PECKING ORDER THEORY AND THE FINANCIAL STRUCTURE OF

PECKING ORDER THEORY AND THE FINANCIAL STRUCTURE OF

MANUFACTURING SMEs FROM AUSTRALIA’S BUSINESS LONGITUDINAL SURVEY

Mr Adrian Zoppa,

Financial Planning Assistant,

LambertsBRS Financial Planning Pty Ltd,

28 Lower Portrush Road,

Marden South Australia 5070.

Telephone: +61 8 83632399

Facsimile: +61 8 81300800

Email: [email protected]

and

Professor Richard G.P. McMahon,*

Head, School of Commerce,

The Flinders University of South Australia,

GPO Box 2100,

Adelaide South Australia 5001.

Telephone: +61 8 82012840

Facsimile: +61 8 82012644

Email: [email protected]

SCHOOL OF COMMERCE RESEARCH PAPER SERIES: 02-1 ISSN: 1441-3906

Acknowledgments

The permission of the Australian Statistician to use confidentialised data from the federal government’s

Business Longitudinal Survey, and to publish findings based on analysis of that data, is gratefully

acknowledged. Responsibility for interpretation of the findings lies solely with the authors.

* Author for correspondence.

mailto:[email protected]

2

FINANCIAL STRUCTURE OF AUSTRALIAN MANUFACTURING SMEs

Abstract

The principal objective in this paper is to ascertain the extent to which Myers’ (1984) Pecking Order Theory (POT) of business financing appears to explain financial structure amongst a panel of 871 manufacturing SMEs legally organised as proprietary companies, taken from the Australian federal government’s Business Longitudinal Survey for three financial years from 1995-96 to 1997-98. The research findings reported in the paper provide further substantial empirical evidence broadly suggesting pecking order financing behaviour amongst SMEs. However, the findings also suggest the need for a modified POT that more fully reflects the special circumstances and nuances of SME financing. A full specification for a modified POT of financing for SMEs is proposed as a basis for further inquiry in the area.

Introduction

Concerning the extent to which extant theories of financing appear to explain the financial structure of

business concerns, Pettit and Singer (1985, p. 54) argue:

Business firms of all sizes select their financial structure in view of the cost, nature, and availability

of financial alternatives. For a number of reasons, our understanding of this decision for large and

small firms is deficient.

In addition, Pettit and Singer (1985, p. 58) posit that the ‘level of debt and equity in a smaller firm is more

than likely a function of the characteristics of the firm and its managers’. Levin and Travis (1987, p. 30)

provide support for this view, suggesting:

In the private corporation, leverage theory doesn’t always apply. The owners’ attitudes towards

personal risk – not the capital structuring policies public companies use – determine what amounts

of debt and equity are acceptable.

Finally, McMahon et al. (1993, p. 244) reason that:

Given the initial failure of modern finance theory to provide normative and practicable guidance on

making financial structure decisions in business enterprises generally, and particularly in small

enterprises, the only alternative is to seek for a positive theory.

The continued absence of a widely accepted normative theory of financial structure for business enterprises

thus underscores the importance of developing and testing the veracity of positive theories of business

financing like the so-called Pecking Order Theory ( POT).

The following outline of the POT of business financing is provided by Myers (1984, p. 581):

• Firms prefer internal finance.

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• They adapt their target dividend payout ratios to their investment opportunities, although dividends

are sticky and target payout ratios are only gradually adjusted to shifts in the extent of valuable

investment opportunities.

• Sticky dividend policies, plus unpredictable fluctuations in profitability and investment

opportunities, mean that internally generated cash-flow may be more or less than investment

outlays. If it is less, the firm first draws down its cash balance or marketable securities portfolio.

• If external finance is required, firms issue the safest security first. That is, they start with debt, then

possibly hybrid securities such as convertible bonds, then perhaps equity as a last resort. In this

story, there is no well-defined target debt-equity mix, because there are two kinds of equity, internal

and external, one at the top of the pecking order and one at the bottom. Each firm’s observed debt

ratio reflects its cumulative requirements for external finance.

In summary, the POT states that businesses adhere to a hierarchy of financing sources and prefer internal

financing when available; and, if external financing is required, debt is preferred over equity.

The principal objective in this paper is to ascertain the extent to which Myers’ (1984) POT of

business financing appears to explain financial structure amongst a panel of 871 manufacturing SMEs

legally organised as proprietary companies. This is made possible by the recent availability of data from the

Australian federal government’s Business Longitudinal Survey (BLS). The paper proceeds as follows.

After reviewing prior research on the POT as it applies to SMEs, the research method is outlined.

Thereafter, the findings of the research are presented, followed by conclusions arising from this

investigation.

Prior Research

Relevance of POT for SMEs

Initially, the POT sought mainly to explain the observed financing practices of large publicly traded

corporations. However, it was soon recognised that the theory may also apply to the financing practices of

non-publicly traded SMEs that might not have the additional financing alternative of issuing external equity

finance. Scherr et al. (1990, p. 10) consider the POT to be an appropriate description of SMEs’ financing

practices, because the ‘Pecking order hypothesis is in keeping with the prior findings that debt is by far the

4

largest source of external finance for small business’. In addition, Holmes and Kent (1991, p. 145) suggest

that in SMEs ‘managers tend to be the business owners and they do not normally want to dilute their

ownership claim’. Thus, the issue of external equity finance, and the consequential dilution of ownership

interest, may be further down the pecking order. The theory’s application to SMEs implies that external

equity finance issues may be inappropriate. In relation to the owner-manager’s control over operations and

assets, if the POT holds, then internal equity finance will be preferred, because this form of finance does

not surrender control. When external financing is required, obtaining debt rather than equity finance is

favoured, because this places fewer restrictions on the owner-manager.

