FINANCIAL RATIOS AND THE SIZE CLASSIFICATION OF SMALL
BUSINESS: SOME AUSTRALIAN EVIDENCE
David Forsaith (Flinders University, Adelaide) and
Jon Hall (Australian Bureau of Statistics, Canberra)
SCHOOL OF COMMERCE
RESEARCH PAPER SERIES: 01-2
ISSN: 1441-3906
A number of financial ratios of firms are analysed to see whether they provide guidance in
classifying firms by size. Regression techniques are applied to a large Australian sample of
more than 2000 businesses in the manufacturing sector and 844 businesses in the wholesale
sector covering the whole range of firm sizes (measured in terms of total employment). The
paper concludes that financial ratios and firm size are not closely related, although there is
some evidence to suggest that the employment-based divide between large and non-large
firms in the Australian manufacturing sector might lie around the 80 employees level of firm
size. There was no discernible relationship for the wholesale sector.
Introduction
Businesses are classified by size for a multitude of purposes by law makers, researchers, data
collection agencies and others. Whatever the purpose, there is at least an implicit intent that
businesses which fall into different size classifications will somehow be different from one
another.
This paper examines a large Australian database to see whether the size definitions used by
the Australian Bureau of Statistics (ABS) reflect fundamental and observable differences in
the characteristics and behaviour of firms. In particular, it explores for relationships between
firms’ financial ratios and their size to see whether relative financial ratios are suggestive of
boundaries between different firm size classifications. It follows a similar analysis of the
definitions of a small business used by the United States Small Business Administration
(Osteryoung et al, 1995).
Small Business Definitions
There is no unanimity in the business size definitions used across countries, or even by
different organisations and groups within a country (Cross, 1983; Ganguly, 1985). Definitions
differ in the break points they employ, and also in the underlying basis used for classification.
Employment is the most frequently used basis for business size classifications, but business
assets and sales revenue are alternative measures that are sometimes used. Australian research
has shown that these different measures are not interchangeable. Businesses may be classified
differently depending on whether employment, assets or sales revenue is used to measure
their size (Forsaith et al 1994, 1995). This result is not unexpected given the different
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production functions used by different firms and different industries.
Many people are more comfortable with a qualitative definition of a small business based on
common characteristics of the sector. We all know a small business when we see one. The
most frequently quoted such definition in Australia is that from the Beddall Inquiry (1990)
which suggested that a business is small if it has the following characteristics:
 It is independently owned and operated
 It is closely controlled by owner-managers who contribute most or all of the operating
capital; and
 The principal decision-making functions rest with the owner-managers.
Bannock (1981) considers owner-management, where the owners “participate in all principle
decisions and will, typically, know what is going on in all parts of their business” (p27), to be
a necessary but not sufficient condition for a firm to be classified as small. He argues that it
should also have only a small share of its market and should not be able to access public
capital markets.
Osteryoung and Newman (1993) suggested that a business be defined as small if its shares are
privately held and are not available to the public at large, and if the owners must personally
guarantee all financing of the business.
Exceptions can be found to every rule and expediency has generally required the adoption of
some quantitative definition where numbers can be used as the basis for size classification. As
Bannock (1981, p28) observes, “small firms are difficult to define because in the market
economy the unit organisation of economic activity is a continuum …with no clear break
points… In practice, economists and legislators alike are obliged to make arbitrary statistical
definitions”. Nevertheless, it is important that the definitions chosen are based on as much
information as possible, which provides the basis and rationale for this (and other) research on
this topic.
Osteryoung et al (1995) examined the US Small Business Administration (SBA) definitions
for the manufacturing and wholesaling sectors. The SBA defines a manufacturing business as
small if it has 500 or fewer employees and a wholesaling business as small if it has 100 or
fewer employees. They concluded that both of these break points were too high. Naturally
occurring break points based on differences in financial characteristics, operations and
performance appeared to be well below those used by the SBA. However, the overall
explanatory power of the relationships tested was low.
The Australian Bureau of Statistics also uses total employment as the basis for business size
classifications. For some of their collections, the ABS differentiates between the
manufacturing and services sectors. A business is considered small in manufacturing if it
employs fewer than 100, while a services business is small if it employs fewer than 20. This
definition of small suggests that a single break point between medium and large businesses in
Australia could be 200 total employment. In other collections, the ABS uses a larger number
of size classifications comprising micro-businesses (0-4 employees), other small businesses
(5-19 employees), medium businesses (20-99 employees), and large businesses (100 or more
employees).
This paper explores the financial ratios of Australian firms to see whether they vary with firm
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size and whether these ratios shed any light on the appropriateness of firm size classification
boundaries.
