Dec 23, 1999 - Report Prepared for the Office of Small Business by. Melbourne ..... Our starting point, consistent with our overall research program in the area of business ... workforce, its management, and what might be called its 'strategic.
EXPLORING SME PERFORMANCE IN AUSTRALIA: AN ANALYSIS OF THE BUSINESS LONGITUDINAL SURVEY
23 December 1999
Report Prepared for the Office of Small Business by Melbourne Institute of Applied Economic and Social Research, University of Melbourne
Authors: Michael Harris, Mark Rogers and Yi-ping Tseng
Contents 1.
INTRODUCTION .....................................................................................................................4 1.1.
The Business Longitudinal Survey (BLS) ....................................................................................... 4
1.2.
Research using the BLS .................................................................................................................... 5
1.3.
Firm performance and firm characteristics.................................................................................... 6
1.4.
Understanding firm performance .................................................................................................... 7
2.
MEASURES OF FIRM PERFORMANCE ...........................................................................10 2.1.
Profitability ...................................................................................................................................... 10
2.1.1. 2.1.2.
2.2.
Productivity...................................................................................................................................... 15
2.2.1. 2.2.2.
2.3.
Productivity Performance Measures..........................................................................................................15 Initial Evidence on Productivity................................................................................................................15
Innovation ........................................................................................................................................ 17
2.3.1. 2.3.2.
3.
Two Profitability Performance Measures..................................................................................................10 Initial Evidence on Profits.........................................................................................................................11
Innovation Performance Measures ............................................................................................................17 Initial Evidence on Innovation ..................................................................................................................18
FIRM CHARACTERISTICS..................................................................................................21 3.1.
Key business characteristics ........................................................................................................... 21
3.1.1. 3.1.2.
Firm Age ...................................................................................................................................................21 Family Business ........................................................................................................................................22
3.2.
Management characteristics........................................................................................................... 24
3.3.
Management methods ..................................................................................................................... 26
3.4.
Industrial Relations Issues.............................................................................................................. 27
3.5.
Use of Government Programs ........................................................................................................ 30
4.
EXPLORING INNOVATION.................................................................................................32 4.1.
Key business characteristics ........................................................................................................... 32
4.2.
Management characteristics........................................................................................................... 34
4.3.
Management methods ..................................................................................................................... 35
4.4.
Industrial relations .......................................................................................................................... 37
4.5.
Government programs.................................................................................................................... 39
4.6.
Conclusions ...................................................................................................................................... 40
5.
EXPLORING LABOUR PRODUCTIVITY ...........................................................................42 5.1.
Key Business Characteristics.......................................................................................................... 43
5.2.
Management characteristics........................................................................................................... 46
5.3.
Management Methods..................................................................................................................... 47
5.4.
Industrial Relations......................................................................................................................... 49
5.5.
Government programs.................................................................................................................... 50
5.6.
Conclusions ...................................................................................................................................... 52
Appendix to Chapter 5............................................................................................................................... 54
6.
EXPLORING PROFITABILITY ...........................................................................................58 2
6.1.
Key Business Characteristics.......................................................................................................... 58
6.2.
Management Characteristics.......................................................................................................... 59
6.3.
Management Methods..................................................................................................................... 62
6.4.
Industrial Relations......................................................................................................................... 64
6.5.
Government Programs.................................................................................................................... 66
6.6.
Conclusions ...................................................................................................................................... 67
7.
CONCLUSIONS......................................................................................................................71
8.
REFERENCES........................................................................................................................74
9.
TECHNICAL APPENDIX......................................................................................................75 9.1.
Definitions of performance variables............................................................................................. 75
9.2.
Definitions of firm characteristics variables ................................................................................. 76
9.3.
Survey design ................................................................................................................................... 77
9.4.
Weights and population estimates ................................................................................................. 77
9.5.
Distributions and measures of central tendency........................................................................... 79
3
1.
INTRODUCTION Small and medium enterprises (SMEs) are a significant sector of the Australian economy. The sector accounts for around half of total employment in the economy, and for a disproportionate degree of employment growth.1 Moreover, much of the potential for economic growth in emerging sectors of the economy will be represented in smaller and younger businesses, some of which will become large businesses in the future. The small and medium business sector has often been thought to have particular characteristics that make it distinctive in terms of understanding how it works, and designing policies to maximise its potential. Particular questions that arise about SMEs include: •
What are the particular barriers SMEs face in starting up? In achieving their growth potential?
•
Which are likely to be the high growth industries/enterprises?
•
What relationships exist between certain business characteristics (e.g. whether the business continually innovates; whether it seeks export markets) and that business’s performance over time?
•
What are the most appropriate roles for government policy?
•
What is known about survival rates for SMEs and what is the policy significance of this?
These issues are relatively under-researched. The collection of the data in the ABS Business Longitudinal Survey (BLS), also known as the Growth and Performance Survey (GAPS), is a valuable step forward in improving our knowledge of the sector. This report uses the BLS data to explore SME performance.
1.1.
The Business Longitudinal Survey (BLS) The BLS is conducted by the Australian Bureau of Statistics, and is sponsored by the Office of Small Business.2 It contains data primarily on SMEs, defined as firms of up to 200 employees. Some supplementary data is collected on large firms for comparative purposes, but these are under-sampled
1
Department of Workplace Relations and Small Business (1998). Description of the survey, the data, and preliminary analyses using the data, can be found in McCann and Tozer (undated), as well as Industry Commission/Department of Industry, Science and Tourism (1997). Some preliminary analysis of firm performance issues using these data can be found in Rogers (1998; 1999). 2
4
compared to SMEs. In the first year of the survey, the sample was about 8700, falling to around 5600 in later years. The BLS is not a random sample of Australian businesses, even of SMEs, and it is therefore not representative of Australian business overall. Particularly, certain industry categories were excluded (including for example agriculture; electricity gas and water; government administration and defence; communications; and education). However, the remaining ‘included firms’— the pool from which the samples were drawn—account for around 60% of total employment. In subsequent years, there was also an over-sampling of particular types of firms (exporters, innovators, and firms exhibiting above average growth), as well as a ‘random sample’ component. The BLS also excludes any firms with no actual employees.
1.2.
Research using the BLS Initial investigations of the BLS database, using the first year’s data, have noted that ‘growing firms’—those reporting sales growth in recent years— tended to have some common characteristics, such as: •
Being younger firms,
•
Being involved in franchising,
•
Incurring expenditures in innovation.