Norton’s (1991, p. 287) support for this application of the POT to SMEs is evident in his assertion

that:

. . . contrary to financial theory, factors dealing with bankruptcy costs, agency costs, and information

asymmetries play little, if any, major role in affecting capital structure policy. Rather, the . . .

financial officers seem to follow a ‘pecking order’ in financing their firm’s needs.

Hall et al. (2000, p. 299) argue that the information asymmetry and agency problems arising between

owner-managers and outside investors providing external finance which give rise to the POT are ‘more

likely to arise in dealings with small enterprises because of their “close” nature, i.e. being controlled by one

person or a few, related people, and their having fewer disclosure requirements’. Scherr et al. (1993, p. 21)

indicate the costs information asymmetry creates are more important for SMEs than for large enterprises,

‘making differences in costs between internal equity, debt, and external equity consequently greater.

Therefore, the hierarchical approach should have even more appeal to small firms than to large’. In

addition, the theory’s assumption that managers act on behalf of existing shareholders is more relevant to

SMEs, because of their closely held nature, and because the managers are usually the existing shareholders.

Since the POT is pertinent to both SMEs and large enterprises, the theory may therefore explain the

observed differences between SMEs and large enterprises’ financial structures. Holmes and Kent (1991, pp.

145-146) explain that the application of the POT to SMEs is constrained by the following two factors:

• Small firms usually do not have the option of issuing additional equity to the public.

• Owner-managers are strongly averse to any dilution of their ownership interest and control (which

are normally one and the same). This is in contrast to the managers of large firms who usually only

5

have a limited degree of control and often have limited, if any, ownership interest, and are therefore

prepared to recognise a broader range of funding options.

Ang (1991) provides an alternative to this constrained POT, proposing a modified pecking order of

financing preferences for SMEs. This involves new capital contributions from owners ranking behind

internal finance, but in front of debt finance. Ang (1991, p. 9) reasons that the actual equity contributions

made by the owner-managers of SMEs are often difficult to measure, because ‘There are also implicit

equity contributions in the form of reduced or below market pay and overtime. The exact cost of these

sources is not well understood’.

Cosh and Hughes (1994, p. 33) argue that within an overall POT, SMEs when compared to large

enterprises would:

• Rely more on carrying ‘excess’ liquid assets to meet discontinuities in investment programs.

• Rely more on short-term debt including trade credit and overdrafts.

• Rely less on new shareholders’ equity compared to ‘internal’ equity and to debt in raising new

finance.

• Rely to a greater extent on hire purchase and leasing arrangements.

Thus, in relation to SME’s debt financing, Cosh and Hughes (1994) propose a refinement of the theory,

because of the lack of information to assess risk, both individual and collective, of SMEs.

Fama and French (2000, p. 28) reveal a blemish in the application of the POT to SMEs in that ‘less

levered non-payers [of dividends] are more profitable, which is consistent with the pecking order model.

But less levered non-payers also have better investments’. Fama and French (2000, p. 28) suggest that ‘the

spread of investment … and earnings … is higher for less levered non-payers. From the perspective of the

simple pecking order model, the low leverage (book and market) of these firms is anomalous’. That is, the

lower free cash flows or higher spreads of investment over earnings for enterprises with lower leverage are

not consistent with the POT. Fama and French (2000, p. 28) go on to reveal that:

The less levered non-payers are typically small growth firms. It is possible that these firms conform

to the complex rather than the simple version of the pecking order model; they keep leverage low to

have low-risk debt capacity available to finance future growth. But … they seem to achieve this

result by violating the pecking order. Specifically, the least levered non-payers make the largest net

6

new issues of stock … (the form of financing most subject to asymmetric information problems),

even though they have low risk debt capacity. This is not proper pecking order behavior.

This quotation recognises the possibility of modifying the financing pecking order for growth SMEs. This

could be so because of owner-managers’ attitudes to the option of raising external equity, and to any

dilution of their control. Thus, the theory may explain the observed differences between SME’s and growth

SME’s financial structures.

Research Hypotheses

Having established the potential relevance of the POT for SMEs, it remains in this section of the paper to

establish hypotheses which, if not rejected by the findings of this research, would suggest pecking order

financing behaviour amongst the Australian manufacturing SMEs in the BLS panel. For space reasons, the

expedient is taken here of summarising a growing theoretical and empirical literature as in Table 1.

INSERT TABLE 1 ABOUT HERE

Table 1 first identifies dependent and independent variables deemed relevant and appropriate by the

research literature. These receive some comment below. The table then presents the hypothesised sign of

the coefficients for the independent variables, mainly as suggested by prior studies that are referenced.

Following the lead of prior research in the area (Van der Wijst and Thurik, 1993; Hutchinson et al.,

1998; Michaelas et al., 1999; Hall et al., 2000), three dependent variables representing the main

components of financial structure (based on balance sheet book values) are used in this study. That is, the

research employs separate variables for short-term and long-term debt ratios, which are then aggregated

into a variable for total debt ratio. Van der Wijst and Thurik (1993, p. 59) suggest that ‘Estimating separate

relations for long and short term debt ratios . . . allows for influences on maturity structure of debt as well

as on leverage’. Van der Wijst and Thurik (1993, p. 62) go on to conclude that:

… the influences encountered in the analyses are far less straightforward than the hypothesized

effects in the theory. Most variables influence the maturity structure of debt rather than leverage: the

effects on long and short term debt tend to cancel out.

