial companies and utility companies were deleted from the

sample. In addition, a number of other f m s were deleted based on the sample selection

cnteria described in Appendix II, which are designed to eiiminate extreme observations.

The sample t h e penod represents a penod of slow growth for Canadian

corporations in response to adverse economic conditions. The existence of high interest

rates contributed to a 1989-90 recessionary period, and recovery from this recession was

very slow due to several factors including: (i) the maintenance of high interest rates by

the Bank of Canada to combat inflationary pressure caused by the 1991 introduction of

the Goods and Services Tax; (ii) substantial downsizing by Canadian corporations in

response to increased international cornpetition as a result of the introduction of the North

Amerïcan Free Trade Agreement; and (fi) increasing amounts of govemment cutbacks as

federal, provincial and municipal governments attempted to reduce fiscal deficits.

The sample includes bo th manufactu~g and non-manufac turing CO mpanies fro m

a variety of industries. It hcludes: 52 agriculturai, minuig, resource and foresuy

companies with primary SIC codes between 1 and 1,999; 98 industrial manufacturing

companies with prirnary SIC codes between 2,000 and 3,999; 37 retail and wholesale

companies with primary SIC codes between 5,000 and 5,999; and 14 service companies

with primary SIC codes between 7,000 and 8,999. Surnmary statistics for the entire

sample are found in Table 9.

5.2. FTRM CLASSIFICATION

5.2.1 Group Characteristics

Firms are classified using three approaches for purposes of this study: (i)

according to theû original classification by FHP88 based on dividend behavior during the

1988-94 period; (ii) according to another rneasure based on dividend payout that allows

fum classification to Vary every year in response to changing dividend payout ratios; and

(iii) according to the discriminant score approach described in section 3.3.2.

The FHP groups were formed similarly to the original FHP88 study. Al1 48 firms

with dividend payout ratios between O and 10% for 5 of the 7 years examined were

assigned to FHP Group 1, the 5 fiims with payout ratios between 10 and 20% for 5 of 7

years were assigned to FHP Group 2 and the 87 f m s with payout ratios above 20% for 76

TABLE 9

Canadian Sample Summary Statistics (1988-94)

A11 financial variables are for the beginning of Gscd year, except for cash Bow and investment which represent ibn cash flow and capital expeaditures during period 't'. n e discriminant score (2) is calcuIated using discriminant analysis accordhg to equation (9). A fui1 description of the variables is induded in Appendix 1. Dividend Group 1 includes firms whose dividend per share (DPS) increased in year 't', Dividend Group 2 includes fkms whose DPS decreased in year 't', while Dividend Group 3 inciudes firrns that had no change in DPS in year 't'.

PANEL A Selected Financial Ratio Means (1988-94)

Total Sample Dividend Group 1 Dividend Group 2 Dividend Group 3 (increased dividend (decreased dividend (no change in per share) per share) dividend per siiare)

Net Fïxed $762111 S820m SI012 S668m Assets (K)

Current Ratio

Debflotal Assets

Fixed Charge Coverage

Net Incorne Margin (9%)

Market- to- Book Ratio

Sales Growth (%)

Slack/K

Cash FlowlK

InvestmentK

Discriminant Score (Z)

PANEL B Number of Firms per Dividend Group

DIVIDEND CROUP 1988-94 1988 1989 1990 1991 1992 1993 1994 1 (increased DPS) 376 89 79 55 3 1 42 29 5 1

(26.78) (44.3%) (39.3%) (27.4%) (15.4%) (20.940) (14.3%) (3.4%) 2 (decreased DPS) 218 19 20 37 60 40 28 13

(155%) (9.5%) (lO.O%) (18.3%) (29.Wo) (19.Wo) (13.9%) (7.0%) 3 (no change in DPS) 8 13 93 102 109 110 119 1 44 136

(57.8%) (46.3%) (50.8%) (54.2%) (54.7%) (59.34) (71.6%) (67.7%)

at least 5 of 7 years were assigned to FKP Group 3. In addition, 61 f m had negative

payout ratios andlor payout ratios exceeding 100% in 3 or more years, and these fums

were assigned to FHP Group O. The nature of this sarnple is substantiaUy different from

the FHP88 sarnple with respect to dividend behavior. The percentage of fums in group 1

is 24 %, which is double the percentage of low payout fums in the original FHP88

sainpie, while the percentage of high payout f ~ m s is o d y 43%, versus 79% in the W 8 8

sample. Obviously, fums in the present sample do not pay out dividends to the extent of

those in the W 8 8 sarnple. This may be attributed to the different time period being

exarnined, as well as the diversified nature of the Canadian sample, which contains many

'smdler' companies from a variety of industries.

The second classification divides the sample into dividend payout groups, but

allows fums to be reclassified every year in response to changes in their dividend payout

ratio in the previous year. This is consistent with the advocated approach of allowing

fim status to be determined every period. Firm-year observations are delegated to four

groups: (i) those with zero dividend payout (the Payû group); (ii) those with O to 30%

payout ratios (the Paye30 group); (üi) those with payout ratios greater than 30% (the

P a p 3 0 group); and, (iv) those with negative payout ratios (the Pay Negative group).

There are 455 observations for the PayO group (32% of the total); 322 observations for

the Payc30 group (23% of the total); 457 observations for the P a p 3 0 group (33% of the

total); and 173 observations for the Pay Negative group (12% of the total).

The third approach classifies f m s into groups every year according to the

fmancid constraint index (Z,,), which is determined using equation (9). The sample is

divided into three categories as descnbed above: group 1 fiims which increase dividends 78

and are Ikely not fmancially constrained; group 2 firms which cut dividends and are

likely fmancially constrained; and group 3 fms which do not change dividend payments

and are no t used for purposes of the discriminant analysis.

Summary statistics for the 1988-94 period are provided in Table 9 for each of

these groups. The difference between f m s that increase and those that decrease

dividends is much more pronounced in this sample than was the case for the FHP88

replication sample, which is consistent with the more heterogeneous nature of this

sample. Firms that cut dividends appear much more lkely to be fmancially constrained

according to traditional financial ratios. They have lower current ratios, higher debt

ratios, lo wer Fued charge coverage, lower net income margins, lower market-to-book

ratios, lower sales growth, and have lower SLACKK values than fvms which increased

dividends. Tabie 9 also shows the standard ratio pedormance for f r m s that did not

increase or decrease dividends, was between the other two groups.

Panel B of Table 9 indicates substantid changes in the number of Fims that

increase and decrease dividends through the yean. The largest number of f i i s

increasing dividends (89) occurred in the pre-recessionary year of 1988, while the largest

number of f i s cutting dividends (60) occurred in 1991. This provides additional

evidence that Frm Fiancial status changes in response to business cycles, and suggests

there are benefïts associated with reclassifying fm status every period.

5.2.2. Discriminant Analysis

The discriminant scores are detennined for the Canadian sample using equation

(9) of chapter 3. The following beginning of period variables are used to proxy for

liquidity, leverage, profitability and growth: current ratio, debt ratio, fixed charge

coverage, net incorne margin, sales growth, and SLACWK Univariate significance

levels indicate net income margin, sales growth, and debt ratio are all significant at the

1% significance level, while current ratio and fvted charge coverage are signircant at the

13% and 1 1 % levels. Correlation coefficients presented in Table 10 indicate a strong

correlation between the discriminant (2) score and net income margin (0.84), as well as

with the debt ratio (-0.48). These relationships are very similar to those observed in the

FHP replication sample. Unlike the FHP replication sample, we also observe a very

strong positive correlation between the discriminant score and sales growth of 0.55.

The variables do a good job of successfully predicting which fms will cut or

increase their dividends, with group 1 and group 2 f i s being properly classified 64% of

the time. Summary statistics for the predicted group classification of f i s are presented

in Table 1 1. They indicate that fims have been successfully classified according to

traditional fuiancial ratios. Firms that have been classified as likely to increase dividends

(Predicted Group l), appear much more solid than f i s that have been classified as

likely to decrease dividends (Predicted Group 2), in terms of all fmancid variables

reported.

Correlations Among Variables (Canadian Sample)

Al1 financial variables are for the beginning of fiscal year, except for cash tlow and investment which represent firm cash flow and capital expenditures during period 't'. Cash flow, investment and slack are al1 scaled by net fixed assers at the beginning of fiscal year 't', The discriminant score (2) is caiculated uskg discriminant anaiysis according to equation (9). A hiIl description of the variables is included in Appendix 1.

Cash Current Debtl F ~ e d Invest- Market Net Sales SlacW Discri- Flow/ Ratio Total Charge ment/ t Income Growth Fixed minant Fixed Assets Cover- Fixed Book Margin (%) Assets Score Assers age assets Ratio (96) (z)

Cash Flow/ 1 .O0 Fixed Assets

Current 0.06* 1.00 h t io

Fixed Charge O. I 1 ** 0.09** -0.17** 1.00 Coverage

Investrnentl 0.17** 0.02 -O.l4** O, 1 1 ** 2 -00 Fixed Assets

Market-to- 0.18** 0.53** -O.I8** 0.18"" 0.19** 2.00 Book Ratio

Net incorne 0. I9** 0.10** -0.26** 0.23** 0.21 ** 0.27** 1.00 Mrirgin (96)

Sales Growtb 0.03 0.09** -0.08** 0.03 0.22** 0.15** 0.21** 2 .O0 (W

S lacW 0.27** 0. 1 1 ** -0.09** O.Os** O. 11 ** 0.13** O. 1 I** 0.0 1 1 .O0 Fixed Assets

Discriminant 0.14** 0,13** -0.48** 0.09** 0.28** 0.28** 0.84** 0.55** 0.05 1.00 Score (Z)

* Significant at the 5% level. ** Significant at the 1% level.

Selected Financial Ratio Means (Canadian Sample 1988-94)

Ali financial variables are for the beginning of fiscai year, except for cash flow and investment, which represent fim cash flow and capital expenditures dirruig period 't'. The discriminant score (2) is calculated using discriminant analysis according to equation (9). A ful l description of the variables is included in Appendix 1. Predicted Group 1 includes finns that are classified as Iikely to inaease dividends in year 't' according to discriminant analpis, while Predicted Group 2 includes f m s that are classified as likety to decrease dividends per share (DPS) in year 't*. The FC, PFC and NFC groups are fonned by sorting al1 F i s according to their discriminant scores. Every year, the firms with the lowest discriminant scores (the bottom îhird) are categorized as finanMy constrained (FC); the next third are categorized as partially financially constrained (PFC); and the top third are categorized as not financially constrained (NFC).