Also, research on innovation has found differences between small and large firms in terms of how they approach innovation and its impact on them. Furthermore, a positive correlation was found between innovative activity and sales growth. The longitudinal nature of the data has enabled presentation of numbers over time, as well as simple cross-section analysis. ABS Catalogue 8141.0 (1997-98) presents figures on employment change and changes in business income between 1994-95 and 1997-98. A ‘stylised fact’ on employment change over the three years is that most firms do not increase or decrease their workforce consistently in each year. Most change in just one year, some in two of the years, almost none in all three. As for business income, half the firms experienced rising income over the three years (comparing start point to end point), about one fifth stayed about the same, and the rest experienced a decline.
5
1.3.
Firm performance and firm characteristics The first part of this report (chapters 2 and 3) looks at the performance and characteristics of Australian businesses in 1997. Firm performance measures are considered in Chapter 2, and firm characteristics in Chapter 3. In both cases the analysis presents population estimates about SMEs for the various variables described below. A ‘population estimate’ is our best guess at the characteristics of all firms in Australia. These estimates are based on using the ‘weights’ provided by the ABS for the BLS survey. Chapters 2 and 3, although focussed on SMEs, also provide statistics on larger businesses. This allows a direct comparison of the performance measures and characteristics of SMEs and larger firms. Firm Performance Measures This report investigates several aspects of firm performance. Our starting point, consistent with our overall research program in the area of business enterprise performance, is to classify three different classes of performance measure, namely, profitability, productivity and innovation. While we view these as inter-related aspects of firm performance, we initially proceed by regarding them independently. Firm Characteristics Our second step is to identify a series of firm characteristics, to do with its workforce, its management, and what might be called its ‘strategic operations’. These are characteristics that we will wish to investigate in terms of their possible impacts on business performance. These characteristics include: •
Whether the firm exports;
•
The education level of its key decision maker (where there is one such person);
•
The degree of union coverage of the workforce;
•
The main forms of award/agreement/contract covering staff;
•
Whether the business has a formal business plan;
•
Whether it benchmarks itself against other firms;
•
Whether the business utilises any government business programs.
6
Firm Size Categories Having identified these performance measures and firm characteristics, we then focus our attention on how firm size interacts with firm performance (Chapter 2) and the identified list of firm characteristics (Chapter 3). We follow convention by using number of employees as the determinant of an enterprise’s size. For Chapters 2 and 3 we primarily employ the following standard categories: small enterprises (1-19 employees); medium enterprises (20-199); and large enterprises (200+). We have also conducted a more detailed breakdown by size principally for use in the chapters examining relationships between characteristics and performance measures. Here we divide small business into two groups (micro businesses of 1 to 4 employees, other small businesses with 5 to 19 employees), and similarly classify two ‘medium firm’ groups (smaller medium businesses of 20-99 employees, and larger medium businesses of 100-199 employees). In the bi-variate analysis in these later chapters, large firms (200+) are excluded. Table 1.1
Firm Size Categories by Employees Small (1-19)
Micro (1-4) Other Small (5-19)
Medium (20-199)
Used mainly for population estimates (Chapters 2 and 3).
Smaller Medium (20-99) Larger Medium (100-199)
Large (200+)
1.4.
Large (200+)
Used mainly for size disaggregation in the bivariate analysis (Chapters 4-6).
Not used in bi-variate analysis.
Understanding firm performance The second part of this report (Chapters 4, 5, and 6) investigates how these characteristics are associated with firm performance. These chapters seek to analyse the relationships, or associations, between the various business characteristics and the measures of performance. The underlying causes of firm performance are, needless to say, complex. Businesses operate in a constantly changing environment and have to make a vast range of decisions concerning strategy, human resources, investment, pricing and the like. 7
External conditions, internal decisions and the characteristics of business all interact to create a performance outcome. Hence, any expectation of a simple set of explanations for business performance is unfounded. Instead, our aim here is to explore some of the major characteristics across a range of businesses. This process often creates more questions than it answers, but it is the starting point for a greater understanding of SME performance. Innovation and Performance Our innovation measure focuses on whether firms have successfully innovated, as opposed to whether they have engaged in innovative activity per se (such as R&D spending). Chapter 4 presents bivariate analysis of the association between the various firm characteristics and their success at innovating. Performance Groups (Profitability and Productivity) In subsequent chapters, we classify SME performance by comparing each business to others in its specific industry (defined at the two digit ANZSIC level). Thus, we use our profitability and productivity measures to allocate firms within each industry to one of three ‘performance groups’: •
High performers (in the top 30% for that industry based on a given measure);
•
Medium performers (in the mid 40% for that industry);
•
Low performers (in the bottom 30% for that industry).
Then, we undertake bivariate analysis using our characteristics variables, to see whether firms associated with particular characteristics appear to have higher or lower representation in any of these particular performance groups. Caution in interpretation is advised here. When we find a ‘relationship’ between two variables, this does not prove any causality. For example, we cannot state that exporting causes businesses to be more innovative, based on any correlation found between exporting firms and innovative firms. All we can say is that a relationship exists; the underlying nature of the relationship is not apparent. Of course, this type of analysis is intended to stimulate thinking on the mechanisms behind the relationships. Analysing SMEs brings specific issues to the fore that need careful attention. We pay particular attention to firm-size issues because a number of the business characteristics and performance measures that we are interested in are correlated with firm size (as shall be shown in the next two chapters). The 8
legal status of a business is also important, as it—particularly whether the business is incorporated or not—affects the accounting procedure used in ways that would affect reported profitability.3 Moreover, in contrast to larger firms (as analysed in Dawkins, Harris and Kells 1999, for example), small enterprises have less ability to influence their profits, by virtue of having less market power. This is likely to influence the strength of any relationships we reveal in the chapter on profitability. Work to Follow. The cross-tab analysis described above is potentially interesting and indicative, but it will not pick up any more detailed dynamic interactions since it only compares data on these variables within a year. In most cases the likely relationship between a firm characteristic (for example, is the business using any government programs?) and its performance is likely to span at least several years. We plan to follow up the work reported here using 1997 data from the BLS, with more sophisticated analyses using the full four-year data set.