The results of Hutchinson et al. (1998, p. 4) reveal that ‘influences on total debt were found to be

the net effect of opposite influences on long and short-term debt for some variables’. Thus, using three

dependent variables allows research to examine influences on the maturity structure of debt as well as the

total debt position of sample SMEs. As Michaelas et al. (1999, p. 119) indicate, ‘There is [a] likelihood that

7

leverage related costs of short-term debt may differ from those of long-term debt’. Michaelas et al. (1999,

p. 119) go on to acknowledge that ‘While firms may have separate policies with regard to short-term debt,

there is likely to be some interaction between the levels of long-term and short-term borrowing’. Hall et al.

(2000, p. 303) argue that ‘By examining both long-term and short-term measures of leverage it should be

possible to determine whether the factors that influence short-term debt differ from those that determine

long-term debt’.

The independent variables for this study identified in Table 1 are generally measured in a

conventional manner. However, three of them require some explanation. The metric variable ‘Asset

structure’ is fixed (or non-current or long-term) assets as a percentage of total assets. The categorical

variable ‘Management/ownership structure’ stems from previous research using the same data undertaken

by one of the authors (McMahon, forthcoming). In that research, the manufacturing SMEs studied were

classified as being either low or high agency cost businesses depending on the complexity of their

management/ownership structures and the values of certain proxy measures for equity agency costs.

Finally, the categorical variable ‘Willingness to sell equity’ reflects recent reported experiences of doing so

by the SMEs surveyed. As will be seen, the presentation of findings for this study focuses wholly upon the

apparent sign and statistical significance of the coefficients for the chosen independent variables in

multivariate modelling of the dependent variables described above.

Research Method

Research Data

The panel data employed in this research are drawn from the Business Longitudinal Survey (BLS)

conducted by the Australian Bureau of Statistics (ABS) on behalf of the federal government over the four

financial years 1994-95 to 1997-98. Costing in excess of $4 million, the BLS was designed to provide

information on the growth and performance of Australian employing businesses, and to identify selected

economic and structural characteristics of these businesses.

The ABS Business Register was used as the population frame for the survey, with approximately

13,000 business units being selected for inclusion in the 1994-95 mailing of questionnaires. For the 1995-

96 survey, a sub-sample of the original selections for 1994-95 was chosen, and this was supplemented with

8

a sample of new business units added to the Business Register during 1995-96. The sample for the 1996-97

survey was again in two parts. The first formed the longitudinal or continuing part of the sample,

comprising all those remaining live businesses from the 1995-96 survey. The second part comprised a

sample of new business units added to the Business Register during 1996-97. A similar procedure was

followed for the 1997-98 survey. Approximately 6,400 business units were surveyed in each of 1995-96,

1996-97 and 1997-98. The BLS did not employ completely random samples. The original population (for

1994-95) was stratified by industry and business size, with equal probability sampling methods being

employed within strata. Further stratification by innovation status, exporting status and growth status took

place for the 1995-96 survey.

Data collection in the BLS was achieved through self-administered, structured questionnaires

containing essentially closed questions. Copies of the questionnaires used in each of the four annual

collections can be obtained from the ABS. The questionnaires were piloted prior to their first use, and were

then progressively refined after each collection in the light of experience. Various imputation techniques,

including matching with other data files available to the ABS, were employed to deal with any missing

data. Because information collected in the BLS was sought under the authority of the Census and Statistics

Act 1905, and thus provision of appropriate responses to the mailed questionnaires could be legally

enforced by the Australian Statistician, response rates were very high by conventional research standards –

typically exceeding 90 per cent.

The specific BLS data used in this study are included in a Confidentialised Unit Record File

(CURF) released by the ABS on CD-ROM in December, 1999. This CURF contains data on 9,731 business

units employing fewer than 200 persons – broadly representing SMEs in the Australian context. Restricted

industrial classification detail, no geographical indicators, presentation of enterprise age in ranges, and

omission of certain data items obtained in the BLS all help to maintain the confidentiality of unit records.

Furthermore, all financial variables have been subject to perturbation – a process in which values are

slightly varied to provide further confidentiality protection.

This research is concerned only with the manufacturing sector of the BLS CURF. The main reason

for this is that it is highly probable that cross-industry differences in the nature of business activities,

typical employment per business, capital intensity, and so on could confound findings. Over 99 per cent of

9

all businesses in the Australian manufacturing sector are SMEs according to generally accepted definitions

(Australian Bureau of Statistics, 1996). There are 3,411 manufacturing SMEs in the BLS CURF,

representing approximately 35 per cent of businesses in the file.

Additional focus is provided to this research by considering only manufacturing SMEs legally

organised as proprietary companies. The main reason for this further narrowing of the unit of analysis is to

avoid difficulties that would arise if the study sample contains both incorporated and unincorporated

businesses. These occur because of the customary procedural difference in accounting for owners’ wages,

which are not separately reported in the BLS data. There are 2,413 manufacturing SMEs legally organised

as proprietary companies in the BLS CURF, representing approximately 71 per cent of manufacturing

SMEs in the file.

Finally, because a key question requesting information on the proportion of an SME’s equity that is

held by owner-managers was not asked in the 1994-95 survey, the analysis presented in this paper is

confined to data for the 1995-96, 1996-97 and 1997-98 financial years only.

Data Analysis

The analytical model for this study, derived from the prior research reviewed earlier, is as illustrated in

Figure 1.