Predicted Predicted FC f i s PFC firms W C f i s Group 1 Group 2 (financially (partially (not (likely to (Iikely to consuained) financidl y financiaüy increase DPS) decrease DPS ) constrained) constrained)

Net Fuced Assets S75Sm S769m S626m SlOOOm S659m

Current Ratio 3.73 1.71 1.77 1.86 3 .O7

Fixed Charge 13.7 5.9 6.5 Coverage

Market-to-Book 1.56 1.15 1-15 1.24 1 -67 Ratio

Sales Growtù 23.7 1.5 0.8 6.8 30.7 (W

Cash Flow/K 0.43 0.20 0.20 0.3 1 0.44

Discriminant O. 10 -0.93 -1.20 -0.10 1 .O0 Score (Z)

The sample is divided into three groups of 67 f m every year according to their

Z,, value. Every year, the firms with the highest Z scores are assigned to the NFC

group, the ones with the Iowest values are assigned to the FC group, and the remaining

fms are assigned to the PFC group. Table 1 1 includes summary statistics for these three

groups that c o n f m the effectiveness of this approach in capturing desired cross-sectional

properties. Similar to the results for the FHP replication sample, the fmancial status of

the NFC firms is superior to that of the PFC fms, while the FC fms appear to be more

constrained than both the PFC and M T fms.

Table 12 reports turnover rates for the NFC. PFC and FC groups which average

33.8%, 46.5 % and 32.1 % per year. These are much higher than those observed for the

FHP replication sample. Further, 74% (or 149) of the total 201 firms were classified as

W C in at least one year, with figures of 81% and 73% for the PFC and FC groups. This

confms that individual firm fmancial status does change ssignificantly from one year to

the next. In fact, only 1 f r m would have k e n classified as PFC for ail seven years, while

only 15 and 12 would have been classified as W C and FC for the entire penod.

Table 13 confms the efficiency of the classification scheme with respect to

dividend changes, as the NFC group consists of the smallest proportion of firms that cut

dividends, and a larger proportion of frms increasing dividends than the FC firms. The

NFC group includes a much Iarger proportion of resource companies than the other two

groups, which could indicate that the classification scheme is picking up an industry

effect. In addition, the WC category contains a higher proportion of high payout f m s

and a lower proportion of low payout f i s , according to dividend groups formed using

the original FHP approach, or using the tirne-varying dividend classification scheme. 83

TABLE 12

Group Turnover Statistics (Canadian Sample)

NFC - - PFC - FC

33.8% 465% 32.1% Overall annual average

(1 988-94)

Numher of Firms in

gr ou^ at least once -

# fms in group for al1 7

vears

# firms in group for 6 of

7 v a r s

# f m s in gr0UD for 5 of

7 ymrs

# f m s in grour, for 4 of

7 vears

# f m in group for 3 of

7 vears

# f m s in group for 2 of

7 vcars

# fms in grour, for 1 of

7 v a r s

TABLE 13

Percentage Group Compositions (Canadian Sample)

Total - Sampie

PFC -

Dividend Group 1 (Incrase)

2 (Decrease) 3 (No Change)

SIC - Group

1 (Resources) 2 (Manufacturing)

3 (Retail) 4 (Service)

Predicted Group

1 (Predict Dividend increase)

2 (Predict Divîdend Decrease)

FHP Group O (PaycO)

1 (Pay <IO) 2 ( 1 O<Pay<20)

3 (Pap20)

Pavout Group O (PaycO) 1 (P~Yo)

2 (Payc30) 3 e W 3 0 )

This sugge-sts there is some degree of commonality among classification schemes based

on dividend behavior and those based upon direct observation of fmancial variables.

5.3. REGRESSION RESULTS

53.1 Total Sample and Dividend Payout Groups

Fked effects estimates, based on equation (17) of chapter 3 are presented for the

entire Canadian sample in Table 14. The evidence suggests that fiim investment

decisions are sensitive to investment opportunities as proxied by market-to-book, but are

even more sensitive to liquidity. The estimated coefficients are 0.023 for market-to-book

and 0.034 for the cash flow terrn. These differ somewhat from those obtained by Schaller

(1993) in his examination of 2 12 Canadian f m s over the 1973- 1986 penod, who obtains

fixed effects coefficient estimates of 0.007 and 0.242 for Tobin's q and C F K This may

be attributable to the different tirne penods being examined, as the estimates are quite

close to the esrimates of 0.043 and 0.058 for Tobin's q and CF/K that are obtained by

Cummins, Hassett and Hubbard (1996) for the Canadian f'irms they examine over the

1982- 1992 penodu. The adjusted R-squared value of 1 -84% for the entire sample is

quite low in cornparison to estimates in previous studies and indicates that we must view

the regression results with caution".

interestingly. the CFlK coefficient estimate obtained by Cummins, Hassett and Hubbard is not signifiant, which is inconsistent with the results of most previous studies. '' For example. the adjusted R-square value for the entire sample obtained by Schailer is 20.4%. while Cummins, Hassett and Hubbard obtain a value of 1 1.4%.

86

'FABLE 14

Regression Estimates for the Total Sample and for the FBP Dividend Groups (Canadian Sample)

Reported coefficients are the 'within' fmed finn and year estimates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditures divided by net b e d assets is the dependen t variable. The fïnn's market-to-book ratio and cash flowhet fmed assets are the independent variables. The groups are formed similar to the original FHP88 ciassification, where: FHP Group 1 includes firms whose payout ratios were between O and 10% for at least 5 of7 years; FHP Group 2 includes firms whose payout ratios were between 10 and 20% for at Ieast 5 years; Fi-lP Group 3 contains finns with payout ratios between 20 and 100% for at lest 5 years; and FEIF' Group O con tains l b n s wi th negative payout ratios andor payou t ratios above 100% for at least 3 years. The empincal p-values are detennined using the simulation procedure described in chapter 3. They are estimated based on the null hypothesis that the coefficients are equai for the two groups under consideration. The alternative hypothesis is chat the coefficient for the Fust group is geater than that of the second group. For example, the p-value of 0.43 84 in the market- to-book column for FKP2 versus FHPl. suggests the market-to-book coefficient for FHP Group 2 is greater than that for FHP Group 1 at the 43.84 % significance level. The 0.1894 p-value in the next colwnn suggests that the coefficient estimate for Cash Fiow/Net Fixed Assets is greater for FHP Group 2 than for FHP Groupl (at the 18.94 % level of significance). P-values in bold indicate a significant difference in coefficient estimates at the 5% level.

Market-to-Book Cash Flow/Net Adjusted Number of Fixed Assets R-squared Observations

Regression Estimates

Total Sample

FHP Group O

FHP Group 1

FHP Group 2

FHP Group 3

Empirical P-values

FHP3 versus FHF2

FHP3 versus FHPl

FHP3 versus FHPO

FHP2 versus FHPl

FHPî versus FHPO

0.023 (2.26)

0.115 (5.13)

0.003 (O. 16)

0.040 (0.75)

-0.014 (-0.72)

0.4886

0.6248

0.9476

0.4384

0.7036

Table 14 also includes regression estimates for the FHP dividend payout groups.

The adjusted R-squared values range kom 1.07% for FHP Group 3 to 36.10% for FHP

Group 2. The market-to-book ratios are insignifcant for three of the four groups, which

may account for the O bserved low R-squared values. The CFlK coefficients are positive

and significant for ail four groups, which is consistent with the results of previous studies.

The cash flow coefficients for FHP groups O, 1, 2 and 3 are estimated at 0.109,0.140,

0.292, and 0.024. These estimates suggest FHP Group 2 is the most sensitive to liquidity,

followed by FHP Group 1, FHP Group 0, and fmally by FHP Group 3. The low

sensitivity exhibited by the high payout group appears to offer some support for the

onginal FHP88 results at fxst glace. However, despite the magnitude of some of the

observed differences, none of them are statistically ~ i g ~ c a n t according to the empirical

values.

Table 15 presents regression estimates for the the-varying dividend payout

groups that are discussed in the previous section. Once again, we observe insignificant

market-to-book coefficient esthates for three of the four groups. PLU of the CFIK

coefficient estimates are positive and three are significant at the 5% level, while the

estimate for the Payc30 group is significant at the 1 1 % level. The coefficient estirnate is

highest for the Pay Negative group (0.238), second highest for the PayO group, third

highest for the Pap30 group and is lowest for the P a y d 0 group. m i s offers some

support for the FHP88 results, since the low payout f ims appear to be more sensitive to

liquidity. However, as before, despite the magnitude of some of the observed

differences, none of them are found to be statisticdly significant. The insignificance of

Regression Estimates for Tie-Varying Dividend Payout Croups (Canadian Sample)

Reported coefficients are the 'within' fixeci fum and year estimates over the 2988-94 sample period. T- statistics are in parentheses. Capital expenditures divided by net fïxed assets is the dependent variable. The f i ' s market- tc~book ratio and osh flowlnet fixed assets are the independent variables. The Pay Negative group represents the group formed using firm year observations where the nnn's dividend payout was l e s than zero; PayO represents zero dividend payout firm years ; Pay <30 represents payouts between O and 30%; and Pay >30 represents payouts between 30 and 100%. The empirid p-values are determined using the simulation procedure described in chapter 3. ïhey are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration- The alternative hypothesis is that the coefficient for the Grst group is p a t e r than that of the second group. For example, the pvalue of 0.7W in the market-to-book colurnn for Pap30 versus Pa*, suggests the market-to-book coefficient for the Pap30 group is pater than that for the Pa$ group at the 70.44 % significance level. The 0.7446 p-vatue in the next column suggests that the coefficient estimate for Cash FlowMet Fixed Assets is greater for the Pap30 group rhan for the Pa@ group (at the 74.46 % level of significance). P-values in bold indicate a signifiant difference in coefficient estimates at the 5% level.

Market-t&Book Cash FlowMet Adjusted Num ber of Fixed Assets R-squared Observations

Regression Estimates

Pay negative firms 0.013 (0.3)

Empirical P-values

Payû versus Pay 0.5752 negative

Paye30 versus Pay 0.0130 negative

Pap30 versus Pay 0.4358 negative

Pap30 versus Payû 0.7044

Pap30 versus 0.9870 Paye3 O

Paye30 versus Pa$ 0.0130 0.8938

89

observed differences across the groups is not surprising given the small number of

observations available for some of the groups. This reinforces the importance of having

an adequate number of firms in a group for cornparison purposes, since it appears the

behavior of a few f m s can have a significant effect on overall conclusions.

5.3.2. Financial Consbaint and Industry Groups

Regression results for the FC, PFC and NFC groups presented in Table 16

indicate wide variations in the market-to-book coefficient estirnates across the groups,

from -0.001 for the NFC group, to 0.058 for the FC group, to 0.083 for the PFC group.

The estimates are significant for the FC and PFC groups, but not for the NFC group. The

coefficient estimates for the liquidity variables are positive for dl three groups, however,

the estimate for the NFC group is very small (0.00 1) and is insignifcant.