3
For small incorporated businesses, there is a tax advantage for directors in receiving ‘surpluses’ as salaries or in other forms that are classified on the cost side of the ledger, rather than as earned profits. As a result, reported profits for such firms will understate actual firm profitability. Thus we divide our sample in the later profitability analysis to separate incorporated and unincorporated businesses. 9
2. MEASURES OF FIRM PERFORMANCE This chapter will outline the various measures of firm performance we will be using to evaluate SMEs, and present a preliminary empirical examination of those measures. As part of a broader research agenda, the Melbourne Institute is interested in three key aspects of performance, these being profitability, productivity and innovation. While the long-run ambition is to investigate the linkages between these three aspects, the first step is to consider their influences and determinants separately. In this chapter, the measures are defined and presented for Australian SMEs based on the BLS data for 1997. Breakdowns by firm size and by industry are also presented and discussed. It is important to recognise that the point-in-time measures presented here are always ‘conditional’ in nature. Economic conditions, and in particular the stage of the business cycle, will influence overall profitability. The stage of business cycle may also affect profitability differently in different industries, meaning that the general numbers we report may well be above or below trend. Similarly, the industry and size comparisons we draw from the 1997 data may hide some additional inter-temporal issues about inter-industry performance. Other time-specific influences—such as changes to the policy environment—will also have effects on numbers that only cover a short time period. Some comparisons between 1995 and 1997 numbers will be reported but some more useful analyses will be presented in subsequent work using the panel of firms over four years. However, this panel will not be long enough to be able to identify and account for, for example, the state of the business cycle.
2.1.
Profitability 2.1.1. Two Profitability Performance Measures The first type of performance measure we will investigate for SMEs is firm profitability. What tends to be important when looking at firm profitability, especially for purposes of comparison, is some relative measure of profits. These relative measures match a dollar value with some measure of the firm’s size or activity. The two main forms of ‘profitability ratio’ we will examine are the EBDIT margin, and the Return on Assets (ROA). EBDIT Margin The EBDIT margin takes as its numerator the firm’s earnings before depreciation, interest and tax, as recorded in its profit and loss statement. This dollar figure can then be compared to a measure of firm activity, typically its total sales revenue or total income. (Sales and income will differ 10
by whether any ‘other income’ is earned by the firm, but for most SMEs ‘other income’ is insignificant, and thus sales and income are highly correlated. On this basis, we confine our attention to EBDIT/sales.4) The ratio that results, the EBDIT margin, is a useful indicator of how much per dollar of sales accrues to the firm’s gross profits. Return On Assets The Return on Assets (ROA) measure is also a ratio, and it also has EBDIT in the numerator. Now instead of looking at firm activity to provide a denominator, we examine a measure more closely related to a financial aspect of the firm’s size, namely, its underlying asset base. Thus, comparing two firms with similar values for EBDIT in dollar terms, the one with fewer assets is more profitable by this measure. One difficulty that faces the user of this measure is to account for recent organisational changes in companies, particularly the impetus to remove some ‘hard capital’ items from the firm balance sheet and instead to lease rather than own these items. This, all other things equal, reduces the asset base of a given firm, and can make comparisons across time and between firms misleading. Fortunately, the BLS data includes information on leased items, so adjustments can be made for this by adjusting the EBDIT terms for leasing expenses. This adjustment has been made for the ratios reported here. The adjustment method uses leasing expenses to impute an ‘asset value’ for leased assets. This imputed value is then added to the (owned) assets recorded in the balance sheet to provide an adjusted asset value for the denominator in the ROA measure. The end result is an ‘adjusted’ ROA measure that treats the leased items as though they were owned by the firm. 2.1.2. Initial Evidence on Profits Generalising from the firms in the survey, based on the ABS weights which enable inferences to be drawn about the population from the sample, the median profit rate for Australian businesses in 1997 as indicated by the EBDIT margin is around 9% (see Table 2.1). By industry, manufacturing, wholesaling and retailing have median margins clearly below this (just below 8%, 5% and 7% respectively); transport/storage, mining and recreational/cultural services are between 9% and 11%; and the other three industries—construction, finance and property/business services—have margins of 11% or higher.
4
The exception is the finance sector, and in particular the larger firms. Much of the income earned by financial institutions is classified as ‘other income’ rather than sales, so the size comparisons reported for this industry are consequently a little misleading. 11
Table 2.1
Overall profitability performance
Industry
EBDIT margin
ROA
9.2
15.6
10.6 7.6 13 4.6 6.6 11.1 19 10 13.7
15.3 15.3 39.1 11.9 13.5 18 13.7 14.8 16.7
Aggregate Mining Manufacturing Construction Wholesaling Retailing Transport and Storage Finance Recreation, Cultural and Personal Services Property and Business Services
The median return on assets is higher than for the EBDIT margin, at just under 16%. The industry breakdown does not resemble that for margins: mining and manufacturing are close to the aggregate median, while wholesaling, retailing, finance and recreational/cultural services have median returns not far below the aggregate median. Transport/storage and property/business services are a little above the aggregate median, and construction is highest by a wide margin at close to 40%.
Table 2.2
EBDIT margins (median) by firm size and industry Firm size (total employees) Small (1-to-19) Medium (20-to-199)
Industry
Large (200+)
Mining
10.6
8.0
16
Manufacturing Construction Wholesaling Retailing Transport/Storage Finance Rec/Cult/Pers. Services Property and Bus. Services
8.5 13.2 4.8 6.8 11.1 22.3 11.5 13.9
4.9 5.2 2.9 5.7 8.9 0.1 -2.0 9.2
8.9 8.2 4.2 6.3 9.3 -608.8 8.8 8.3
If we compare median EBDIT margins by firm size across industries (see Table 2.2), we find in general that for most industries there is a ‘U-shape’ effect, with median margins being higher for small and large enterprises than for medium sized ones. For no industry did medium sized firms have the highest median margin. (The notable outlier in this table is the margin for large firms in the finance industry. We regard these firms as tangibly different in that much of their earnings, especially for larger enterprises, is classified as ‘other income’ which is excluded from our measure.)
12
Breaking down the size categories into the more disaggregated set described in Chapter 1, we find the following (these results are not shown in the table above):
Table 2.3
•
Mining and related firms of all but one size category show median margins in excess of 10% (with firms in the 20-to-99 employee group, smaller medium, having a median margin greater than 20%), the exception being a negative median margin for the larger medium category of firms.
•
Manufacturing businesses show margins in the range 5-10%, with the micro and large firm categories recording the highest margins.
•
Construction and wholesaling enterprises exhibit margins in the range of 3-8%, except for micro firms where the margin is close to 12%. In construction, small businesses perform best, while in wholesaling, small and large businesses perform similarly.
•
For retailing firms, margins cluster in the 5-7% range, except for a 2% margin in the larger medium enterprise category.
•
Transport and storage enterprises in the other small categories have median margins in the range 5-9%, while the micro firms have a median margin of 15%.
•
Performance in finance businesses varies dramatically, with small firms doing spectacularly, medium firms poorly and large firms ‘catastrophically’. (Reasons why this sector’s performance may not be accurately captured by this measure have already been reported.)