INSERT FIGURE 1 ABOUT HERE

This model represents profitability, enterprise growth and size, enterprise age, and certain other enterprise

characteristics (essentially controls) as likely to influence the financial structure of SMEs. The key study

relationship for the model can be represented mathematically as follows:

Fs = f (Pa, Gb, Sc, A, Cd) Equation 1

where Fs = financial structure, dependent variables

Pa = profitability, independent variables

Gb = enterprise growth, independent variables

Sc = enterprise size, independent variables

A = enterprise age, independent variable

Cd = other enterprise characteristics, independent (control) variables

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Thus, it is for the research to ascertain whether such dependencies seem to prevail in the study sample, and

infer if they are likely to exist in the population of Australian manufacturing SMEs.

As indicated earlier, three dependent variables representing the main components of financial

structure (based on balance sheet book values) are used in this study. That is, the research employs separate

variables for short-term and long-term debt ratios, which are then aggregated into a variable for total debt

ratio. Some descriptive statistics for these three metric dependent variables are presented in Table 2.


Examination of Table 2 reveals that, in each of the three years of the longitudinal study, the manufacturing

SMEs maintained quite high levels of short-term debt to total funding, in the range 37 to 41 per cent. By

contrast, long-term debt to total funding is in the range 9 to 13 per cent. Overall, total debt to total funding

in the range 63 to 65 per cent reveals that the financial structure of the SMEs examined is clearly debt-

oriented. The significance values for a series of Kolmogorov-Smirnov one-sample tests suggest that these

metric dependent variables are far from being normally distributed. Transformation of the variables to

produce normal distributions has been avoided because of difficulties of interpretation often created by

such procedures. Thus, the three dependent variables have been dichotomised into below-median and

above-median categories for modelling purposes.

The independent variables employed in this research, largely suggested by prior research in the area,

have already been revealed in Table 1. For reasons of space, descriptive statistics for the various

independent variables are not included in this paper, but they can be provided by the authors on request.

The significance values for a series of Kolmogorov-Smirnov one-sample tests suggest that all the metric

independent variables are not normally distributed. As with the dependent variables, transformation of the

metric independent variables to produce normal distributions has been avoided. A series of associative tests

suggests the possibility of multicollinearity between the profitability measures ‘Return on total assets’ and

‘Net margin on sales’, and between the enterprise size measures ‘Total assets’, ‘Annual sales’ and ‘Total

employment’. As will become evident, simultaneous use in modelling of multicollinear independent

variables has been precluded.

11

The principal modelling procedure used in this research is logistic regression (also referred to as

‘logit analysis’). The main reason for choosing this multivariate technique is the categorical (that is, non-

metric) nature of the dependent variables. As Hair et al. (1995, p. 130) point out:

. . . discriminant analysis is also appropriate when the dependent variable is nonmetric. However,

logit analysis may be preferred for several reasons. First, discriminant analysis relies on strictly

meeting the assumptions of multivariate normality and equal variance-covariance matrices across

groups, features not found in all situations. Logit analysis does not face these strict assumptions,

thus making its application appropriate in many more situations. Second, even if the assumptions are

met, many researchers prefer logit analysis because it is similar to regression with its straightforward

statistical tests, ability to incorporate nonlinear effects, and wide range of diagnostics. For these and

more technical reasons, logit analysis is equivalent to discriminant analysis and may be more

appropriate in certain situations.

The assumptions underlying logistic regression are undemanding and its use with the irregularly distributed

(that is, non-normal) data available to the present study is entirely appropriate (Aldrich and Nelson, 1984).

Further information on logistic regression as a statistical technique is presented in an Appendix to the

paper.

Research Findings

Short-Term Debt to Total Funding

The first stage of the multivariate logistic regression modelling undertaken employed a dichotomous

dependent variable indicating whether short-term debt to total funding is above or below the median value

for this ratio amongst the 871 manufacturing SMEs in the longitudinal panel. Separate modelling was

undertaken for each of the three years considered in the study. The year 1997-98 was actually modelled

twice – once using simple rates of growth in assets, sales and employment for that year, and once using

compound rates of growth in assets, sales and employment over the three years of the study. For any one

year, avoiding the joint inclusion of multicollinear independent variables meant producing six models:

• Three models used ‘Return on total assets’ as the operating profitability measure, and three models

used ‘Net margin on sales’ for this purpose.

• Two models used ‘Total assets’ as the enterprise size measure, two models used ‘Annual sales’ for

this purpose, and two models used ‘Total employment’ for this purpose.

12

Results from this modelling effort, expressed in terms of the apparent sign and statistical significance of the

coefficients for the chosen independent variables, are presented in Table 3.


Comments below focus upon the shaded independent variables in Table 3 for which there appear to be

relatively consistent statistically significant relationships with the dependent variable in a multivariate

context.

It would appear from the modelling findings that short-term debt to total funding for the business

concerns studied is significantly influenced by:

• Operating profitability as measured by either ‘Return on total assets’ or ‘Net margin on sales’. In

both cases the sign of the regression coefficient is negative, as hypothesised earlier on the basis of

prior writing and empirical research on the POT of business financing. The implication is that the

less profitable an SME is, and therefore the less self-sufficient it is through reinvestment of profits,

the more likely it will need to depend upon short-term debt financing for its assets and activities.

• Recent (simple) annual sales growth. The sign of the regression coefficient is positive, as

hypothesised earlier on the basis of prior writing and empirical research on the POT of business

financing. The implication is that growth in an SME’s sales creates financing pressures that are

likely to be met, at least initially, with short-term debt.