Contrary to the KZ conclusions, these estimates indicate that the FC firrns are the

most sensitive to liquidity, followed by the PFC f m s , while the W C f m s are relatively

insensitive to the availabitity of intemal funds. Empincal p-values indicate the difference

between the NFC and FC fm estirnates is significant at the 2.70% level, while the

differences between the FC and PFC estimates and between the PFC and NFC estimates

is insignificant, despite some rather large differences in estimates.

Table 13 indicated that the NFC group consists of a higher proportion of resource

f i s than the other two groups, which may impact the regression results. To examine

this matter, 1 divide the sample into the four industry categones described in section 5.1.

Regression estimates obtained for the different industry groups are reported in Table 17. 90

TABLE 16

Regression Estimates for the Financial Constraint Groups (Canadian Sample)

Reported coefficients are the 'within' Gxed fm and year estimates over the 1988-94 sarnpie period. T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable. The fhn's market-to-book ratio and cash flowlnet fixed assets are the independent variables. The FC, PFC and NFC groups are formed by sorting al1 firms accordhg to their discriminant scores. Every year, the f m s with the lowest discriminant scores (the bottorn third) are categorized as fmancialiy constrained (FC); the next third are categorized as partially financiaiiy constrained (PFC); and the top third are categorized as not frnanàally consirained (NFC). The empiriml p-values are determined using the simulation procedure described in chapter 3. They are estimated based on the nul1 hypothesis bat the coefficients are equd for the two groups under consideration. The alternative hypothesis is that the coefficient for the first group is greater than that of the second group. For example, the p-value of 0.9382 in the market-tsbook colurnn for NFC versus PFC, suggests the market-tebook coefficient for the NFC group is greater than that for the PFC group at the 93.82% significance ievel. The 0.7600 p-value in the next column suggests that the coefficient estimate for Cash Flow/Net Fixed Assets is greater for the NFC group than for tbe PFC group (at the 76.00% llevel of significance). P-values in bold indicate a signifiant difference in coefficient estimates at the 5% level.

Market-to-Book Cash Flow/Net Adjusted Number of Fixed Assets R-squared Observations

Regression Estimates

FC f m s

PFC f w s

NFC f i s

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

TABLE 17

Regression Estimates for hdustry Groups (Canadian Sample)

Reported coefficients are the 'within' f ïed f m and year estirnates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditrires divided by net fixed assets is the dependent variable. The f i ' s market- to- book ratio and cash flowhe t fixed assets are the independen t variables. The SIC 1 group includes 52 argïcuItud, rnining, resource and forestry companies with primary SIC codes between 1 and 1,999; SIC2 includes 98 industrial rnanufacturing companies with primary SIC codes between 2,000 and 3,999; SIC3 includes 37 retail and w h o l d e companies with primary SIC codes between 5,000 and 5,999; and SIC4 includes 14 service companies with primary SIC codes between 7,000 and 8,999. The e m p m d pvalues are determined usïng the simulation procedure describeci in chapter 3. They are estimated based on the null hypothesis that the coefficients are eqwi for the two groups under consideration, The alternative h ypotbesis is that the coefficient for the first group is greater than that of the second goup. For example, the p-value of 0.7312 in the market-to-book column for SIC4 versus SIC3, suggests the market-to-book coefficient for the SIC4 group is greater than that for the SIC3 group at the 73.12 % significance level. The 0.2234 pvalue in the next column suggcsts that the coefficient estimate for Cash FlowB\let Fied Assets is greater for the SIC4 group than for the SIC3 group (at the 22.34 % Ievel of significance). P-values in bold indiate a signifiant difference in coefficient estirnates at the 5% level.

Market-to-Book Cash Flow/Net Adjusted Number of Fixed Assets R-squarecl Observations

Regression Estimates

SIC 1-1999 f i s (SICI)

SIC 2000-3999 f m s (SIC2)

SIC 5000-5999 funs (SIC3)

SIC 7000-8999 finns (src4)

Empirical P-values

SIC4 versus SIC3

SIC4 versus SICl

SIC3 versus SIC2

SIC3 versus SICl

SIC2 versus SICl

The market-to-book coefficient estimates are insignifcant for al l four groups, while the

cash flow coefficients are a l l positive. The CFlK coefficient estimates are si@icant for

all industry groups except resource-based companies (the SIC1 group). This may

account for the observed insignifcance of the CFIK coefficient for the W C group, since

it contains a much higher proportion of these f i s (42%) than is present in the FC group

(16%), the PFC group (20%), or the entire sample (26%). The coefficient estimates

suggest that resource fms are less sensitive to intemal hind availability than fms in

other industries, however, the p-values indicate the difference is only statistically

significant between the resource fums and manufacturing f m s (the SIC2 group). It is

noteworrhy that manufacturing f m s comprise the entire FHP88 sample, and these fums

tend to pay higher dividends than resource f m s , on average.

The focus of this study is to examine the generality of conclusions regarding the

investrnent sensitivity of various groups to the avaiinbility of intemal funds. The focus of

most previous studies, excluding Schaller (1993), has been the examination of U.S. firms.

This suggests that extending these results into a Canadian setting, is a usehl exercise.

The nature of the Canadian sample is substantially different from the sarnple used for the

FHP replication in several ways, apart from the obvious country difference. The sample

spans a different time period and is somewhat diversified across industries, unlike the

RIP sample, which consisted only of manufacturing f i s . It contains a higher

proportion of low payout fums and a lower proportion of high payout f m s thm is 93

present in the FHP sample. In addition, the oumber of F i s is smaIIer (201 versus 245),

and the sample period is shorter (7 versus 12 years), which results in a substantially

smaller number of avaiIable observations (1407 versus 2940).

The liquidity coefficients are positive and signifcant for 7 of 8 groups formed

based on dividend behavior, which is consistent with the results of previous studies,

including the FHP replication. The cash flow coefficient estimates suggest that fms

with high dividend payouts are the least sensitive to internd fund availability, which

offers some support for the onginal FHP88 results. However, despite the magnitude of

some of the observed differences, none of them are statistically significant according to

the empirical values.

Contrary to the KZ conclusions, the liquidity coefficient estirnates indicate that

the FC f m s are the most sensitive to liquidity, foIlowed by the PFC f m s , while the

NFC f m s are relatively insensitive to the availability of internai hinds. Empirical p-

values indicate the difference between the NFC and FC estirnates is statistically

significant, while the other differences are insignificant, despite some rather large

differences in estimates. Regression results for industry groups, suggest these results

may be partially attributable to the fact that the NFC group consists of a higher

proportion of resource firms than the other two groups. In particular, coefficient

estimates suggest that resource fm s are less sensitive to intemal fund availability than

f i s in other industries, however, the p-values indicate the differences are generdly

statistically insignificant.

The results in this study are not directly comparable to those of FHP88 and KZ

because of the differences in the samples. The concIusions offered by the Canadian study 94

offer weak support for the FHP88 results and do not suppon the KZ results. By contrast,

the FHP replication results do not support the FHP88 results, but do offer some support

for the KZ conclusions. The inconclusive nature of both of these studies may be

attnbutable to the smaU number of observations available for some of the groups in these

sarnples. as well as the pronounced industry effects. The small number of observations

results in some large observed differences in coefficient estimates, which are found to be

insignificant. This reinforces the importance of having an adequate number of f m s in a

group for cornparison purposes. Alternatively, it may be the case that the observed

differences across the groups are really not significant. The next chapter uses a large

diversified sample in order to provide more substantive evidence regarding whether or

not there r e d y are significant differences in investment behavior across different

categories of finns-

THE U S SAMPLE

6.1. SAMPLE CHARACTERISTICS

The US. sample consists of 1080 US. fms that have complete fmancial

information available for the 1987- 1994 penod on the SEC Worldscope Disclosure data

set. It includes 603 NYSE listed companies, 324 NASDAQ cornpanies and 153

companies listed on the AMEX or other U.S. exchanges. The sample is also diversifed

across industries as measured by their primary SIC code: 723 manufac tu~g f m s (SIC

codes 2000-3999); 73 agricultural, mining, forestry, fshing and construction f m s (SIC

codes 1-1999); 172 retail and wholesale trade h s (SIC codes 5000-5999); and 112

service fvms (SIC codes 7000-8999).

Details of the calculation of financial variables utilized are included in Appendix

1. Since the majority of frms have a December fiscd year end, f rms were included only

if their last available financial statements were reported for fiscal year ends occurring

between July of 1994 and June of 1995. Banks, insurance companies, other fmancial

companies and'utility companies were deleted from the sample. in addition, a number of

other f m s were deleted based on the sample selectioo criteria desctibed in Appendix II,

which are designed to eiiminate extreme observations. Summary statistics for the entire

sample are presented in Table 18.

U.S. Sample Summary Statistics (19ûû-94)

All financiai variables are for the beginning of fiscal year, except for cash flow and investment, which represent f m cash Eiow and capital expenditures during period 't'. The discriminant score (2) is calculateci using discriminant analysis according to equation (9). A lüll description of the variables is included in Appendix 1. Dividend Group 1 includes f i whose dividend per share (DPS) inaeased in year 't', Dividend Group 2 includes fbns whose DPS decreased in year 't', while Dividend Group 3 includes firms that had no change in DPS in year 't'.

PANEL A Selected Financial Ratio Means (1988-94)

iICILiLI

Total Sample Dividend Group 1 Dividend Group 2 Dividend Group 3 (increased dividend (decreased dividend (no change in per s hare) per s hare) dividend per share)

Net Fixed

Current Ratio

Fixed Charge Coverage

Net income Mar@ (9%)

Marke t-to- Book Ratio

Cash FlowK

Discriminant

P r n L B Number of Firms per Dividend Group

D'IVIDEND GROUP 1988-94 1988 1989 1990 1991 1992 1993 1994 1 (increased DPS) 2913 496 489 428 369 367 372 39 1

(38.5%) (45.Wo) (45.3%) (39.6%) (34.2%) (34.0% (34.3%) (36.240) 2 (decrcased DPS) 539 44 60 81 109 96 77 72

(7.1 %) (4.1%) (5.6%) (75%) (IO. 1%) (8.9%) (7.1 Qo) (6.7%) 3 (no change in DPS) 4109 540 53 1 57 1 602 617 63 1 617

(54.4%) (50.M) (49.2%) (52.90) (55.7%) (57.1%) (58.4%) (57.1%)

97

6.2. GROUP CLASSIFICATION

6.2.1. Group Characteristics

Firms in the U.S. sample are classified using the same three approaches used for

the Canadian study: (i) according to the FHP88 classification scheme, based on dividend

behavior during the 1988-94 penod; (ü) according to the tirne-varying dividend payout

measure described in section 5.2.1; and (üi) according to the discriminant score approach

described in section 3.3.2. The FHP groups were formed based on seven year average

dividend payout ratios, which is slightly different than the approach used to f o m the FHP

groups for the Canadian study.