•
The median margins for recreational and personal service firms range from a negative 2% for smaller medium firms, 16% for micro firms, and between 5% and 9% in the other size categories.
ROA (median) by firm size and industry
Industry
Firm size (total employees) Small (1-to-19) Medium (20-to-199)
Large (200+)
Mining
16.6
12.0
11.0
Manufacturing Construction Wholesaling Retailing Transport/Storage Finance Rec/Cult/Pers. Services Property and Bus. Services
16.2 39.6 12.4 13.5 18.0 15.5 16.2 16.7
11.7 15.4 10.8 12.6 13.9 0.9 5.6 19.4
13.5 12.4 8.6 16.4 12.7 -1.6 14.8 19.8
13
How does the information on margins compare to the ROA measure? Again, we show in Table 2.3 the median ROA figures across industries and by firm size. We find fewer ‘U-shapes’ (only for manufacturing, retailing and recreational services), and instead find more negative correlations between firm size and profitability according to this measure. Only property and business services defies the pattern with a positive correlation. •
Mining and related firms of all but one size category show median ROA in excess of 11% (with firms in the smaller medium group having a median margin greater than 20%, and micro firms around 37%), the exception being a low median ROA (under 2%) for the larger medium category of firms.
•
Manufacturing businesses show returns in the range 10-20%, with the smallest and largest firm categories (micro and large) recording the highest margins.
•
Construction businesses have an outstanding median return for small firms, and respectable returns in the other categories.
•
Wholesaling enterprises exhibit returns declining with firm size, but the relatively strongest performance is in the medium sized category.
•
For retailing firms, returns cluster in the 12-16% range, except for a 7.5% ROA in the larger medium business category.
•
Transport and storage enterprises in the other small enterprises categories have returns in the range 12-14%, while the smallest firms have a median ROA of 18%.
•
For communication and finance businesses the high ROA for small firms contrasts with the poor performance of both medium and large businesses in that industry (as was found for EBDIT margins).
•
The median returns for recreational and personal service firms range from almost 6% for smaller medium firms, 17.5% for micro firms, and between 13% and 15% in the other size categories.
•
As with EBDIT margins, the ROAs for property and business services are amongst the highest for small, medium and large firms.
According to data on both margins and returns on assets, the mining industry and the property and business services industry appear to be the high performers in 1997 although it is more apparent when using margins as the performance measure than using ROA. There is a distinct clustering of high median margins and returns in the micro firm category (1-4 employees). 14
If any size group appears to under-perform across industries, it is the larger medium group. Comparing EBDIT margins to ROA in general, the median returns on assets are higher than margins in almost all categories. There also seems to be some ‘compression’ in the returns relative to the margins, with outcomes being a little closer to each other. Similar calculations for 1995 (the year in which this survey began) show that the story does not change remarkably. Mining firms perform especially well on both profit measures, with the firms in the larger medium category doing particularly well (unlike 1997) and the other small businesses recording spectacular median profit performances (76% for the EBDIT margin, 320% for ROA). As before, the smallest firms across the industry groups tend to perform as well as or better than most of their larger counterparts.
2.2.
Productivity 2.2.1. Productivity Performance Measures Productivity measures are most easily computed in a ‘factor specific’ way, for example, labour productivity. This provides a measure of the labour intensity used in a production process: more specifically, it is a measure of averaged value-added per worker. For firms with similar production processes, labour productivity provides a comparative measure of how efficiently the labour inputs are used. The performance measure we will focus on here will primarily be labour productivity, and it is important to note that high or low figures for labour productivity do not automatically imply ‘good’ or ‘bad’ economic performance. Different industries will have intrinsically different mixes of capital and labour, and a person working with complex machinery is likely to add considerably more value to that firm’s output (be ‘more productive’) than someone in the labour-intensive service sector. Note that unlike the profit measures which are interpretable as rates of return (i.e. as percentages), the labour productivity figures are shown in thousands of dollars per effective unit of labour. 2.2.2. Initial Evidence on Productivity Looking the median labour productivity by industries (Table 2.4), the mining industry has the highest measured labour productivity, reflecting (arguably) the highly capital intensive nature of the industry. Note that the figure here can be read as $83,200 of value added per (effective) full time employee. To the extent that the industry can earn ‘scarcity rents’ over competitive profits due to resource scarcity, these may also be reflected in these higher figures. (This is consistent with the high profitability found in the mining sector.) Construction, wholesaling, and transport and storage, tend to be the next 15
highest industry agglomerations, with manufacturing, property, finance and retailing following in approximate descending order, with the other service industry (recreation and personal) recording the lowest value. Table 2.4
Labour productivity (median) by industry
Industry
Labour Productivity
Aggregate (mean)
78.6
Aggregate (median) Mining Manufacturing Construction Wholesaling Retailing Transport and Storage Finance Recreation, Cultural and Personal Services Property and Business Services
44.7 83.2 48.3 50.9 60.7 37.3 61 57 31.6 46.4
Table 2.5 shows a breakdown of the median values for labour productivity by both industry and firm size. One important observation is that measured labour productivity is higher in larger firms. This is in contrast to the profit figures where, as we saw previously, observed profit rates tended to be higher for smaller firms.
Table 2.5
Productivity (median) by firm size and industry Firm size (total employees) Small (1-to-19) Medium (20-to-199)
Industry
Large (200+)
Mining
83.2
157.0
267.0
Manufacturing Construction Wholesaling Retailing Transport/Storage Finance Rec/Cult/Pers. Services Property and Bus. Services
44.3 50.1 56.3 37.0 57.5 55.0 31.6 46.0
65.2 76.0 85.1 55.8 81.1 125.1 28.1 70.7
92.4 94.1 82.3 59.2 130.8 20.8 86.9 88.4
The substantial differences in labour productivity between firm sizes is linked to the fact that larger firms have substantially higher capital-labour ratios. That is, large firms tend to combine large amounts of machines, plant, etc with each employee, hence allowing each employee to be more ‘productive’ (at least as measured by our labour productivity measure). Another common way of describing this is to say that large firms are less labour intensive: meaning that (per unit) labour is more productive. This holds, approximately, across industries. For example, in mining we calculate 16
that the median capital-labour ratio for large firms is $2.2 million of capital to each employee. The small firms in the mining sector only have $152,000 per employee. Similar patterns can be found in all industries, as can be seen in Table 2.6. Table 2.6
Capital-labour ratio (median) by firm size and industry Firm size (total employees) Small (1-to-19) Medium (20-to-199)
Industry
Large (200+)
Mining
152.0
414.9
2194.5
Manufacturing Construction Wholesaling Retailing Transport/Storage Finance Rec/Cult/Pers. Services Property and Bus. Services
62.0 36.0 120.0 116.4 128.6 95.4 51.0 63.0
123.8 74.5 201.9 175.3 153.0 1000.8 67.1 85.7
240.7 174.2 195.6 85.4 334.1 3039.9 86.9 128.0
Comparing median labour productivities in 1997 with 1995, we find a mixed story. In the majority of cases we find that labour productivity is lower in 1995. This is to be expected given that we are using current, not real, prices (i.e. we are not adjusting for inflation). However, all mining groups in 1995 have higher labour productivity.