• Enterprise size as measured by ‘Total assets’. The sign of the regression coefficient is positive,

contrary to the hypothesis presented earlier on the basis of prior writing and empirical research on

the POT of business financing. The implication is that the larger an SME is in terms of assets, the

more likely it will need to depend upon short-term debt financing for those assets. This would be the

case, of course, if limited access to longer-term debt and equity financing arising from an alleged

‘finance gap’, prevented the business from following the financial management dictum of matching

the term of finance used to the term of assets acquired (the so-called ‘matching’ or ‘hedging’

principle). It could also be conjectured that SMEs might fall into such circumstances because of

ignorance of this dictum or principle.

• Enterprise age. The sign of the regression coefficient is negative, as hypothesised earlier on the basis

of prior writing and empirical research on the POT of business financing. The implication is that the

13

younger an SME is, and therefore the less time it has had to become self-sufficient through

reinvestment of profits, the more likely it will need to depend upon short-term debt financing for its

assets and activities.

• Asset structure measuring fixed (or non-current or long-term) assets as a percentage of total assets.

The sign of the regression coefficient is negative, as hypothesised earlier on the basis of prior

writing and empirical research on the POT of business financing. The implication is that the lower

the proportion of fixed assets held by an SME, the more likely it will be that it depends upon short-

term debt financing for its assets. This is, of course, in accord with the dictates of the matching or

hedging principle.

Overall, then, the findings of this research appear to be consistent with the POT of business financing as

regards short-term debt to total funding of the SMEs studied. There does, however, seem to be a suggestion

that these businesses may not choose to, or be able to, adhere to the matching or hedging principle with

respect to short-term financing of assets.

Long-Term Debt to Total Funding

The second stage of the multivariate logistic regression modelling undertaken employed a dichotomous

dependent variable indicating whether long-term debt to total funding is above or below the median value

for this ratio amongst the 871 manufacturing SMEs in the longitudinal panel. The extent and pattern of

modelling undertaken were similar to those already described for short-term debt to total funding. Results

from this modelling effort, expressed in terms of the sign and statistical significance of the coefficients for

the chosen independent variables, are presented in Table 4.




context.

It would appear from the modelling findings that long-term debt to total funding for the business

concerns studied is significantly influenced by:

• Operating profitability as measured by ‘Return on total assets’. The sign of the regression coefficient

is negative, as hypothesised earlier on the basis of prior writing and empirical research on the POT

14

of business financing. The implication is that the less profitable an SME is, and therefore the less

self-sufficient it is through reinvestment of profits, the more likely it will need to depend upon long-

term debt financing for its assets and activities.

• Enterprise size as measured by ‘Total employment’. The sign of the regression coefficient is

positive, as hypothesised earlier on the basis of prior writing and empirical research on the POT of

business financing. The implication is that the larger an SME is in terms of employment, the more

likely it will depend upon long-term debt financing. This would be the case, of course, if access to

longer-term debt financing is dictated, to some degree, by the size of the business.




reinvestment of profits, the more likely it will need to depend upon long-term debt financing for its

assets and activities.

• Asset structure measuring fixed (or non-current or long-term) assets as a percentage of total assets.

The sign of the regression coefficient is positive, as hypothesised earlier on the basis of prior writing

and empirical research on the POT of business financing. The implication is that the higher the

proportion of fixed assets held by an SME, the more likely it will be that it depends upon long-term

debt financing for its assets. This is, of course, in accord with the dictates of the matching or

hedging principle.


regards long-term debt to total funding of the SMEs studied.

Total Debt to Total Funding

The final stage of the multivariate logistic regression modelling undertaken employed a dichotomous

dependent variable indicating whether total debt to total funding is above or below the median value for

this ratio amongst the 871 manufacturing SMEs in the longitudinal panel. The extent and pattern of

modelling undertaken were similar to those already described for the previous stages. Results from this

modelling effort, expressed in terms of the sign and statistical significance of the coefficients for the chosen

independent variables, are presented in Table 5.

15




context.

It would appear from the modelling findings that total debt to total funding for the business concerns

studied is significantly influenced by:

• Operating profitability as measured by either ‘Return on total assets’ or ‘Net margin on sales’. In

both cases the sign of the regression coefficient is negative, as hypothesised earlier on the basis of

prior writing and empirical research on the POT of business financing. The implication is that the

less profitable an SME is, and therefore the less self-sufficient it is through reinvestment of profits,

the more likely it will need to depend upon debt financing of whatever term for its assets and

activities.

• Enterprise size as measured by ‘Total assets’. The sign of the regression coefficient is positive,

contrary to the hypothesis presented earlier on the basis of prior writing and empirical research on

the POT of business financing. The implication is that the larger an SME is in terms of assets, the

more likely it will depend upon debt financing of whatever term for those assets. This would be the

case, of course, if limited access to equity financing prevented the business from ‘balancing’ its use

of debt and equity as per ‘optimal financial structure’ theory. It could also be conjectured that SMEs

might fall into such circumstances because of so-called ‘external equity aversion’ amongst their

owner-managers, reflecting their reluctance to surrender ownership and control of their businesses to

outside parties like venture capitalists, business angels, etc. that might seek equity participation in

return for their support.




reinvestment of profits, the more likely it will need to depend upon debt financing of whatever term

for its assets and activities.

16


regards total debt to total funding of the SMEs studied. There does, however, seem to be a suggestion that

these businesses may not choose to, or be able to, adhere to the dictates of optimal financial structure

theory in ‘balancing’ their use of debt and equity financing.

Conclusions and Recommendations

The key findings from this research into the POT and financial structure amongst Australian manufacturing

SMEs included in the BLS CURF panel can be summarised as follows:

• There is now further substantial empirical evidence broadly suggesting pecking order financing

behaviour amongst SMEs.