Based on the fust classification approach, the 413 fms with average dividend

payout ratios between O and 10% were assigned to FHP Group 1, the 156 f ims with

payout ratios between IO and 20% were assigned to FHP Group 2, and the 51 1 F i s with

payout ratios above 20% were assigned to FHP Group 3. Based on the second approach,

F i - y e a r observations are delegated to three groups: (i) those with zero dividend payout

(the Payû group); (5) those with O to 30% payout ratios (the Pay<30 group); and (fi)

those with payout ratios greater than 30% (the P a p 3 0 group). There are 3428

obsenrations for the PayO group (45% of the total), 1830 observations for the Paye30

group (24% of the total), and 2302 observations for the P a p 3 0 group (3 1 % of the total).

The nature of this sample is substantially different fiom the previous samples with

respect to dividend behavior- For example, the perceotage of low payout f m s in RIP

Group 1 is 38%, which is well above the figures for the Canadian and FHP samples of

24% and 1 1 %. The percentage of FHP Group 3 (high payout) fms is 47%, which is

sirnilar to the Candian sample percentage of 43%, but well below the FHP figure of 79%.

These observations are consistent with the use of a larger and more diversified sample.

The third approach classifies firms into groups every year according to the

fiancial consuaint index (Z,, ), which is determined using equation (9). Summary

statistics for the 1988-94 period provided in Table 18, indicate that f i s cutting

dividends appear much more Likely to be fmancially constraioed according to traditional

fiancial ratios. They have lower current ratios, higher debt ratios, lower f i ed charge

coverage, Io wer net income margins, lower market- to-book ratios, lower sales growth,

and have lower SLACWK values than f m s which increased dividends. Table 18 ais0

shows the standard ratio performance for fms that did not increase or decrease

dividends, was between the other two groups. This supports the evidence in both the

Canadian and FHP replication studies. Panel B of Table 18 confms that the number of

f i s increasing and decreasing dividends changes through the years. Similar to the

Canadian evidence, the largest number of fums increasing dividends (496) occurred in

the pre-recessionary year of 1988, while the largest number of f m s cutting dividends

( 109) occurred in the recessionary year of 199 1.

6.2.2. Discriminant Anaiysis

The discriminant scores are determined for the U.S. sample using equation (9) of

chapter 3. Similar to the Canadian sample, the following beginning of period variables

are used: current ratio, debt ratio, futed charge coverage (FCCov), net income margin

(NI%), sales growth, and sL,AcK/KZ~. Univariate ~ i ~ c a n c e levels indicate net income

margin, sales growth, debt ratio and fuced charge coverage are al i significant at the 1%

level. Correlation coefficients presented in Table 19 indicate the following variables

exhibit strong correlations with the discriminant (2) score: net incorne margin (0.82);

sales growth (0.59); fvced charge coverage (0.34); and the debt ratio (-0.32). Overall, the

relationships are very similar to those observed in the previous samples, except for the

fact that the discriminant score is weakly correlated with current ratio in this sample (-

0.01).

Discriminant analysis is much more successful in predicting which frms will cut

or increase their dividends for the US. sample than for the other two sarnples. Group I

and group 2 f m s are properly classified 77% of the time, versus 57% for the FHP

sample and 64% for the Canadian sample. This is consistent with the use of a larger and

more diversified sample, where the differences between the group ratios are easier to

distinguish. Table 20 indicates that f m s classified as likely to increase dividends

(Predicted Group l), have a stronger fmancial position than f m s classified as likely to

decrease dividends (Predicted Group 2). As before, f m s are classified as FC, PFC and

NFC every year according to their Z, value. Table 20 confms the superionty of the

financial ratios for the NFC fwms, the inferior financial status of the FC firms, with the

PFC f m s falling somewhere in between.

26 Alternative specificaùons, including one usïng the variables in Altman (1968) were also employed with similar results, but with a slightly lower success rate in predicting which fms will cut or inuease dividends.

TABLE 19

Correlations Among Variables (U.S. Sample)

All financial variables are for the beginaing of fiscal year, except for cash flow and investment which represent fjrm cash flow and capital expenditures during period 't'. Cash flow, investment and slack are ail scaied by net fixed assets at the beginning of fiscal year 't'. The discriminant score (Z) is calculated using discriminant analysis according to equation (9). A fdl description of the variables is included in Appendix r.

Cash Flow1 Fixed Assets

Current Ratio

De b t/Total Assets

Fixed Charge Coverage

Investmen t/ Fixed Assets

Market- to- Book Ratio

Net incorne Margin (95)

Sales Growth (%)

Slack/ Fixed Assets

Discriminant Score (2)

cash Flow/ Fiied Asse ts

1 .O0

O. Id**

-0.28**

0.30**

0.42**

0.35**

0.30**

0.24**

0.44**

0.34**

Current Ratio

1 .O0

-0.33**

0.24**

O. 12**

-0.02

0.20**

0.03*

0.44**

-0.01

Fiied Charge Cover-

a s

1 .O0

0.21**

0.24**

0-28 * *

O. 13**

0.17**

0.34**

Invest- ment/ Fixed assets

1 .O0

0.25**

O. 17**

0.29**

0.34**

o z * *

Market - to- Book Ratio

1 .O0

0.24**

0.22**

0.04**

0.33**

Net lncome Mirgin

WTO)

1 .O0

0.28**

0.06**

0.82**

Sales Slack/ Discri- Growth Fmed minant

(%) Assets Score (a

f .O0

0.06** 1.00

0.59** 0.01 1 .O0

* Signifiant at the 5% level. ** Signifiant at the 1% level.

TABLE 20

Selected Financial Ratio Means Sample 1988-94)

Al1 financial variables are for the beginning of fiscal year? except for cash flow and invatment, which reprcsen t finn cash flow and capitai expenditures during period 't'. The disahinan t score (2) is calculated using discriminant analysis according to equation (9). A hl1 description of the variables is included m Appendix 1. Predicted Group 1 includes fims that are classified as Iikely to inaease dividends in year 't' according to discriminant analysis, while Predicted Group 2 includes f m s that are classified as likely to deaease dividends per share (DPS) in year 't'. The FC, PFC and NFC groups are furmed by sorting dl f m s according to their discriminant scores. Every year, the firms with the lowest discriminant scores (the bottom third) are categorized a s financiaiiy constrained (FC); the next third are categorized as partially financially constrained (PFC); and the top ihird are categorized as not fiaanciaIiy constraüied (NFC).

Predicted Predicced FC f i PFC f m NFC f m s Group 1 Group 2 (finmciaily (partially (not (likely to (likely to constrained) financialiy financially increase decrease constrained) consuained) DPS) DPS )

Net Fixed Assets (K) S803m S591m S527m S907rn S701m

Current Ratio 2.37 2-54 2.61 2.29 2.42

Fixed Charge 18.3 4.8 4.1 9.8 23.6 Coverage

Nethcorne 7.2 -1.2 -2.5 4.1 9.1 Margin (%)

Market-to- 2.58 1-50 1.46 1.87 3 .O2 Book Ratio

Sales Growth 15.1 -0.6 -2.6 8.3 19.2 (%)

Cash Flow/K 0.52 0.24 0.22 0.37 0.62

Discriminant 0.51 -1 -45 -1.71 -0.25 0.97 Score (Z)

Table 21 suggests the importance of classiQing fm fianciai stanis every year is

even more important for the U.S. sample. Turnover for the NFC, PFC and FC groups

averages 4O.O%, 55.4% and 42.7% per year- These turnover rates are higher than for the

previous two samples, as are the percentages of f m s that were in the NFC, PFC and FC

categories at least one year, which are 77%, 86% and 76% respectively. In fact only six

f i s would have been classified as PFC for a i l seven years, while only 23 and 65 would

have k e n classified as FC and NFC for the entire perïod.

Table 22 indicates the composition of the various groups in terms of several

characteristics. It confirms the efficiency of the classification scheme in categorizing

f i s with respect to dividend changes, as the NFC group consists of 54.7% of f m s that

increase dividends and only 3.2% of fms that decrease dividends. The FC group, on the

other hand, consists of only 16.0% of f m s that increase dividends, and 13.3% of f m s

that cut dividends, while the PFC group falls somewhere in the middle of the other two

groups.

A prion, one would not expect to see a large variation in composition with respect

to industry classification. The results indicate this to be the case, as the groups are

relatively homogeneous with respect to their percentage composition of f m s from

different industry groups, unlike the Canadian sample, which allocated a larger

percentage of resource companies to the NFC category. On the other hand, one would

expect that groups classified as being less fuiancially constrained, would consist of a

higher proportion of f i s listed on the NYSE, which has the rnost stringent listing

requirements. The results confim this to be the case, as group composition does Vary

substantially across exchange groups. The FC group has a lower percentage of

TABLE 21

Croup Turnover Statistics (U.S. Sample)

PFC -

55.4% Overaii amml average

11988-94)

Number of Firms in

group at least once -

# firms in group for a11

7 vears

# firrns in group for 6 of

7 vars - # firms in

group for 5 of 7 vars

# firm in group for 4 of

7 years

# firms in prow for 3 of

7 vears

# fums in group for 2 of

7 vars

# fiims in group for 1 of

7 vars -

TABLE 22

Percentage Group Compositions (US. Sarnple)

TohI - Sample

NFC - PFC -

Dividend Grouy!

1 (Inaease) 2 (Decrease)

3 (No Change)

Exchange Group

1 (AMEX) 2 (NASDAQ)

3 (NYSE)

SIC - Group

1 (Resources) 2 (Manufactur)

3 (Retaii) 4 (Service)

Predicted Groue

1 (R-edict Dividend hcrease)

2 (Predict Dividend Decrease)

FHP Group Z (Pay d o )

2 (20<Payc20) 3 (Paq720)

NYSE fims (49.2%) and a higher percentage of AMWE firms (19.4%) than do the NFC

and PFC groups, which consist of 59.4% and 59.0% NYSE f i s , and 10.4% and 12.5%

AMEX f m s . Table 22 also iudicates that the FC group contains a much larger

percentage of Iow dividend payout firms than do the W C and PFC groups. In particular,

the FC group consists of 7 1.3% PayO firms and 5 1.5% FHP Group 1 fms, versus 3 1.5%

and 33.9% for the NFC group, and 33.3% and 29.3% for the PFC group.

6.3- REGRESSION RESULTS

6.3.1. Total Sample and Dividend Payout Croups

Fixed effects regression estimates for the entire sample are presented in Table 23.

The estirnated coefficients of 0.02 1 for the market-to-book variable, and 0- 120 for the

CF/K variable indicate that fm investment decisions are sensitive to investment

opportunities, but are even more sensitive to liquidity. This is consistent with previous

evidence. The market-to-book and CWK coefficient estirnates are positive and

significant for all three FHP groups. AU of the cash flow coefficients are much larger

than the correspondhg market-to-book coefficients, as was the case for the entire sample.