2.3.
Innovation 2.3.1. Innovation Performance Measures There are various indicators of innovative activity in the BLS survey, but it is important to distinguish between inputs to innovation and the associated outcomes. Innovative effort (e.g. R&D spending) is likely to be strongly related to future innovation outcomes and so if we were to think of their being ‘innovative firms’ and ‘non-innovative firms’ then it would not much matter if we assessed firms on their effort or on the results that arose from that effort. However, there is evidence that the effectiveness of R&D does vary across firms. In view of this it seems appropriate to focus on whether firms actually innovate. Thus the primary performance indicator we will focus on regarding innovation is an ‘outcome’ measure, namely, has the firm introduced any new products or processes in the last year? This is, admittedly, not a perfect measure of innovative activity. It provides no information on either the quantity of innovations undertaken during the year, nor of the ‘quality’ of the innovations in terms of affecting the enterprise’s productivity or profits. Thus, when looking at how innovative particular industry or firm size categories were during 1997, we cannot assess how numerous or significant such innovations were across firms or 17
industries. But we do glean information on whether (and where) a desire to innovate is being translated into actual changes in what a business actually produces (and/or how it produces it). The appropriate way to interpret the numbers generated is that they are percentages, indicating the proportion of firms in a given category that have introduced some form of innovation (new product/process) in the year under consideration. 2.3.2. Initial Evidence on Innovation Table 2.7
Innovation by industry
Industry
% of firms innovating
Mining Manufacturing Construction Wholesaling Retailing Transport and Storage Finance Recreation, Cultural and Personal Services Property and Business Services
2 12.8 9.5 25.3 13.5 6.1 10.9 12.8 15.3
According to Table 2.7, relatively few firms are innovative in the sense of introducing what they perceive as a distinctly new product or process. Mining is particularly low in terms of the percentage of firms who innovate by this criterion. Transport and storage is also a relative under-performer (although that story changes somewhat when firm size categories are considered separately; see below) as are finance and construction although less so. The rest of the industries, with the notable exception of wholesaling, fall into a narrow band between around 12-15% of firms being innovative. The data in Table 2.7 shows that most firms are not innovative, at least in the year we examine. This results coincides with more general evidence that suggests that many firms are sporadic innovators (i.e. they only innovate once every few years). Hence, a snapshot of one year of data shows low overall innovativeness. Future work will investigate which firms are continuously innovative (i.e. in every year), but this analysis requires use of a balanced panel of Australian firms. However, the level of innovativeness does vary dramatically across firm sizes as shown in Table 2.8. It is to these differences we now turn.
18
Table 2.8
Innovation by industry and firm size (% of firms) Firm size (total employees) Small (1-to-19) Medium (20-to-199)
Industry Mining Manufacturing Construction Wholesaling Retailing Transport/Storage Communication?/Finance Rec/Cult/Pers. Services Property and Bus. Services
Large (200+)
0.0
24.0
1.4
10.5 9.1 24.1 12.0 4.9 9.7 12.9 14.9
26.9 30.4 35.9 39.3 22.8 31.5 11.5 24.5
29.7 10.9 43.2 14.9 31.7 60.5 9.5 29.2
The data in Table 2.8 show that different firm size groups can vary substantially in the proportion of innovators. A summary of the results is: •
Few firms in the mining industry were innovative in this sense, except for the smaller medium category where over a third had introduced some form of innovation.
•
The proportion of innovative manufacturing firms was in the low-toaverage range, with around 30% being the highest figure (for the large firm category).
•
Wholesaling was a strong industry performer in introducing innovations. A quarter of small firms, a third of the medium firms, and close to half the large firms, introduced innovations.
•
Retailing was less innovative, with respectable performances in the smaller medium and larger medium groups, but with under a fifth of firms innovating in the other three sub-groups.
•
Transport/storage industries showed respectable rates of innovation in all but micro businesses, and a high level of innovativeness in larger medium firms.
•
Innovation in the finance industry increased markedly with firm size.
•
Recreational service industries were also strongest in the larger medium firms, and comparatively strong in the micro businesses, but underperformed elsewhere.
•
Property and business services also increased with size but less dramatically than did the finance industry.
19
Examining the firm size groups, the idea that larger firms would most likely be more innovative is somewhat borne out, but the pattern is not consistent. Comparative rates of innovation tended to be lower in the smallest firms, but the size category that recorded the most impressive innovation rates in most industries was the larger medium (100-199 employees) group. The size groups on either side, smaller medium and large (20-99 and 200+ employees) recorded similarly respectable performances, with one or other usually being in at least ‘second place’ in each industry. Comparing 1997 to 1995, we find that in the earlier year, rates of innovation were rather lower than in the later year. This time, manufacturing was the top performer, and mining was lowest in each size category.
20
3. FIRM CHARACTERISTICS In subsequent chapters we will be examining firm performance as it is associated with certain characteristics of firms. These characteristics are classified as: •
key business characteristics,
•
management characteristics,
•
management methods,
•
industrial relations, and
•
use of government programs.
As a precursor to this analysis, this chapter summarises these characteristics. We will be particularly interested in how these characteristics vary over different firm sizes. As before, we mostly report results according to the small/medium/large enterprise classification in Table 1.1, but we do include some more disaggregated breakdowns.
3.1.
Key business characteristics 3.1.1. Firm Age Figure 3.1 shows the age structure of firms, classified in three groups (up to 3 years old, from 3 to 10 years, and over 10). Comparing firms of similar sizes, the relative share of young firms in a given size category declines with increasing firm size. This result also holds for ‘middle-aged’ firms, while older firms showed the opposite pattern. The 3-to-10 year age bracket is, proportionally speaking, dominated by firms of under 100 employees (small and smaller medium), where 40-45% of these firms fit into this category, while firms of 100+ employees have between a fifth and a quarter of their number in that age group. Correspondingly, the oldest firms (10+ years) tend also to be the largest firms: two-thirds or more of the 100+ firms are at least 10 years old. Around half of the firms in each of the two size groups between 5 and 99 employees were over 10 years old, and two-fifths of the smallest firms were this old.