• However, there is also further substantial empirical evidence suggesting the need for a modified

POT that more fully reflects the special circumstances and nuances of SME financing.

These findings are generally consistent with those of prior studies like those undertaken by Ang (1991),

Holmes and Kent (1991), Cosh and Hughes (1994), and Fama and French (2000). The principal

modifications to the POT indicated by this body of research arise from such phenomena as below market

financial returns often accepted by SME owners and owner-managers, the alleged finance gap faced by

SMEs seeking longer-term development capital, the widespread failure of SMEs to follow the dictates of

the matching or hedging principle, the common usage of so-called ‘quasi-equity’ by SMEs, frequent

reliance upon financing from family and friends (so called, ‘F-connections’) of SME owners and owner-

managers, and the recognised prevalence of external equity aversion amongst SME owners and owner-

managers.

On the basis of this and prior empirical research in the field, a full specification for a modified POT

of financing for SMEs could appear as follows (from most preferred source of finance to least preferred):

• Reinvestment of profits (fully reflecting ‘in-kind’ contributions of existing owner-managers such as

long working hours and below market salaries).

• Short-term debt financing (beginning with major reliance upon trade credit and including use of

personal credit card financing).

17

• Long-term debt financing (possibly beginning with longer-term loans from existing owners and

owner-managers (that is, quasi-equity), and perhaps from their families and friends).

• New equity capital injections from existing owners and owner-managers (perhaps including their

families and friends, and fully reflecting acceptance by existing owners and owner-managers of low

or zero dividends).

• New equity capital from hitherto uninvolved parties (including new owners and owner-managers,

venture capitalists, business angels and Second Board listing).

Note that this proposed POT for SMEs differs from that of Holmes and Kent (1991) in that the possibility

of raising new equity capital from hitherto uninvolved parties is included. While Holmes and Kent (1991)

heavily discount this alternative, government policy initiatives over the last decade have been moderately

successful in improving the institutional framework for external equity raising by principally medium-sized

enterprises. Note also that, in contrast to the suggestion of Ang (1991), new equity capital injections from

existing owners and owner-managers, and possibly their families and friends, are included after debt

financing. Ang (1991) proposes that this alternative should follow reinvestment of profits and precede debt

financing. However, the BLS panel data for manufacturing SMEs reveal that only a small proportion

(typically 5 to 10 per cent) of these businesses ever undertake new equity financing, and that debt financing

appears to dominate the balance sheets of such concerns. Where new equity finance is raised, it is

predominantly (typically in excess of 80 per cent) sourced from existing owners and owner-mangers, and

from their families and friends.

Apart from proposing a modified POT for SMEs as a basis for further inquiry in the area, two other

recommendations for further research using the BLS data set can be made. The first is the clear need to

ascertain the extent to which the POT of business financing (however modified) appears to explain

financial structure amongst SMEs in industries other than manufacturing. This could be especially

important for less capital intensive industries with more modest financing requirements than

manufacturing. The second recommendation is to examine much more closely the rather curious indication

from this study that enterprise growth may not be an important influence upon the financial structure of the

manufacturing SMEs investigated. Recall that recent annual sales growth seems to significantly influence

the short-term debt to total funding ratio in a positive manner; the implication being that growth in an

18

SME’s sales creates financing pressures that are likely to be met, at least initially, with short-term debt.

However, asset and employment growth do not appear to significantly impact short-term debt to total

funding. Furthermore, none of asset, sales or employment growth seems to significantly influence long-

term debt to total funding or total debt to total funding. Given that enterprise growth is most likely to be the

key driver for seeking new financing, these findings are counter-intuitive. They are also inconsistent with

the prior research of (inter alia) Agarwal (1979), Allen (1993), Vos and Forlong (1996), Jordan et al.

(1998), Michaelas et al. (1999), Fama and French (2000), and Hall et al. (2000).

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19

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Longitudinal Survey’, Research Paper Series No. 01-7, School of Commerce, The Flinders University

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Business Longitudinal Survey’, Small Business Economics.

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21

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22

Table 1: Hypothesised Sign of Coefficients for Independent Variables

Dependent Variable Independent Variable Short-Term Debt

to Total Funding Long-Term Debt to Total Funding


Return on owners equity

Negative (Van der Wijst and Thurik, 1993; Chiarella et al., 1992)

Negative (Van der Wijst and Thurik, 1993)

Negative (Van der Wijst and Thurik, 1993)

Return on total assets

Negative (Michaelas et al., 1999)

Negative (Michaelas et al., 1999)

Negative (Constand et al., 1991; Michaelas et al., 1999; Allen, 1993)

Net margin on sales

Negative (Chittenden et al., 1996; Hall et al., 2000)

Negative (Hutchinson et al., 1998)

Negative (Chittenden et al., 1996; Hutchinson et al., 1998)

Assets growth – simple and compound

Positive (Michaelas et al., 1999)

Positive (Michaelas et al., 1999)

Positive (Michaelas et al., 1999; Allen, 1993)

Sales growth – simple and compound

Positive (Hall et al., 2000)

Positive (Vos and Forlong, 1996)

Positive (Vos and Forlong, 1996; Jordan et al., 1998)

Employment growth – simple and compound

Positive (Agarwal, 1979)a



Total assets Negative (Chittenden et al., 1996; Hutchinson et al., 1998; Michaelas et al., 1999)

Positive (Constand et al., 1991; Chittenden et al., 1996; Hutchinson et al., 1998; Michaelas et al., 1999; Hall et al., 2000)

Negative (Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998)b

Annual sales Negative (Scherr and Hulburt, 2001)