The market-to-book coefficients are alI very sirnilar in magnitude and the

empirical p-values confirm there are no significant differences in sensitivity to this

variable across the groups. The coefficient estirnates for the Liquidity variable (CFIK) do

Vary substantially, with estimates of 0.141, 0.120 and 0.088 for FHP Group 3, FHP

Group 1, and FHP Group 2. The empirical p-values indicate the CFIK coefficient

TABLE 23

Regression Estimates for the Total Sample and for the FEP Dividend Croups (US. Sample)

Reported coefficients are the 'within' k e d finn and year estimates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable. The frnn's market- to- book ratio and cash flowlnet fmed assets are the independen t variables.. The groups are formed sirniiar to the original FHP88 classification, where: FHP Group 1 includes fms whose average payout ratios were between O and 10%; FHP Group 2 includes firms whose average payout ratios were between 10 and 20%; and FHP Group 3 contaùls b s with average payout ratios above 20%. Tbe empincal pvalues are determined using the simulation procedure dacribed in chapter 3. They are estimated based on tbe nul1 hypothesis that the coefficients are quai for the two gcoups under consideration. The alternative hypothesis is that the coefficient for the Eirst group is greater than that of the second group. For example, the p-value of 0-4254 in the market-to-book column for FEP3 versus FHQ, suggests the market-to-book coefficient for FHP Group 3 is geater than for FHP Group 2 at the 42.54 % significance Ievel. The 0.0178 p-value in the next column suggests that the coefficient estimate for Cash FlowNet Fmed Assets is greater for FHP Group 3 than for FHP Group 2 (at the 1.78 Qo levei of significance). P-values in bold indicate a signifïcant difference in coefficient esrimates at the 5% level.

Market-to-Book Cash Fiow/Net Adjusted Number of Fixed Assets R-squared Observations

Regression Estimates

Total Sample 0.022 (12.3) O. 120 (27-3)

FHP Pay Group I 0.024 (7.81) O. 120 (17.62)

FHP Pay Group 2 0.017 (4.06) 0.088 (8.16)

FHP Pay Group 3 0.019 (8.65) 0.141 (19.04)

Empiricd P-values

FHP3 versus FHP3 0.4254 0.0178

FHP3 versus FHPl 0.8162 O. 1256

FHP=! versus FHPl 0.8534 0.9432

estimate for FHP Group 3 is larger thm for FHP Group 2 at the 1.78% signifïcance level,

while the estirnate for FHP Group3 is greater than for FHP Group 1 at the 12.56%

~ i g ~ c a n c e level, and the estimate for FHP Group 1 is greater than for FHP Group 2

estimate at the 5.68% significance level.

The results in Table 23 contradict the FHP88 argument that the low dividend

payout f m s (FHP Group 1) would be the most sensitive to liquidity while the high

payout f m s (FHP Group3) would be the least sensitive. Regression estirnates for the

tirne-varying dividend groups are presented in Table 24. They indicate no significant

differences in CFIK coefficient estirnates across the three payout catepries. These

results suggest there is no signifcant pattern in liquidity sensitivity across f m s

categorized based on dividend behavior.

Overall, we do not observe the investment-fmancing pattem observed in the

original FHP88 study, despite the fact that groups are formed using a similar approach.

Unlike some previous studies that had very smail numbers of frms in some of the groups

being compared, the regression estirnates are based on large numbers of observations

from each group, which suggests there is no reason to dispute the results. In retrospect, it

is curious that the low payout firms are not more sensitive to liquidity in my U.S. sarnple,

since it is more likely to include f m s that are more susceptible to informational

asymmetry problems. For example, this sample contains a greater number of 'srnall'

f i s , as well as f m s listed on smaller exchanges, such as NASDAQ and AMEX. In ail

likelihood, the contradicting results are attnbutable to a variety of sample differences

with respect to: sample periods; size of the f m s being examined; industry

diversification; exchange listing diversification; and fm dividend behavior. This

TABLE 24

Regression Estimates for Tie-Varying Divfdend Payout Groups (US. Sample)

Reported coefficients are the 'within' fixed firm and year estimates over the 1988-94 sample @od. T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable. The f m ' s market-tebook ratio and cash EIowfnet fixed assets are the independent variables. PayO represents the group fonned using E h year observations where the fm's dividend payout was zero; Pay c30 represents payouts be tween O and 3040; and Pay >30 represents payouts between 30 and 100%. The empirical pvalues are detennined using the simulation procedure descrikd în chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. 'The alternative hypothesis is that the coefficient for the frrst group is geater than that of the second group. For example, the p-value of O.9990 in the market-to-book co1um.n for Pap30 versus Pa*, suggests the market-to-book coefficient for the Pap30 group is grmer than that for the PayO group at the 99.90 % ~i~nificance level. The 03532 pvalue in the next column suggests that the coefficient e sha t e for Cash FiowNet Fixed Assets is greater for the Pay-30 group than for the PayO group (at the 25.32% level of significance). P-values in boId indicate a signifiant ciifference in coefficient estimates at the 5% level.

- -

Market-to-Book Cash FlowMet Adjusted Number of Fixed Assets R-squared Observations

Regression Estirnates

Empirical P-values

Pay>30 versus Pa* 0.9990 0.2533

Payc30 versus Payû 0.8982 0.0924

questions the overall generality of the FHP88 conclusions when applied to different

samples and tirne periods.

6.3.2. Exchange and Industry Groups

This section examines the sample to determine if there are significant differences

in investment behavior in relation to industry groups or where the shares are listed. One

would expect that fïrms with shares listed on the world's Iargest stock exchange, the

NYSE, wouid be Iess subject to informationai asymmetry problems than t-ms whose

shares trade in smaller markets such as NASDAQ and AMEX. The informational

asyrnmetry arguments discussed in chapter 2 irnply that NYSE-listed f w s should be less

sensitive to the availability of internal funds. Regression estimates presented in Table 25

indicate this is not the case, however. The CFIK coefficient estirnates are remarkably

close for firms that trade on the NYSE (0.127), NASDAQ (O. 1 IO), and AMEX (O. 120).

None of these differences are significant according to the empirical p-values. These

results imply there are no signifcant differences in investment-liquidity sensitivity across

eroups fonned on the basis of where the fm' shares vade. C

There is no obvious reason to expect that f m s in different industries will react

differently to the availabiiity of internal funds. However, in the Canadian sample,

agricultural, mining, resource and forestry companies, with SIC codes between 1 and

1,999 resource f m s (SIC 1 group), were found to be insensitive to liquidity, unlike F i s

in other industry groups. Regression estimates presented in Table 26, do not support the

existence of this pattern in the U.S. smple, and the CF/K estimates are positive,

TABLE 25

Regression Estimates for Exchange Groups (US. Sample)

Reported coefficients are the 'within' fmed fm and year estimates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable- The fimi's market- to-book ratio and cash flowhet fixed assets are the independent variables. Exchange Group 1 includes fïms whose shares are listed on the American Stock Exchange (AMEX); Exchange Group 2 includes firms whose shares trade on NASDAQ; and Exchange Group 3 includes f m s whose shares trade on the New York Stock Exchange (NYSE). The empiricai pvaiues are detennined using the simulation procedure described in chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. The alternative hypothesis is that the coefficient for the first group is greater than that of the second group. For example. the pvalue of 0.0390 in the market-to-book colurnn for NASDAQ versus AMEX, suggests the market-to-book coefficient for the NASDAQ g r o q is greater than that for the AMEX group at the 3.90% significance level. The 0.6502 p value in the next column suggests that the coefficient estimate for Cash FlowMet Fixed Assets is greater for the NASDAQ goup than for the AMEX group (at the 65.02 % IeveI of significance). P-values in bold indicate a significant difference în coefficient estirnates at the 5% level,

Marke t-teBook Cash Flow/Net Adjusted Number of Fixed Assets R-squared Observations

Exchanrge Group 1 0.015 (2.98) 0.120 (10.94) 1 1.23% 1071 (-1

Exchange Group 2 0.032 (8.71) O. 1 10 (1 3.5) 13.38% 2268 (NAS DAQ)

Exchange Group 3 0,016 (8.64) 0.127 (21.37) 13.28% 4221 W S E )

Empirical P-values

NASDAQ versus 0.0390 0.6502 AMEX

NYSE versus APVIEX 0.4082 0.3838

NYSE versus 0.9944 O. 1844 NAS DACI

TABLE 26

Regression Estimates for Industry Groups (U.S. Sample)

Reported coefficients are the 'withïn' fixed finn and year estimates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditures divided by net hxed assers is the dependent variable, The f i ' s market-to-book ratio and cash flowlnet fixed assets are the independent variables. The SIC1 group includes 73 argiculturaL mining, resource and forestry companies with primary SIC codes between 1 and 1,999; SIC2 includes 723 industrial manufacturing companies with primary SIC codes between 2,000 and 3,999; SIC3 includes 172 retail and wholesaie companies with prhary SIC codes between 5,000 and 5,999; and SIC4 incfudes 112 service companies with primary SIC codes between 7,000 and 8,999. The empincal pvalues are determineci using the simulation procedure described in chapter 3. They are estimated based on tbe nul1 h ypothesis that the coefficients are equd for the two grorrps under consideration. The altemative hypothesis is that the coefficient for the fust group is greater than tbat of the second group. For example, the p-value of 0.0010 in the market-to-book column for SIC3 versus SEC2, suggests the market-to-book coefficient for the SIC3 group is greater than that for the SIC2 group at the 0.10 % significance leve1. The 0.0222 p-value in the next column suggests that the coefficient estimate for Cash FlowNet Fixed Assets is greater for the SIC3 group chan for the SIC2 group (at the 2.22 5% level of significance). P-values in bold indicate a significant difference in coefficient estimates at the 5% level.

Market-to-Book Cash FiowMet Adjusted Number of Fmed Assets R-sauared Observations

Regression Estimates

SIC 1-1999 firms (SIC11

SIC 200-3999 f m s (S

SIC 5000-5999 fms (SIC3)

SIC 7000-8999 f m s (S IC4)

SIC3 versus SIC1

signiticant and similar for a l l four industry categories. The CFK estimates are highest

for the SIC 3 group (retail and wholesale f l s ) at 0.163, second highest for the SIC 1

group at 0.144, third highest for the SIC 4 group (service companies) at 0.12 1, and are

lowest for the SIC 2 group manufacturing f m s . The only ~ i g ~ c a n t difference is

between SIC 3 and SIC 2 h s , which suggests that retail and who lesale f i s are more

sensitive to cash flow than are manufacturing companies.