Data
This study utilised data from the Business Longitudinal Study (BLS) database collected by the
ABS, although only data from 1996-97 financial year were used for this paper. (This contrasts
with Osteryoung et al (1995) who used repeated annual observations of the same firms to
increase their sample size ten-fold.) Following Osteryoung et al (1995), only two sectors,
manufacturing and wholesaling, were examined. The sample sizes were 2051 firms in
manufacturing and 844 firms in wholesaling.
The BLS sampling came from employing business other than government enterprises and
enterprises in certain industrial classifications (principally the agricultural sector, utilities,
education and health and community services). Survey response rates exceeded 90%
reflecting the legal sanctions available to the Australian Statistician.
The BLS survey obtained data on the location, activity, employment operations and financial
details of businesses. This enables a number of financial ratios to be calculated and for any
relationship between the size of the business (in terms of numbers of employees) and these
ratios to be examined across all firms in the survey.
The financial ratios used in this study are the current ratio, inventory turnover ratio, total asset
turnover ratio, profit margin, debt ratio, return on assets and return on equity. These were
similar to the ratios reported by Osteryoung et al (1995), except that the receivables turnover
ratio could not be examined because data on accounts receivable were not collected by the
ABS. A priori, it might be expected that these ratios vary with firm size; eg , larger firms
might be able to economise on inventory holdings, or achieve higher profitability by being
able to spread overheads more widely. If such size-related relationships were found to be
sufficiently strong, they might provide guidance in classifying firms by size.
Inspection of the sample data showed that the tails of the distributions had extreme minimum
and maximum values which had a serious impact on the observed means of the financial
ratios. To remove these distortions, winsorisation procedures were applied to the bottom 1%
and top 1% of observations. By this technique, these firms were not removed from the
sample, but the values of their ratios were constrained to those of the first and ninety-ninth
percentile firms respectively. This still allows their low or high ratios to influence the
analysis, but not to the full extent that the extreme values of the raw data would allow (Sprent,
1993).
The treatment of employees was also examined to see whether the sample statistics were
sensitive to the fulltime/parttime split of employees. This appeared to have no impact on the
results and so the distinction has not been made.
The different effects of incorporated and unincorporated businesses on the data were also
examined. Sample statistics were analysed for all firms and for incorporated firms.
(Unincorporated firms – sole proprietorships and partnerships – were not analysed separately
because very few of them are large employers so that an adequate range of business size could
3
not be examined.) This distinction was of no consequence for the current, inventory turnover
or total assets turnover ratios, but including unincorporated businesses had the effects of
substantially increasing the mean values for the gross profit margin, return on assets and
return on equity and decreasing the gearing ratio. (These differences in means are consistent
with small enterprises being perceived as being more risky. Accordingly they are likely to be
more equity financed and to have higher required rates of return.) Therefore, both the all firms
and incorporated firms data sets were included in the study. (Even the incorporated data set
alone provides wider coverage of firm size than the Osteryoung et al (1995) study which only
included publicly traded firms.)
The size category breakdown of these samples is in table 1.
Table 1 Sample Size by Numbers of Employees
Numbers of
Numbers of Firms
Employees
Manufacturing
1-5
413 (273)
6-20
537 (486)
21-50
490 (469)
51-100
256 (250)
101-200
122 (120)
200+
233 (231)
Total
2051 (1829)
(sample sizes of incorporated firms only shown in parentheses)
Wholesaling
171 (130)
232 (220)
211 (205)
129 (127)
45
(44)
56
(56)
844 (782)
The sample statistics for each of the samples are presented in Table 2.
Table 2 Sample Statistics
Manufacturing (all firms)
Mean
Standard
error
Current
2051
1.7
0.06
Inventory turnover
1912
37.0
1.98
Total assets turnover
2051
3.5
0.09
Gross profit margin (%)
2051
61.2
0.50
Return on assets (%)
2051
25.9
1.60
Ret Return on equity (%)
2051
31.1
8.07
Debt to assets ratio (%)
2051
63.1
1.43
N = number of firm observations used in each regression
Ratio
N
Wholesaling (all firms)
N
Mean
Standard
error
844
2.3
0.15
844
31.9
3.14
828
3.1
0.10
844
38.8
0.86
844
10.4
1.37
844
4.5
10.1
844
63.0
1.63
Methodology
The study carried out two main testing procedures. First, each of the financial ratios listed
above was regressed separately on employment to see the extent to which firm size
differences explained differences in financial ratios. These equations were of the form:
Dependent variable = a + b(number of employees) + error term
The null hypothesis was that the financial ratios of firms are not explained by the number of
employees.