Positive (Constand et al., 1991; Scherr and Hulburt, 2001)


Total employment Negative (Agarwal, 1979)a



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Table 1 (cont.): Hypothesised Sign of Coefficients for Independent Variables

Dependent Variable Independent Variable Short-Term Debt

to Total Funding Long-Term Debt to Total Funding


Enterprise age Negative (Hutchinson et al., 1998; Michaelas et al., 1999; Hall et al., 2000)

Negative (Chittenden et al., 1996; Hutchinson et al., 1998; Michaelas et al., 1999; Hall et al., 2000)

Negative (Chittenden et al., 1996; Hutchinson et al., 1998; Michaelas et al., 1999)

Asset structure Negative (Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998; Hall et al., 2000)

Positive (Constand et al., 1991; Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998; Michaelas et al., 1999)


Management/ ownership structure

Negative (Jensen and Meckling, 1976; Myers and Majluf, 1984; Ang et al., 2000; Graham and Harvey, 2001; McMahon, 2001)c



Willingness to sell equity

Negatived

Negatived

Negatived

Business plan Positive (Romano et al., 2001)

Positive (Romano et al., 2001)

Positive (Kotey, 1999; Romano et al., 2001)

Family business Positive (Romano et al., 2001)

Positive (Romano et al., 2001)

Positive (Bopaiah, 1998; Romano et al., 2001)

a The empirical evidence of Agarwal (1979) reveals that the three measures of enterprise size have

correlation coefficients between 0.88 to 0.97, thereby implying total assets, annuals sales and total

employment are highly associated. b The overall association with total debt reflects an averaging out of opposite signs for short-term debt and

long-term debt, and the high proportion of short-term debt in total debt. c Enterprises with low management ownership would use internal funds to avoid high costs of external

financing associated with the substantial information asymmetry between insiders and outside suppliers

of finance. d SMEs with a willingness to employ new equity financing would have lower debt-to-total funding ratios

on the grounds that total funding is defined as total liabilities plus owners equity.

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Table 2: Descriptive Statistics for Dependent Variables

Year Dependent Variable Statistics 1995-96 1996-97 1997-98

Median (per cent) 41.0 38.4 36.7 Kolmogorov-Smirnov statistic .118 .098 .092 Degrees of freedom 871 871 871

Short term debt to total funding

Significance .000 .000 .000 Median (%) 8.7 12.5 10.9 Kolmogorov-Smirnov statistic .354 .249 .326 Degrees of freedom 871 871 871

Long-term debt to total funding

Significance .000 .000 .000 Median (%) 65.2 63.3 62.6 Kolmogorov-Smirnov statistic .200 .105 .168 Degrees of freedom 871 871 871

Total debt to total funding

Significance .000 .000 .000

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Table 3: Sign and Statistical Significance of Coefficients for Independent Variables

in Logistic Regression Modelling of Short-Term Debt to Total Funding

Sign and Statistical Significance of Coefficient (see Note below) Independent Variable 1995-96a 1996-97a 1997-98a 1997-98b

6/6 Positive 6/6 Positive 6/6 Positive 6/6 Positive Return on owners equity 0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Return on total assets 0/3 Significant 3/3 Significant** 3/3 Significant** 3/3 Significant** 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Net margin on sales

3/3 Significant** 3/3 Significant** 3/3 Significant** 3/3 Significant** 6/6 Positive# 6/6 Negative 6/6 Negative n.a. Assets growth – simple

0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Positive# n.a. Sales growth – simple

6/6 Significant* 6/6 Significant* 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Positive# n.a. Employment growth –

simple 0/6 Significant 0/6 Significant 0/6 Significant n.a. n.a. n.a. 6/6 Negative Assets growth – compound

0/6 Significant n.a. n.a. n.a. 6/6 Positive# Sales growth – compound

0/6 Significant n.a. n.a. n.a. 6/6 Positive# Employment growth –

compound 0/6 Significant 2/2 Positive 2/2 Positive 2/2 Positive 2/2 Positive Total assets

1/2 Significant* 2/2 Significant* 1/2 Significant* 2/2 Significant* 2/2 Positive 2/2 Positive 2/2 Positive 2/2 Positive Annual sales

0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 2/2 Negative# 2/2 Negative# 2/2 Positive 2/2 Negative# Total employment 0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Enterprise age

3/6 Significant* 6/6 Significant* 6/6 Significant* 5/6 Significant* 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Asset structure

6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Management/ownership

structure 1/6 Significant* 0/6 Significant 3/6 Significant* 0/6 Significant 6/6 Negative# 6/6 Negative# 6/6 Positive 6/6 Positive Willingness to sell equity 0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 4/6 Positive# 6/6 Negative 6/6 Negative 6/6 Negative Business plan

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Negative 6/6 Negative 6/6 Negative 6/6 Negative Family business

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant a, b Using simple (year) and compound (1995-98) enterprise growth rates in the models. # Indicates coefficient sign for independent variable is consistent with prior research. *, ** Indicate statistical significance at the 5 and 1 per cent levels. Note: By way of example, 4/6 means four of six logistic regression models. Shading indicates relatively consistent statistically significant relationships are evident over the years covered by the research.