6.3.3. Financial Constraint Groups

Regression results for groups formed according to discriminant scores are

presented in Table 27. They indicate that liquidity and market-to-book are significant

determinants of investment (at the 1% significance level) for all groups. The coeffkients

for market-to-book ratios are virtually identical for all three groups. The coefficients for

the liquidity variables are all positive and significant, which suggests f i m investment

decisions are sensitive to the availability of intemal funds, The CFIK coefficient

estimates for the NFC, PFC and FC fms are 0.174,O. 124 and 0.068. These indicate that

investment of NFC f m s is more sensitive to liquidity than that of PFC and FC fums,

while PFC f m s are more liquidity sensitive than FC fïms. Empirical p-values suggest

ail of these differences are significant at the 2% level or bette?. This venfies the KZ

result on a much larger, broader sample using an objective classification criterion.

" The result that W C and PFC f m s are most sensitive to Liquidity is robust to a nurnber of alternative sorting arrangements whose results have not ken reported here, including: (i) whether the sample was divided into two or three groups; (ii) groups fonned using absolute discriminant score cutoff points for the entire period to create the NFC, PFC and FC groups, rather than dividïng the sample into thirds each year; (iii) groups formed based on dividend groups according to whether dividends were increased, deueased or not changed; and (iv) groups formed on predicted dividend groups according to discriminant analysis.

Regression Estimates for the F inc ia l Constraint Croups (US Sample)

Reported coefficients are the 'within' h e d tïrm and year estimates over the 1988-94 sample period. T- statistics are in parentheses, Capital expenditures divided by net h e d asse& is the dependent variable, The m ' s market-to-book ratio and cash flowfnet flxed assets are the independent variables. The FC, PFC and NFC groups are fonned by sorting ail firms according to their discriminant scores. Every year. the firms with the lowest discriminant scores (the bottom third) are categorized as financially constrained (Fa; the next third are categorized as partialiy financially constrained (PFC); and the top third are categorized as not financiaIly constrained (NFC). The empmcai p-values are detennined using the simuiation procedure described in çhapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. The alternative hypothesis is that the coefncient for the first group is greater than bat of the second group. For example, the pvalue of 0.5754 in the market-t*book column for NFC versus PFC, suggests the market-CO-book coefficient for the NFC group is greater than that for the PFC group at the 57.54% significance level. The 0.0136 p-value in the next column suggests that the coefficient estimate for Cash Flow/Net Fixed Assets is pater for rhe NFC group than for the PFC group (at the 1.36% level of significance). P-values in bold indicate a signifiant difference in coefficient estimates at the 5% level.

Market-to-Book Cash FlowMet Adjusted Number of Fixed Assets R-squared Observations

Regression Estima tes

Total Sample

FC f m s (financially consuained)

PFC f m s (partial1 y financidi y constraùied)

NFC f m s (not financially cons trained)

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

The rernainder of this section examines the robustness of this general result across

different categories of fms, based on dividend behavior, exchange listing and industry

classification. In order to obtain more homogeneous sub-groups and reduce the potential

impact of dividend policy, the entire sample is divided into dividend payout groups,

according to the two approaches described above. Each dividend payout group is then

sub-divided according to discriminant scores every year, as above, in order to detemùne

the FC, PFC and NFC groups within a given dividend group.

Table 28 presents regression results for these sub-groups within the FHP dividend

groups. Panel A presents regression estimates for the f m s in FHP Group 1 (the low

payout f i s ) . The CF/K coefficients for the NFC, PFC and FC f m s are 0.204,0.098

and 0,055, which indicate the investment decisions of the NFC f m s are the most

sensitive to liquidity, foilowed by the PFC f i s and fmally by the FC f m s . The

differences are all significant at the 3.70% level or better. This group is the one analyzed

by KZ, which lends support to their conclusions.

The results change somewhat when we examine the fmancial constraint groups

within the other two FHP groups, dthough the FC groups remain the least liquidity

sensitive for these groups as well. For FHP Group 2, the CFIK coefficient estimates are

virtually identical for the NFC and PFC groups, while the estimate for the FC group is

significantly below those of the other two groups. In FHP Group 3, the NFC group has

the highest liquidity coefficient, followed by the PFC group and the FC group, however,

none of the differences is significant.

TABLE 28

Regression Estimates for Financial Constraint Sub-Groups Witbin FHP Groups (U.S. Sample)

Reported coefficients are the 'within' fmed firm and year estimates over the 1988-94 sample period- T- statistics are in parentheses. Capital expenditures divided by net fixed assets is the dependent variable. The tilnn's market-to-book ratio and cash flowhet fixeci assets are the independent variables. The groups are fomed similar to the original FHP88 classification, where: FKP Group 1 indudes finns whose average payout ratios were between O and 10%; FHP Group 2 includes fWis whose average payout ratios were between 10 and 20%; and FHP Group 3 contains ail remaining bris. The FC, PFC and NFC groups are formed by sorting h s within a aven FHP group according to their discriminant scores. Every year, the firms in the group with the lowest discriminant scores (the bottom third) are categorized as fmanciaiiy constrahed (Fa; the next thïrd are categorized as partially financiaiiy constrained (PFC); and the top ihird are categorized as not financially constrained (NFC). The number of observations for the PFC group may be larger than the other two because the 'left' over h n s are assigned to the PFC group when the totai number of f m s in a group is nota rnuitiple of three. The ernpincal p-values are detennined using the simulation procedure described in chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. The alternative hypotfiesis is that the coefficient for the first group is greater han that of the second group, For example, the pvalue of 0.2182 in the market-to-book column for PFC versus FC in FHP Group 1, suggests the market-to-book coefficient for the PFC group is greater than bat for the FC group at Ihe 21.82 % significance level. The 0.0370 p- value in the next column suggests that the coefficient estimate for Cash FlowNet F ied Assets is greater for the PFC group than for the FC group in FHP Group 1 (at the 3.70 % level of significance). P-values in bold indicate a significant difference in coefficient estimates at the 5% level.

Market-to-Book Cash FîowMet Adjusted Nurnber of Fixed Assets R-squared Obsemtions

PANEL A - FHPGroup 1

Regression Estimates

FC f m s 0.018 (4.16) 0.055 (6.17) 6-23 % 959

PFC fims 0.024 (3.85) 0.098 (7.28) 8.21 5% 973

NFC f i s 0.019 (3.67) 0.204 (15.64) 24.17% 959

Empirical P-vaIues

PFC versus FC 0.2182 0.0370

W C versus FC 0.5034 0.0000

NFC versus PFC 0.7250 0.0000

PANEL B - J?ElP Group 2

Regression Estima tes

FC 6rms

PFC füms

NFC ikns

Empirical f-values

PFC versus FC

NFC versus FC

NFC versus PFC

Regression Es timates

FC f i

PFC firms

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

PANEL C - FHP Croup 3

Regression Estimates for F i n c h l Constraint Sub-Groups Within the Time-VHng Dih5dend Groups (U.S. Sample)

Reported coefficients are the 'within' fixeci hnn and year estimates over the 1988-94 sample period, T- statistics are in parentheses. Capital expenditures divided by net fmed assets is the dependent variable. The firm's market-to-book ratio and cash flowlnet 6xed assets are the independent variables. PayO represents the group formed using fim year observations where the W s dividend payout was zero; Pay <30 represents payouts between O and 30%; and Pay >30 represents payouts between 30 and 100%- The FC, PFC and NFC groups are formed by sorting fïnns within a given payout group according to their discriminant scores. Every year, the hnns in the group with the lowest discriminant scores (the bottom third) are categorized as fmancially constrained (Fa; the next third are categorîzed as partially financially constrained (PFC); and the top third are categorized as not finanâally constrained (NFC). The empiricd p- values are determined using the simulation procedure described in chapter 3. They are estimated based on the nuil hypothesis that the coefficients are equal for the two groups under consideration, The alternative hypothesis is that the coefficient for the fïrst group is greater than chat of the seconbgroup. For example, the p-vdue of 0.1224 in the market-to-book column for NFC versus PFC in the PayO group, suggests that tbe market-to-book coefficient for the NFC group is greater than that for the PFC group at the 12.24 % significance level. The 0.0024 p-value in the next column suggests that the coefficient estimate for Cash FiowMet Fixed Assets is greater for the NFC group than for the PFC group in the PayO group (at the 0.24 92 level of significance). P-values in bold indiate a significant difference in coefficient estirnates at the 5% level,

Market-to-Book Cash Flow/Net Adjusted Number of Fixed Assets R-sauared Observations

PANEL A - PayO Group

Regression Estimates

FC f m s 0.015 (4.0) 0.054 (6.4)

PFC f m s 0.020 (3.7) 0.091 (8.5)

Empiricd P-values

PFC versus FC 0.2862 0.0352

NFC versus FC 0.0390 0.0000

NFC versus PFC O. 1224 0.0024

PANEL B - Pay<30 Group

Regression Estimates

FC ûrms

PFC firms

NFC f i s

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

Regression Estimates

FC fms

PFC fms

W C fms

Ernpiriwl P-values

PFC versus FC

NFC versus FC

M.'C versus PFC

PANEL C - Pap30 Group

The results above suggest the KZ result, that NFC f m are the most sensitive to

liquidity, is strongest among firms with low dividend payouts. Table 29 confums this

observation using the time-vuying dividend payout categories described above.

Regression results for the fimancial consuaint groups within the zero payout group offers

very strong support for the KZ result. In particular, the CF/K coefficients are

significantly higher for the NFC h s than for the other two groups, while the estimate

for the PFC group is signifcantly higher than the one for the FC group. The coefficient

estirnates follow the same pattem for the other two dividend groups (Payc30 and

Pay>30), however, none of the differences is significant.

Tables 30 and 3 1 c o n f i higher Liquidity sensitivity for the NFC and PFC f m s in

sub-sarnples formed within excbange and industry groups. Panel A of Table 30 shows

that, within the NYSE-iisted group of f m s , the CFIK coefficient estirnate is largest for

the NFC group (0.1 86), followed by the PFC group (O. 1 16) and then the FC group

(0.084). The differences between the NFC estimates and the other two groups are both

significant at the 2% level, while the difference between the PFC and FC estirnate is

significant at the 5.52% level. This pattern in coeffïcient estimates across the groups is

the same for f m s whose shares trade on NASDAQ, and ail of the differences are

significant at the 4% level. Results for AMEX-listed fums are based on a smaller

number of observations, and may not be as reliable. Within this category of f m s the

pattem changes somewhat, with the PFC F i s exhibithg the highest liquidity

coefficient, followed by the NFC f i s and then the FC Rms. The difference between

the PFC F i s and the FC fvms is significant, however, the difference between the NFC

and PFC f m s estimate is no t significant.