Next, a series of multiple regressions of different pairs of size groups regressed on all of these
financial ratios together was performed. This was to see whether financial ratios explained the
4
differences in firm size between each group, and allowed the size boundaries currently used
by the ABS to be tested. Firms within a size group classed as small or medium (eg, 1-10, 1120, 21-30 etc) were given a dummy value of one and those classed as large (eg, having 200+
employees) were assigned a dummy value of zero. Firms not in either of the groups being
analysed in a particular regression were given missing values.
To examine whether differences in firm size were related to differences in financial ratios,
regressions of the following type were performed between each of the small or medium firm
size groups and the large firm size group:
Size dummy = a + b1(current ratio) + b2(inventory turnover) + b3(total asset turnover)
+ b4(gross profit margin) + b5(return on assets) + b6(return on equity)
+ b7(debt asset ratio)
The null hypothesis was that the financial ratios do not separate the firms into different size
groups.
Initially, the large firm group comprised those with 200+ employees. Subsequently, other
large firms boundaries of 100, 50 and (for wholesaling) 20 employees were also analysed.
The regression output was also examined to see whether alternative boundaries might be more
clearly suggested by the financial ratios (ie, “natural” size boundaries were sought).
If these equations are statistically significant, it would indicate that the financial ratios
effectively separate the firms into the two size groups represented by the dependent dummy
variable, the more highly significant the regression, the more clearly this discrimination
would be. If the regression equations were significant, then those involving firm group sizes
with larger differences in the number of employees would be expected to be more highly
significant.
The use of multiple regression techniques with a two group dummy dependent variable is
equivalent to a multiple discriminant analysis (Norusis/SPSS, 1990; Osteryoung et al, 1995;
StatSoft Inc, 1997). The adjusted R2 indicates the power of the discriminant function
(Osteryoung et al, 1995). The power of the discriminant function should diminish as the two
groups of firms more closely approach each other in numbers of employees.
“In the two-group case, discriminant function analysis can also be thought of as (and is
analogous to) multiple regression analysis….If we code the two groups in the analysis
as 1 and 2, and use that variable as the dependent variable in a multiple regression
analysis, then we would get results that are analogous to those we would obtain via
Discriminant Analysis….The interpretation of the results of a two-group problem is
straight forward and closely follows the logic of multiple regression” (StatSoft, 1997,
p3).
The coefficients on the independent variables in a two-group linear discriminant analysis are
proportional to the coefficients obtained in a multiple regression (Norusis/SPSS, 1990).
5
Results
Table 3 shows the results of the regressions of each of the financial ratios on the number of
employees. The manufacturing and wholesaling results are shown separately and are the
results for all firms (ie, including incorporated and unincorporated firms). The only equations
found to be statistically significant were for total asset turnover and gross profit margin and
for the manufacturing sector only. (This was also the case when incorporated firms only were
analysed.) Moreover, all of the adjusted R2 are extremely low. This suggests that the number
of employees is a poor explanatory variable for the financial ratios of firms, for both the
manufacturing and wholesaling sectors.
These results are reasonably consistent with those of Osteryoung et al (1995). While
Osteryoung et al (1995) found more equations to be significant, only two equations (which
were for the wholesaling sector) had adjusted R2 of any worth (being 18.6 per cent and 12.5
per cent).
The Australian data shows even less sign of a relationship than the United States data and this
could possibly be due to the much wider range of firm size used in this Australian study. As
Osteryoung et al (1995, pp85-6) acknowledge, their study being confined to publicly traded
companies only included firms that on “almost all definitions…would not be considered
small”.
Table 3 Regression Analysis of Firm Financial Ratios with Number of Employees
Dependent variable = a + b(number of employees) + error term
Dependent
variable
Current ratio
Inventory
turnover
ratio
Total assets
turnover
ratio
Gross profit
margin
Return on
assets
Return on
equity
Debt to
assets ratio
Manufacturing firms
Wholesaling firms
a
(standard
error)
1.682
(0.068)*
37.29
(2.014)*
b
(standard
error)
0.0003
(0.0007)
-0.016
(0.0007)
Adj. R2
(F value)
b
(standard
error)
-0.002
(0.003)
-0.024
(0.0739)
Adj. R2
(F value)
0.0004
(0.157)
0.0079
(13.312)
a
(standard
error)
2.301
(0.150)*
32.20
(3.242)*
3.57
(0.096)*
-0.002
(0.0009)#
0.0021
(5.217)#
3.13
(0.103)*
-0.0006
(0.0023)
-0.0011
(0.065)
0.619
(0.005)*
0.263
(0.016)*
0.312
(0.082)*
0.632
(0.014)*
0.0002
(0.0001)*
-0.0003
(0.0002)
0.0000
(0.0008)
-0.0000
(0.0001)
0.0040
(9.264)*
0.0008
(2.623)
-0.0005
(0.004)
-0.0005
(0.049)
0.391
(0.009)*
0.104
(0.014)*
0.035
(0.104)*
0.627
(0.017)*
-0.0003
(0.0002)
-0.0001
(0.0003)
0.0009
(0.0024)
0.0002
(0.0004)
0.0012
(1.980)
-0.0012
(0.024)
-0.0010
(0.129)
-0.0007
(0.380)
-0.0008
(0.320)
-0.0011
(0.108)
* = significant at 1% level
# = significant at 5% level
6
The results of the multiple regressions using size-based dummy variables are presented in
Table 4. The results presented are for all firms, although the sample of incorporated firms
showed a similar pattern of results. Initially these regressions compared large firms employing
200+ persons with smaller size groups.