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in Logistic Regression Modelling of Long-Term Debt to Total Funding


6/6 Positive 6/6 Positive 6/6 Positive 6/6 Positive Return on owners equity 0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Return on total assets

2/3 Significant* 3/3 Significant* 3/3 Significant** 3/3 Significant** 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Net margin on sales 0/3 Significant 0/3 Significant 0/3 Significant 0/3 Significant 6/6 Positive# 6/6 Positive# 6/6 Negative n.a. Assets growth – simple

0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Negative n.a. Sales growth – simple

0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Negative 4/6 Negative n.a. Employment growth –

simple 0/6 Significant 0/6 Significant 0/6 Significant n.a. n.a. n.a. 6/6 Negative Assets growth – compound

0/6 Significant n.a. n.a. n.a. 6/6 Negative Sales growth – compound


compound 0/6 Significant 2/2 Positive# 2/2 Positive# 2/2 Positive# 2/2 Positive# Total assets

0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 2/2 Positive# 2/2 Positive# 2/2 Positive# 2/2 Positive# Annual sales

0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 2/2 Positive# 2/2 Positive# 2/2 Positive# 2/2 Positive# Total employment

2/2 Significant* 0/2 Significant 2/2 Significant* 2/2 Significant* 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Enterprise age

1/6 Significant* 0/6 Significant 6/6 Significant** 6/6 Significant** 6/6 Positive# 6/6 Positive# 6/6 Positive# 6/6 Positive# Asset structure

6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Management/ownership

structure 0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive 6/6 Positive 6/6 Positive 6/6 Positive Willingness to sell equity

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Negative 6/6 Negative Business plan

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Positive# 6/6 Positive# Family business

5/6 Significant* 0/6 Significant 0/6 Significant 0/6 Significant a, b Using simple (year) and compound (1995-98) enterprise growth rates in the models. # Indicates coefficient sign for independent variable is consistent with prior research. *, ** Indicate statistical significance at the 5 and 1 per cent levels. Note: By way of example, 4/6 means four of six logistic regression models. Shading indicates relatively consistent statistically significant relationships are evident over the years covered by the research.

27


in Logistic Regression Modelling of Total Debt to Total Funding


6/6 Positive 6/6 Positive 6/6 Positive 6/6 Positive Return on owners equity 0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Return on total assets 0/3 Significant 3/3 Significant** 3/3 Significant** 3/3 Significant** 3/3 Negative# 3/3 Negative# 3/3 Negative# 3/3 Negative# Net margin on sales

3/3 Significant** 3/3 Significant** 3/3 Significant** 3/3 Significant** 6/6 Positive# 6/6 Positive# 3/6 Positive# n.a. Assets growth – simple

0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive# 6/6 Positive# 6/6 Negative n.a. Sales growth – simple

0/6 Significant 0/6 Significant 0/6 Significant 6/6 Negative 6/6 Negative 6/6 Positive# n.a. Employment growth –

simple 0/6 Significant 0/6 Significant 0/6 Significant n.a. n.a. n.a. 3/6 Positive# Assets growth – compound

0/6 Significant n.a. n.a. n.a. 6/6 Negative Sales growth – compound


compound 0/6 Significant 2/2 Positive 2/2 Positive 2/2 Positive 2/2 Positive Total assets

2/2 Significant* 2/2 Significant** 1/2 Significant* 1/2 Significant* 2/2 Positive 2/2 Positive 2/2 Positive 2/2 Positive Annual sales

0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 2/2 Positive 2/2 Negative# 2/2 Negative# 1/2 Positive Total employment

0/2 Significant 0/2 Significant 0/2 Significant 0/2 Significant 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Enterprise age

6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Significant** 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Asset structure

6/6 Significant* 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Negative# 6/6 Negative# 6/6 Negative# 6/6 Negative# Management/ownership

structure 6/6 Significant* 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Positive 6/6 Positive 6/6 Positive 6/6 Positive Willingness to sell equity

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Negative 6/6 Negative 6/6 Negative 6/6 Negative Business plan

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant 6/6 Negative 6/6 Negative 6/6 Positive# 6/6 Positive# Family business

0/6 Significant 0/6 Significant 0/6 Significant 0/6 Significant a, b Using simple (year) and compound (1995-98) enterprise growth rates in the models. # Indicates coefficient sign for independent variable is consistent with prior research. *, ** Indicate statistical significance at the 5 and 1 per cent levels. Note: By way of example, 4/6 means four of six logistic regression models. Shading indicates relatively consistent statistically significant relationships are evident over the years covered by the research.

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Figure 1: Analytical Model for the Research

Financial Structure:

• Short-term debt vs total funding

• Long-term debt vs totalfunding

• Total debt vs total funding

Key Study Relationship

Enterprise Characteristics:

• Profitability

• Enterprise growth

• Enterprise size

• Enterprise age

• Other enterprise characteristics

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Appendix: Logistic Regression Modelling

The generalised form of the multivariate logistic regression model for the case of a dichotomous dependent

variable with values 0 or 1 and continuous independent variables can be expressed as follows:

ux...x)]1/(ln[ nn11 +β++β+φ=π−π Equation 2

where π = probability that the value of the dichotomous dependent variable, y, equals 1

x1, . . . , xn = independent variables

φ = constant

β1, . . . , βn = coefficients

u = stochastic disturbance term representing that part of ln[π/(1-π)]

which is unexplained by the independent variables

Note that the left hand side of the equation is not the dependent variable, y, itself; but the so-called ‘log

odds’ or ‘logit’ of y. Where an independent variable is categorical rather than continuous, two treatments

are possible. The variable can possibly be dealt with as if it is continuous. Alternatively, indicator (design

or dummy or contrast) variables may be created and coded as 0 or 1 for all but one category (usually the

last); and coefficients are estimated for each of these indicator variables. The latter treatment is more

common for polytomous independent variables whether they are nominal or ordinal. It is usually

recommended that dichotomous independent variables are treated as if they are continuous.

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