Regresion Estimates for Financial Constraint Sub-Croups Within &change Croups (US. Sample)

Reported coefficients are the 'within' fïxed hnn and year estimates over the 1988-94 sample period. T- statistics are in parentheses. Capital expenditllres divided by net fixed assets is the dependent variable. The fm's market-to-book ratio and cash flowlnet fixed assets are the independent variables. The AMEX Group includes fms whose shares are iisted on the American Stock Exchange (AMEX); the NASDAQ Group includes f m whose shares trade on NASDAQ; and the NYSE Group includes hrms whose shares trade on the New York Stock Exchange (NYSE). The FC, PFC and NFC goups are formed by sorting f m s within a given exchange group according to their discriminant scores. Every year, the fms in the group with the lowest discriminant scores (the bottom third) are categorized as financially constrained (FC); the next third are categorized as partially financially constrained (PFC); and the top third are categorized as not fmancially constrauied (NFO. The empirical pvdues are determined using the simulation procedure described in chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equal for the two groups under consideration. The alternative hypothesis is that the coefficient for the first group is greater than that of the second group. For example, the pvahe of in the market-to-book column for NFC versus PFC in the NYSE group, suggests the market-to-book coefficient for the W C group is greater than that for the PFC group at the 36.42 % significance Ievel. The 0.0188 p-value in the next column suggests that the coefficient estirnate for Cash FlowMec Fied Assets is greater for the NFC group than for the PFC group in the NYSE group (at the 1.88 % Ievel of significance). P-values in boid indicate a signifiant difference in coefficient estimates at the 5% level,

Market-to-Book Cash FlowINet Adjusted Number of Fmed Assets R-squared Observations

PANEL A - NYSE Group

Regression Estimates

PFC fims 0.01 1 (3.3) 0.1 16 (10.9)

Empirical P-values

PFC versus FC 0.8980 0.0552

NFC versus FC 0.8166 0.0000

NFC versus PFC 0.3642 0.0188

PANEL B - AMEX Group

Regression Estimates FC fums

PFC fms

NFC Eïnns

Ernpirical P-values

PFC versus FC

NFC versus FC

W C versus PFC

Regression Estirnates

FC f i s

PFC f m s

NFC fms

Empirical P-values

PFC versus FC

NFC versus FC

NFC versus PFC

PANEL C - NASDAQ Group

TABLE 31

Regression Estimates for Financial Constraint Sub-Groups Within Industry Groups (U.S. Sample)

Reported coefficients are the 'within' fixed fkm and year estimates over the 1988-94 sample period, T- statistics are in parentheses, Capital expenditures divided by net fmed assets is the dependent variable- The h ' s market-CO- book ratio and cash flowfnet Gxed assets are the independen t variables. The SICI- 1999 group includes 73 agricultural, mining, resource and forestry companies with prirnary SIC codes between 1 and 1,999; SIC 2000-3999 includes 723 indusmal manufacturing companies with primary SIC codes between 2,000 and 3,999; SIC 5000-5999 includes 172 retail and wholesale companies with prhary SIC codes between 5,000 and 5,999; and SIC 7000-8999 includes 112 service companies with primary SIC codes between 7,000 and 8,999. The FC, PFC and NFC groups are formed by sorting fïms within a given industry group according to their discriminant scores. Every year, the fnms in the group with the lowest discriminant scores (the bottom third) are categonzed as financially constrained (Fa; the next third are categorized as partially financirilly constrained (PFC); and the top third are categorized as not financiaily consûained (NFC). The number of observations for the PFC group may be Iarger than the other two because the 'left' over f m s are assigned to the PFC group when the totai number of firms in an industry group during a given year is not a multiple of three. The empiricai pvaiues are determined using the simulation procedure describeci in chapter 3. They are estimated based on the nul1 hypothesis that the coefficients are equd for the two groups under consideration. The alternative hypothesis is that the coefficient for the first group is g r a t a than that of the second group. For example, the pvalue of 0.8644 in the market-tebook column for NFC versus PFC in the SIC 1-1999 group, suggests the market-tebook coefficient for the NFC group is greater than that for the PFC group at the 86.44 % significance level. The 0-4852 p-vaIue in the next coiumn suggests that the coefficient estimate for Cash FlowMet Fixed Assets is greater for the W C group than for the PFC group in the SIC 1-1999 group (at the 48.52 % level of significance). P-vatues in bold indicate a significant difference in coefficient estimates at the 5% level,

Market-CO-Book Cas& FlowfNet Adjusted Number of Fied Assets R-squared Observations

PANEL A - SIC 1-1999 Group

Regression Estimates

PFC f m s

W C f i s

Empirical P-values

PFC versus FC 0.1818 0.7878

W C versus FC 0.4398 0.7806

NFC versus PFC 0.8644 0.4852

PANEL B - SIC 2000 - 3999 Group

Regression Estimates

FC fms

PFC firms

NFC fums

Empirical P-values

PFC versus FC

M.% versus FC

W C versus PFC

Regression Estima tes

FC f i s

PFC f m s

NFC fms

Empiriwi P-values

PFC venus FC

NFC versus FC

NFC versus PFC

0.6072 0.0004

0.4094 0.0000

0.2852 0.1018

PANEL C - SIC 5000 - 5999 Group

0.9946 0.0292

PANEL D - SIC 7000 - 8999 Group

Regression Estima tes FC fms 0.012 (2.0) 0.065 (4.8) 9.70%

PFC firms 0.012 (1.3) 0.133 (6.3) 15 -77%

NFC firms 0.020 (1.8) 0.220 (6.8) 17.88%

PFC versus FC 0.5046 0,0386

NFC versus FC 0,3986 0,0002

W C vcrsus PFC 0.3956 0.0816

Table 3 1 presents regression estimates for financial constmint groups within the

various industry categories. Panel B presents coenicient estimates for manufacturing

f i s (the SIC 2000-3999 group) which follow the pattern described above, and are based

on a relatively large number of observations. The CFlK coefficient estimates are highest

for the NFC f m s , foliowed by the PFC firms, and then by the FC f i s . The difference

between the NFC and FC estimates, and the PFC and FC estirnates, are significant at the

1% level, while the difference between the NFC and PFC estimates is sigmcant at the

10- 1 8% level.

Unfortunately, the number of available observations is rather srnall for the other

groups, and the results are not as conclusive. For example, the liquidity coefficient of

0.208 for the FC fms in the SIC 1- 1999 group (agricultural, mining, resource and

forestry f i i s ) , is more than double the coefficients for the PFC and NFC groups,

however, the differences are not statistically significant. The other two industry groups

are relatively supportive of the investment-liquidity pattern demonstrated by the entire

sample. The PFC and NFC f m s in the SIC 7000-8999 group (senrice fms) are

significantly more sensitive to Liquidity than the FC fiims, while the difference between

the NFC liquidity coefficient of 0.220 and the PFC coefficient of 0.133 is significant at

the 8.16% level. Findy, NFC frms are significantly more sensitive to liquidity than

PFC and FC f r m s for retail and wholesale firms (the SIC 5000-5999 group), while there

is virtually no difference between the CF/K coefficient estirnates for the FC and PFC

frms.

6.4. INTERPRETATION OF RESULTS

The evidence implies all f m s act as if they are consuained at certain points of

time, depending upon the amount of fmancial resources held inside the fm. Based on

this notion, consuained (FC) f m s may exhibit low investment liquidity sensitivit y due to

the necessity of irnproving liquidity rather than investing. This notion is raised by

Modigliani and Miller (1963), who argue that Fimis will maintain 'reserve borrowiog

capacity7. Myers and Majluf (1984) demonstrate that Fiancial slack is valuable to f m s

when informational asyrnmetry problems exist between owners and managers. The low

investrnent liquidity sensitivity for constrained finns also supports the existence of the

underinvestment problem identified by Myers (1977). Myers argues that more highly

levered f m s will be reluctant to invest in otherwise desirable projects, because the

benefits of such investments will accrue primarily to the fim's debtholders. Bemanke

and Gertler (1990) attribute underinvestment to the existence of an inverse relationship

between borrower net wonh and agency costs. They demonstrate that "both the quantity

of investment spending and its expected retum will be sensitive to the 'creditworthiness'

of borrowers (as reflected in their net wonh positions)."

The high investment liquidity sensitivity of the unconstrained f m s appears

puzzling at fxst glance. However, it is consistent with Mayer (1990)'s empincal

evidence that interna1 fmancing is the dominant source of fmancing for all fims, which

implies the investment decisions of the majonty of f m s will be sensitive to current

liquidity. Since unconstrained f m s will be less concerned with increasing fmancial

slack, internai cash flow will be channeled toward to new investments, This evidence

concurs with the results of Lamont (1997). He documents a large decrease in the capital

expenditures of non-oil subsidiaries of oil conglomerates, in reaction to the 1986 drop in

oil prices. Larnont concludes that large reductions in cash flow and collateral value lead

to decreased ïnvestrnent, independent of changes in available investment opportunities.

The ove rd investment-financing pattem is consistent with the agency argument

of Bernanke and Gertler (1990), who predict investment outlays will be positively related

to net worth. The evidence supports the free cash 80w argument presented by Jensen

(1986) that firms will increase investment in response to the availability of cash fiows.

Jensen argues that "managers have incentives to cause f m s to grow beyond optimal

size" since "growth increases managers' power by increasing the resources under their

control." It is also consistent with the 'option' approach to capital budgeting, which

implies that f m s will defer capital spending until internal resources become available.

Alternatively, KZ suggest that "managerial risk aversion" may contribute to the

correlation between investment and liquidity. Given the size and changing group

composition of the approach used in this study, the observed sensitivities are not likely to

be driven by overly risk-averse managers in a particular group, and this may in fact, be a

general behavioral c haracteristic of most frm managers.

Examining the cash flows of the various proups provides insight into the

relationship between fum investment and financial slack. Table 32 presents the mean

and median values for I/K and CWK, which decrease monotonically as we move from

NFC to FC f ~ m s . The pattem across NFC, PFC and FC f i s also exists for the mean

and median values oE (change in net working capita1)lK; (dividends)/K; (extemal

finance)lK; (change in total debt)K; and (change in equity)lK The small values for

TABLE 32

Fincial Constraint Group Cash FIow Cornparisons

The hrst line is the mean and the second Line is the median. AU cash flows are scaled by the hm's beginning of period net fixed asset figure (K). Cash Flow represents the h ' s net income plus depreciation plus inaease in deferred taxes during period 't', Investment represents capital expenditures, &"@WC rqiesents the change in net working capital, ExtFin represents the change in debt and equity, chgDebt represents the change in total debt, chgLTDebt represents the change in long term debt, chgSTDebt represents the change in short term debt, chgEqty represents the change in preferred stock and common stock and Div represents the total amount of common dividends paid Details of the caiculation of these variables are provided in Appendix 1. The FC, PFC and NFC groups are fomed by sorüng ali fbns according to their discriminant scores. Every year, the fïrxns with the lowest discriminant scores (the bottom third) are categorized as fmancially constnined (Fa; rhe next third are categorized as partialiy financially constrained (PFC); and the top third are categorized as not hnanciaily constrained WC).