The regressions for manufacturing firms suggest that the 200+ employees threshold is too
high a threshold for large firms. The broad pattern of the results suggests that firms in the
manufacturing sector employing up to 80 are different from those with higher employment
levels. This was again revealed when the threshold for large firms was reduced to 100, the
conventional statistical divide between small and large firms in the manufacturing sector that
has been used in Australia. All of the individual employee size groups up to 80 employees,
when regressed against firms employing more than 100, were significant at the 5% level, with
all but the 51-60 employees range also being significant at the 1% level. The multiple
regressions were then performed on a large/non-large divide of 80 employees, and the
adjusted R2 and the significances of the F-values were virtually the same as where 100
employees was the boundary.
The adjusted R2 for the regressions were generally below 0.1 for the regressions where 200
was the threshold for large firms. The adjusted R2 tended to be higher when the threshold for
large was reduced to 100.
The regressions for the wholesale sector cannot be interpreted in a meaningful way. This
appears to confirm the earlier evidence in this study indicating the absence of any statistical
relationship between financial ratios and firm size in the Australian wholesale sector.
Table 4 Regression Results Comparing the Financial Ratios of Different Firm Size Groups
with Large Firms (200+employees)
Size dummy = a + b1(current ratio) + b2(inventory turnover) + b3(total asset turnover)
+ b4(gross profit margin) + b5(return on assets) + b6(return on equity)
+ b7(debt asset ratio)
Firm Size Group
(employees)
1-10
11-20
21-30
31-40
41-50
51-60
61-70
71-80
81-90
91-100
101-125
126-150
151-175
176-200
Manufacturing
Adjusted R2
F ratio
0.0008
1.097
0.0326
3.494*
0.0279
2.810*
0.0567
4.384*
0.1270
8.107*
0.0301
2.390#
0.0551
3.417*
0.0365
2.501#
0.0056
1.214
-0.0029
0.891
0.0235
0.0593
0.0362
2.428#
0.0093
1.337
0.0645
3.482*
Wholesaling
Adjusted R2
-0.0199
-0.0282
-0.0345
-0.0126
0.0844
0.0761
0.1933
0.0940
0.0291
-0.0315
0.1347
0.1289
0.2271
0.2137
F ratio
0.090
0.319
1.705
0.781
2.475#
2.118#
3.876*
2.216#
1.321
0.703
2.535#
2.459
3.602*
3.524*
* = significant at 1% level
# = significant at 5% level
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Conclusions
The results obtained from this Australian data are broadly consistent with those of Osteryoung
et al (1995) for US data. They suggest that although financial ratios are not good indicators of
firm size, the divide of 100 employees between small and not small that is applied to some
Australian statistical collections in the manufacturing sector may not be too far from the mark.
No guidance is provided for the wholesale sector statistical collections.
Despite its limited findings in terms of delineating firms of different size, this study has been
important for a number of reasons. It has tested for size related financial relationships on
Australian data and has employed a large database to avoid the possibility of any serial
correlation. Moreover, it has been able to apply the methodology used to a much wider range
of firm sizes than previous international research on this issue.
This study has found the relationships to be somewhat weaker that those of Osteryoung et al
(1995) and this may be because of the much wider range of firm sizes studied.
Caution should be exercised in interpreting the findings because of the low overall
explanatory power of these financial ratios on business size differentials. Only a weak link
appears to exist between firm size and firm financial ratios. This suggests that other firm
characteristics might be more useful in classifying business by size.
Acknowledgements
The authors wish to thank ABS colleagues Clem Tozer and Bill Pattinson for their invaluable
help and comments on this research.
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The views expressed in this paper are those of the authors and do not necessarily represent
those of the Australian Bureau of Statistics. Where quoted or used, they should be attributed
clearly to the authors
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