21
Figure 3.1: Firm age by size 80
70
60
per cent
50 Young (0-3) middle-aged (3-10) Old (10+)
40
30
20
10
0 small
medium
large
Firm size
3.1.2. Family Business Another characteristic of many SMEs is that they are family businesses. We can show the pattern of family businesses by firm size by looking at the following figure. Figure 3.2 shows the proportion of family businesses in each size category, leading us to draw the unsurprising conclusion that smaller firms are predominantly family concerns (over 60% of small businesses), and that bigger businesses are less likely to be family businesses. Around two thirds of micro firms are family firms, compared to: •
Just over half the other small group (5-19 employees);
•
Less than a half of smaller medium firms;
•
Around a quarter of larger medium businesses;
•
Well under 20% of large firms.
22
Figure 3.2: Family businesses by firm size 70
60
Per cent
50
40
30
20
10
0 small
medium
large
Firm size
Figure 3.3 shows how exporting activity varies with firm size: a clear correlation exists between the two variables. Only 2% of micro firms export, compared to around 15% in the middle (smaller medium) group and over a third of all firms in each of the largest two categories. This strong firm sizeexport link might be expected given that exporting requires some fixed costs (e.g. finding markets, distributors, overseas business trips) as well there often being economies of scale in shipping goods.
Figure 3.3: Exporters by firm size 45 40 35
per cent
30 25 20 15 10 5 0 small
medium
large
Firm size
23
3.2.
Management characteristics This next set of characteristics concern the characteristics of the major decision maker of the business. The survey results suggest that 62% of businesses have a single, major decision maker. Only if a business responded ‘Yes’ to this question do we have any additional information on the characteristics of the management. This situation obviously means that the information we have is limited to a specific set of businesses. The percentage of businesses with single, major decision maker is relatively constant for SMEs (between 57% and 63%), however, for the largest firms (200+ employees) only 37% report having a single, major decision maker. One obvious issue of interest with such business managers is their highest educational qualification. Table 3.1 describes the educational qualifications of the key decision maker, for those enterprises who had one such person. The table should be read as follows: for a given firm size, what proportion of the sole decision maker had achieved ‘high school’, ‘trade certificate’, ‘tertiary’ qualification as their highest formal qualification? Note that the tertiary category is sub-divided into business-related and ‘other’ qualifications, where business related includes disciplines such as commerce, administration, economics, etc.
Table 3.1
Education level of key decision maker, by firm size
Firm size 1-4 5-19 20-99 100-199 200+
high school 33.0 40.0 27.7 16.5 21.7
Level of educational attainment certificate tertiary 28.3 25.6 21.4 14.5 16.9
24.5 16.9 29.8 32.9 24.0
Tertiary (business related) 14.2 17.5 21.2 36.1 37.4
100 100 100 100 100
The general impression gleaned from this table is that key decision makers in larger businesses tend to have higher qualifications than those in smaller firms. Yet just over a third of the decision makers in the smallest Australian firms have some form of tertiary qualification; for larger firms this increases to around two thirds. The division between ‘business discipline’ and other tertiary qualifications does not exhibit a particular pattern, although the decision makers in larger firms to have a somewhat greater disposition towards business-related degrees.
24
Figure 3.4: Male Decision Maker by Firm Size 98
96
per cent
94
92
90
88
86 small
medium
large
Firm size
Another observation from data on businesses with a single decision maker is that those people are predominantly male, across all firm sizes. Figure 3.4 shows that the percentage of firms with a male director/proprietor is in excess of 90% for all except the smallest firms (and they are not much lower), and firms in the two largest size categories are more than 95% male-run. Looking at that person’s years of experience of the major decision maker is more complex (see Figure 3.5). The firms where the director/proprietor’s average years of experience are greatest are actually those in the smallest three size categories, above seven years in all cases. The largest enterprises have what seems like the surprising result that their directors have the least experience at the top, less than four years. While this seems counterintuitive, it is likely that many of the smaller enterprises in this category are owneroperated, with the business having been set up by the person running it. It is more likely to be the case in smaller businesses that they will be run by the founder for a long period of time (if the business survives). By contrast, in larger firms people will often be promoted into the top position having had some years of experience in positions near but not at the top in the firm or elsewhere.
25
Figure 3.5 Years experience in top job of key decision maker by firm size 10 8 6 4 2 0 small
3.3.
medium
large
Management methods The BLS survey included various questions that can be considered related to management methods. In this report we focus on three of these questions. These are whether the business has a formal business plan, if networks are used, and if benchmarking is conducted? Figure 3.6 shows the proportion of businesses that use each of these methods by firm size grouping. All of these items correlate positively with firm size. Where less than one fifth of small firms have a business plan, three quarters of the largest ones do. Where a little over one tenth of small firms network, around half the largest firms do; a similar disparity occurs with benchmarking.
26
Figure 3.6: Planning, networking and benchmarking by firm size 80
70
60
50 pe r ce 40 nt
Business Has Business Plan Business Networks Business Benchmarks
30
20
10
0 medium
small
large
Firm size
3.4.
Industrial Relations Issues Several issues concerning labour markets and industrial relations are covered in the data set. These include ‘union coverage’ (the extent of union membership through the workplace), and data on employee pay schemes which examine the nature of the awards, contracts, or agreements governing the individual workplace, and a comparison of the dominant form of employer-employee ‘agreement’. Figure 3.7: Union coverage by firm size
100 90 80 70
per cent
60 None Less than 50% More than 50%
50 40 30 20 10 0 small
medium
large
Firm size
27
Data on the union membership of employees of SMEs reveal an unsurprising picture (Figure 3.7), in that the smallest firms have very low rates of unionisation of their workforces, and across the size categories, larger businesses have consistently higher rates of unionisation (proportionally speaking). More specifically: •
Approximately nine-tenths of micro businesses have no union coverage at all.
•
We also note that, of smaller medium firms, around two-thirds have no union coverage, around one-quarter have less than half the workforce unionised, and less than one-tenth of these firms have coverage of greater than half the workforce.
•
Of larger medium firms, over a third have no union membership, or less than half the workforce being union members, respectively. A quarter of the firms have higher rates of coverage.
•
Less than a quarter of large firms have zero union representation, close to half have minority union membership, and close to a third have majority union membership.