Total Sample W C fms PFC f i s FC fms (financiail y (partially financially (not financially constrained) constrained) cons train ed)

Cash Flow/K 0.40 0.34

external fiiancing sources relative to I/K and CFK is consistent with the fact that fkns

are reluctant to raise extemal finance in general. The (external finance)/K and (change in

debt)/K values are rnuch larger for the NFC and PFC groups than for the FC group. In

fact, the FC group displays negative values for changes in debt, which suggests they are

reducing their debt levels. This observation supports the argument made earlier that

constrained fms would channel funds toward improving their fmancial position at the

expense of foregoing additional capital expenditures.

The U.S. sample contains a large number of fms and is diversified across

industries and by exchange listing. Discriminant analysis works extremely well for this

sample and successfÜlly predicts which f m s will reduce or increase dividends 77% of

the t h e . Group turnover statistics verify the importance of allowing group composition

to vary through tirne, in response to changing fm financial status.

Regression resdts indicate that fm investment decisions are sensitive to growth

oppomnities, but are even more sensitive to fm liquidity, which supports previous

evidence. Unlike the FHP88 results, there does not seem to be any pervasive pattern in

investment-liquidity sensitivity across groups formed according to f i dividend

behavior. In addition, there is no evidence of differences in Iiquidity sensitivity across

f m s fiom different industry categories or whose shares Vade in different markets.

The key result of this study is the observation that investment decisions of f m s

with high creditworthiness, according to traditional fmancial ratios, are significantly more

sensitive to internal fund availability than f m s which are less worthy of credit. This

provides strong support for the KZ conclusions, and is based on an objective

classification SC heme and a large, diversified sample. These results support the relevance

of fuiancial slack at the level of the fm. which is consistent with the arguments of Myers

and Majluf (1984)' and Bemanke and Gertler (1990). The high liquidity sensitivity

displayed by the unconstrained fms supports Jensen's fiee cash flow argument. It is

also consistent with the 'option' approach to capital budgeting, which implies that firms

will defer capital spending until internal resources become available. Cash flow evidence

suggests that constrained fms are reducing debt levels. which likely accounts for theu

10 w investrnent-iiquidity sensitivity.

CONCLUSIONS

Following the basic approach of Kaplan and Zingdes (1997), fms are classifed

according to financial statement variables that are related to their ability to raise external

fmancing. An objective multivariate classifcation index, similar to Altman's Z factor, is

used to determine f m fuiancial status and this status is ailowed to Vary from one period

to the next- This approach successfÙlly classifies h s that increase or decrease

dividends 57% of the t h e in the FHP replication sarnple, 64% of the tirne in the

Canadian sample, and 77% of the t h e in the U.S. sample. More irnponantly, f m s are

categorized into fmancial constraint groups that are clearly distinct according to

traditiond financial ratios.

The discriminant score ciassification scheme allows F i financial status to be

reclassified every year, in response to changing fmancial conditions. This represents an

improvement over previous studies that do not allow the composition of their fiancial

constraint groups to change throughout the sample penod. This approach disregards the

fact that the same fm can be constrained in one period and unconstrained in others.

Evidence supports the changing nature of f r m fiancial status. For example, the

fuiancial consuaint groups in the U.S. sample display average annual group turnover

rates between 40 and SS%, while as few as 6 out of 1080 finns would have k e n

classified in one group over a seven year penod.

The focus of this and previous studies is the comparison of investrnent-liquidity

sensitivities across differeot groups of f m s . 1 use a bootstrap rnethodology to determine

significance levels of observed dserences in coefficient estimates. This represents an

improvement over previous studies whose conclusions have been based prirnarily on the

observed differences in magnitude and level of significance of the liquidity variable

coefficient estimates.

The availability of significance levels regarding differences in coefficient

estimates leads to an important conclusion. In particular, some rather large differences in

coefficient estirnates are found to be insignificant, contrary to expectations. This problern

arises when groups with small numbers of observations are used for comparison

purposes. The implication is that whenever small groups of f m s are compared with

other groups, we must view the results with caution. This is very relevant to the

empincal investment literature, since the conclusions of several previous studies are

based on the comparison of srna11 groups of f m s . For example, Fauari, Hubbard and

Petersen (1988) had only 49 firrns in one group and only 39 in another, while Hoshi,

Kashyap and Scharfstein (199 1) have only 24 f i s in their group of fums they

categorize as constrained. The Kaplan and Zingales (1997) study is based on even

smaller groups of 19,8 and 22 fums, which implies the importance of verifying the

pnerality of their results, especidly since they contradict previous evidence.

Overall, investment decisions of all fums are found to be very sensitive to fm

liquidity, which supports the existence of a fmancing hierarchy. The observed

differences in investment-liquidity sensitivity across the groups formed within the FHP

replication sample and the Canadian sample are relatively inconclusive. The

inconclusive nature of the results may be directly attributable to the small numbers of

observations available for each of these sarnples, as many large differences in liquidity

coefficients are found to be insignificant. This c o n f i s the importance of obtaining a

large sample in order to reach significant conclusions regarding differences in f i

investment behavio r.

The results for the U.S. sample of 1080 f i s are based on a large number of

observations, and do offer sorne very strong conclusions. First, there does not appear to

be any signifcant pattem in investment-liquidity sensitivity across groups of fwms

formed according to dividend behavior. This does not support the results of Fazzari,

Hubbard and Petersen (1988) and may be attributable to a variety of sarnple differences

with respect to: sample periods; size of the fms being examhed; industry

diversification; exchange Listing diversification; and Firm dividend behavior.

The key result of this study is the confirmation of the Kaplan and Zingales (1997)

conclusions. In particular, fums that are more creditworthy, exhibit greater investment-

liquidity sensitivity than those which are classified as less creditworthy. These

conclusions are based on the use of a large, diversified sample and an objective

classification scheme, which alleviates the two main cnticisrns of the Kaplan and

Zingales study.

The results suggest managers balance the rewards of undertaking present

inves tment O pportunities against the rïsk of becoming overextended in subsequent

penods. The observed pattem in investment-liquidity sensitivities across the groups

supports the agency argument of Bernanice and Gertler (1990) that investment spending

will be sensitive to the 'creditworthinessT of borrowers, as well as Jensen (1986)'s free

cash flow argument, where F m s which have more fkee cash tend to invest more. It is

also consistent with the 'option' approach to capital budgeting, which irnplies that f m

will defer capital spending until intemal resources become available. The constrained

f i ' behavior supports the classic argument made by Myers and Majluf (1984) that

'slack' has value, therefore, f m s will be concemed with maintainhg an adequate

amount of fuiancial slack. This argument is supponed by cash flow evidence, which

suggests that constrained f i s tend to be reducing their debt levels, while the less

cons trained fms are increasing de bt.

Financiai Variable Calculations

The financial variables utilized are calculated as follows:

current assets (1) current ratio =

current liabilities

cumnt portion of long term debt +long term debt (2) debt ratio =

total assets ?

(3) Futed charge coverage ratio = EBIT

interest + preferred dividend payments x 1 - tax rate

(4) net income = net income before extraordinary items + I - extraordinary items and discontinued operations;

net income (5) net income rn arg in =

net sales '

(6) cashflow = net income + depreciation and I or amonization exp ense + change in deferred taxes;

(7) investrnent = net capital expenditures;

net sales- - net sales. , (8) net sales growth = Z t - 1 .

net sales T

t-1

total dividends paid (9) dividend payout =

net income Y

(dividend per share) -(dividend per share) (10) dividend growth = t t -1 ,

(dividend per share) 9

t-1

( 1 1) STACK =cash +short term investments +(OSO~inventory) + (0.7Oxaccounts receivable) -short tenn loans;

(12) net fuced assets (K) = net property, plant and equipment; market value of common equity * (13) market - to - book = book value of common equity '

(14) change in net working capital= (net working capital) -(net working capid) t t-1 '

(15) change in debt = (total debt) -(total debt) - l;

(1 6) change in equity = (preferred stock + common stock) - (prefemd stock + comrnon stock) - ; t

( 17) extemal fmance = change in debt +change in equity; and,

interest expense

Sample Selection Cnteria and Default Settings

A. Sample Selection Criteria:

1. Complete history of Fuiancial information available (1987-94). 2. Sales, total assets and net f i e d assets are ail greater than O. 3. The absolute value of (investrnent/total assets) is less than 0.50. 4. The absolute value of growth in total assets is less than 100%. 5. The absolute value of sales growth is less than 100%. 6. The market-to-book ratio is greater than O. 7. The absolute value of (investmentfK) is less than 2. 8. The absolute value of (cash flow/K) is less than 5- 9. The absolute value of change in net working capital is less than 10. 10. The absolute value of (SLACWK) is less than 10,

B. Default Settings:

1. If market-to-book is greater than 10, then a value of 10 is assigned. 2. If current ratio is greater than 10, then a value of 10 is assigned. 3. If net income margin is greater than lOO%, then a value of 100% is assigned. 4. If net income margin is less than - 100%. then a value of - 100% 'o assigned. 5. If fuced charge coverage ratio is greater than 100, then a value of 100 is

assigned. 6. If fixed charge coverage ratio is less than O, then a value of -0.1 is assigned.

APPENDIX III

Discriminant Analysis

The discriminant analysis coefficients are estimated according to the procedure

outlined in Fisher ( 193 8). Fischer transformed multivariate observations (X) into

univariate observations (Y), such that the Y's denved from two separate populations

were separated as much as possible. He suggested taking h e a r combinations of X to

create Y's because the y are simple enough functions of the X to be handled easily. His

approach does not assume the populations are normal, however, it does irnplicitly assume

the population covariance matrices are equal because a pooled estimate of the common

covariance matrix is used.

Fischer selects the linear combinat ions of X that achieve maximum sep aration of

the sample means YI - , expressed in standard deviation units, where:

is the pooled estirnate of variance.

LI -1 The linear combination y = l ' x = (El - 5 )'Spooled x maximizes the ratio

( ~ ~ - 7 ~ ) ~ - (êtZ+*) 2 - - - 2 ê over all possible coefficient vectors

"Y spooled ê'spooled

A

C whered = (FI - % ) . The maximum of the ratio above is given by:

(Y - F2) . Note that s may be calculated using D~ = ( ~ ~ 2 ) t s ~ ~ o l e d 1

* A

y l j = P x l j and y2j = P X 2 j 0 -

The allocation mle based on Fisher's Discriminant Function is to diocate xo to

population 1 if:

or yo-&>O.

Allocate .ro to population 2 if: y c &, or y - 6i c O. O O

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