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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.
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.
3
• 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
10
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.
INSERT TABLE 2 ABOUT HERE
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.
INSERT TABLE 3 ABOUT HERE
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.
INSERT TABLE 4 ABOUT HERE
Comments below focus upon the shaded independent variables in Table 4 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 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.
• 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
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 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.
Overall, then, the findings of this research appear to be consistent with the POT of business financing as
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
INSERT TABLE 5 ABOUT HERE
Comments below focus upon the shaded independent variables in Table 5 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 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.
• 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
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 debt financing of whatever term
for its assets and activities.
16
Overall, then, the findings of this research appear to be consistent with the POT of business financing as
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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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
Total 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
Positive (Agarwal, 1979)a
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)
Negative (Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998)b
Total employment Negative (Agarwal, 1979)a
Positive (Agarwal, 1979)a
Negative (Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998)b
23
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
Total 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)
Negative (Van der Wijst and Thurik, 1993; Chittenden et al., 1996; Hutchinson et al., 1998)b
Management/ ownership structure
Negative (Jensen and Meckling, 1976; Myers and Majluf, 1984; Ang et al., 2000; Graham and Harvey, 2001; McMahon, 2001)c
Negative (Jensen and Meckling, 1976; Myers and Majluf, 1984; Ang et al., 2000; Graham and Harvey, 2001; McMahon, 2001)c
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.
24
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
25
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.
26
Table 4: Sign and Statistical Significance of Coefficients for Independent Variables
in Logistic Regression Modelling of Long-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
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
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
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
Table 5: Sign and Statistical Significance of Coefficients for Independent Variables
in Logistic Regression Modelling of Total 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 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
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
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.
28
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
29
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.