The BLS survey also asks businesses about the number of employees who are employed under various employment contract types. These are classified as: •
Covered by an award only,
•
Registered enterprise agreement,
•
Unregistered enterprise agreement, and
•
Individual contract or agreement.
Using the responses we examined which was the dominant form of payment agreement in each firm. Figure 3.8 shows the result of this by firm size: moving from small to large firms, the share of firms where awards or contracts dominate declines. We also created new variables that classify a business according to whether it has ‘None’ employees under the specific scheme, ‘Some’ employees, or ‘All’ employees under the scheme. This classification is intended to capture the extreme cases of usage. Results on these are presented in some detail in Tables 3.2-3.5.
28
Figure 3.8: Major form of payment agreement by firm size 40
35
30
Per cent
25 Major payment agreement awards Major payment agreement contracts
20
Major payment agreement unregistered Major payment agreement registered
15
10
5
0 small
medium
large
Firm size
Table 3.2
Use of awards by firm size Employees paid on Federal/State awards
Firm size 1-4 5-19 20-99 100-199 200+
None 46.6 31.4 28.9 40.1 39.8
some 6.8 17.3 32.2 49.3 50.4
all 46.6 51.3 38.9 10.6 9.8
Table 3.2 shows how the use of awards varies across firm size groups. For businesses in the smallest firm size group, the table shows most firms use either no awards or all employees are covered by awards. In contrast, as firm size rises a higher proportion of firms tend to have some, but not all, employees on awards. Table 3.3 Firmsize 1-4 5-19 20-99 100-199 200+
Individual contracts and firm size Employees paid based on individual contract or agreement None some all 66.0 65.6 53.0 23.7 22.2
4.4 13.9 30.3 61.1 66.5
29.6 20.4 16.7 15.2 11.2
Table 3.3 shows the equivalent table for individual contracts. Here, the smallest firms show a similar pattern to awards, but there is a higher proportion in the ‘None’ category. Again, as firm size increase we tend to see a higher proportion of firms in the ‘Some’ category. 29
Table 3.4 Firmsize 1-4 5-19 20-99 100-199 200+
Unregistered agreements and firm size Employees paid based on unregistered enterprise agreement None some All 81.9 87.2 84.4 89.2 89.3
4.0 5.8 8.9 9.4 10.5
14.1 6.9 6.6 1.4 0.2
Tables 3.4 and 3.5 show results for registered and unregistered agreements in force at the business. As can be seen from Table 3.4, very few businesses in each firm size category have unregistered agreements. The highest proportion is for micro firms, with 14% of such businesses having all employees covered by such schemes. Table 3.5 shows that registered agreements are very uncommon across all small businesses. Only in the large firm size group are there over 50% of businesses that have some registered agreements in place. Table 3.5 Firmsize 1-4 5-19 20-99 100-199 200+
3.5.
Registered agreements and firm size (employees paid based on registered enterprise agreement) None Some All 98.0 96.2 93.0 67.1 46.3
1.6 3.6 6.8 32.6 52.8
0.4 0.2 0.2 0.3 0.9
Use of Government Programs The last set of characteristics considered in this report concern use of government programs. The BLS survey asks firms a number of questions concerning use of such programs. Here we construct one overall ‘Yes/No’ variable that signals the use of any government program. We also distinguish between use of AusIndustry, Austrade, EFIC (export finance and insurance program) and employment programs. (The BLS survey also asks questions about the costs and benefits of using R&D, AusIndustry loans and enterprise improvement/development program, but we do not use these data here). The survey results suggest that there is a strong firm size and government program link. Overall, only 4.4% of micro firms use a government program. This compares to 40.0% for the large firm size group. The proportion of businesses using government programs grows steadily with firm size in between these two extremes.
30
Figure 3.9: Government program participation 45
40
35
Per cent
30
25
20
15
10
5
0 small
medium
large
Firm size
Figure 3.10: Use of specific government programs by firm size 30
25
Per cent
20 Ausindustry program Austrade EFIC Employment
15
10
5
0 small
medium
large
Firm size
Figure 3.6 shows the proportions of businesses using the specific government programs. These also tend to be correlated with firm size but not so clearly. Where almost none of the smallest firms participate in Ausindustry, Austrade or EFIC programs, some larger firms do, but participation rates are 10% or lower. Employment programs are more heavily subscribed: nearly 5% of the smallest firms, up to over a quarter of businesses in the medium-size and largest groups participate in these.
31
4. EXPLORING INNOVATION This chapter investigates how the range of firm characteristics discussed in Chapter 3 are related to innovation. Our measure of innovation was detailed in Chapter 2 and classifies firms as either an ‘innovator’ or not an ‘innovator’. This classification is based on the survey question concerning whether a new product, service or process had been introduced by the firm in the last financial year. All of the analysis in this chapter considers businesses up to 200 employees; large businesses are included in the analysis. The method of analysis used in this chapter involves ‘cross tabulating’ the firm characteristic with the innovator classification. This method is explained in detail below using actual examples, but in essence it looks at the relationship between two variables. For example, when looking at export status and innovation, we are interested in assessing whether firms that export are more or less likely to innovate. In this chapter we analyse the firm characteristics in five sub categories: key business characteristics, management characteristics, management methods, labour market factors, and government programs.
4.1.
Key business characteristics Table 4.1 shows a cross tabulation of businesses on the basis of their export status and innovation status. Export status, as discussed Chapter 3, is defined on the basis of whether the business exported any goods or services in 1997. Looking at the Table, the row which has ‘No’ under export status contains the percentages 82.1%, 17.9% and 100%. The first two numbers refer to the proportion of firms that answered ‘No’ and ‘Yes’ to innovation. Therefore, of those firms that did not export 17.9% were innovators. The only way to compare this figure is to look at the equivalent percentages for those firms that did export. This row shows that 35% of firms that exported were also innovators. The substantially higher proportion may suggest that there is some relationship between exporting and innovation.5
Table 4.1
Export status and innovation
Does business export?
No Yes Total
Innovation No
Yes
Total
82.1 65.0 78.6
17.9 35.0 21.4
100 100 100
5
As discussed previously, we can statistically test the ‘association’ using a chi-squared test. In this particular case we find that the relationship is statistically significant. All the cross tabulations shown here are statistically significant at the 1% level. 32
This method of analysing the data is used through out the chapter. At the outset we should note that when we find a ‘relationship’ between two variables this does not prove any causality. For example, we cannot state that exporting causes businesses to be more innovative. All we can say is that a relationship exists; the underlying nature of the relationship is not apparent. Of course, this type of analysis is intended to stimulate thinking on the mechanisms behind the relationships. In the case of exports and innovation we can consider two possible, and distinct, reasons for the relationship. First, businesses that are innovative may also pursue export opportunities. If the innovation represents a new product or service that has potential markets overseas this would be an expected strategy for a business. Second, firms that export may be exposed to more technology, business ideas and competition, all of which may stimulate innovative efforts. Both of these reasons are found in the economic and business literature on innovation and export performance. In reality, it appears likely that both reasons are present. This means that policy measures to stimulate either innovation or exporting by SMEs will lead to a positive feedback between the two: innovation and export performance will jointly rise. Although these explanations may have appeal, we need to be cautious in our interpretations. The results in Chapter 3 show that the probability of exporting increase substantially with firm size (Figure 3.3), as does innovation (Table 2.8). This means that the export-innovation association may be driven largely by firm size-innovation association. We can use the data to investigate this issue: we break the sample into four distinct size groups (micro; other small; smaller medium; and larger medium as per Table 1.1) and again examine the relationship. The results are clear: the exporting-innovation correlation holds clearly and significantly for each of the size groups. Using the cross tabulation method we have also investigated the relationship between age of business and innovation. Intuition might suggest that younger businesses may have higher rates of innovation almost by definition (i.e. a new business set up in the last year would be expected to answer ‘Yes’ to whether it has introduced a new product or service). Equally, younger businesses might be thought of as more dynamic and flexible, which perhaps allows them to innovate. Table 4.2 shows that these type of reasons are supported by the data: businesses younger than three years show a higher proportion of innovators. (This result also holds across the different firm size groups we examined.) The other key business characteristic investigated was whether family owned businesses are any more or less innovative. Here the results show no strong distinction: both family owned and non-family owned SMEs have a proportion of innovators close to the overall average.6
6
More formally, the statistical Chi squared test of the association is not significant at the 1% level. 33
Table 4.2
Age of business and innovation
Age of business in years
3 years and less more than 3 to 10 more than 10 Total
4.2.
Innovation No
Yes
Total
72.3 80.7 78.1 78.6
27.7 19.4 21.9 21.4
100 100 100 100
Management characteristics The ability of a business to innovate is ultimately dependent on a range of factors, including employee skills, financial resources, access to new knowledge, and relationships with customers and suppliers. However, the ability to channel these factors towards innovative effort and outcomes will depend heavily on the ability of management. To investigate these issues we would ideally like comprehensive details of the management team, including past experience, education and strategic outlook. The BLS data allows us to investigate some of these characteristics. One of the questions in the survey asks the business whether there exists a single major decision maker in the business. Chapter 3 has shown that 62% of businesses responded ‘Yes’ to this question. When we look at these businesses against those that responded ‘No’ (i.e. no sole major decision maker exists), we find a small difference in innovative status: 22.3% of businesses with a single, major decision maker in the survey are also innovative, compared to 19.6%.7 Given this, we are reluctant to read to much into this magnitude of difference without further investigation. If the business has a single decision maker, further questions ask about the gender and experience of this decision maker. Overall, the estimates in Chapter 3 suggest that less that 10% of businesses have a female decision maker. When we look at innovative status according to gender of decision maker we find no significant differences. (Breaking this up by firm size, we find there is a significant difference for smaller medium firms, where femalerun firms appear to be more innovative. While female-run firms appear more innovative in other categories also, these effects are not significant.) Table 4.3 shows how the educational level of the sole decision maker is linked to the proportion who have innovated. Firms with tertiary-educated decision makers show higher levels of innovation. Here, once again, it is important to stress that the tables do not necessarily show a causal relationship. In other words, it may be that the businesses with ‘tertiary qualified decision makers’ have other characteristics – such as being larger or having access to better finance – that underlie the relationship (see Table 3.2).
7
We find that this difference is not statistically significant at the 1% level. 34
The other interesting aspect of Table 4.3 is that businesses with high school qualified decision makers tend to have higher rates of innovation than those with a trade certificate. Whether this is a robust finding and, if so, what underlies this relationship, is unclear at this stage. However, the relationship does appear to apply over the different size groups of firms, when the sample is broken up. Table 4.3
Educational level of sole decision maker and innovation
Education of major decision maker
High school Certificate Tertiary Tertiary (business related) Total
Innovation No
Yes
Total
79.2 83.8 73.1 72.7 78.6
20.9 16.3 26.9 27.3 21.4
100 100 100 100 100
Our analysis also looked at the how the experience of the single decision maker influenced the proportion of businesses that reported innovation. Here we found that around 23.6% of businesses with the most experienced decision makers innovated, as compared to a survey proportion of 21.4%. The slightly higher percentage might be expected, if innovation is viewed as a complex process that requires management experience: however, again, the difference is small and may be due to other factors (using a Chi squared test, we find this association is not significant at the 1% level). Looking at the size breakdown, we find one significant effect: for smaller medium businesses, more experience correlates with greater innovativeness.
4.3.
Management methods The characteristics of the management considered above are likely to be only one of the factors linked to innovation. This section considers three elements related to management methods, namely, does the business have a formal business plan, does the business network with other businesses, and is benchmarking conducted? In the case of formal business planning this can be thought of as an indicator of the attention given to planning within the business. Since innovation is directly concerned with developing products, services and processes to raise competitiveness in the future, this may be an important management method. However, we must be aware that the adjective ‘formal’ may fail to reveal the true nature of management planning. Some managers may have detailed and highly effective planning methods that are not expressed as a written, formal plan. This may be especially the case in some SMEs. Despite this, it does seem likely that the process of completing a formal business plan is important to SMEs. Table 4.4 shows the cross tabulation of ‘formal business plan’ with innovation. The results show that, for businesses in the 35
survey, those with a formal business plan are much more likely to be innovators (35.9% compared to 14.3%). Again, it is important to consider whether this association is being driven by firm size. Large SMEs are much more likely to have a formal business plan (see Figure 3.5) and are also more likely to have reported an innovation (see Table 2.8). It may also be, however, that businesses that intend to innovate also realise the need to plan formally. Looking at the different size categories, we find that the relationship holds strongly and significantly across each one. The effect is not firm-size driven. Table 4.4
Use of formal business plan and innovation
Does the business have a formal business plan?
No Yes Total
Innovation No
Yes
Total
85.7 64.1 78.6
14.3 35.9 21.4
100 100 100
We find a similar pattern when we consider businesses that network with other businesses. Table 4.5 shows that businesses that network are also much more likely to innovate. It may be that SMEs use networks in the process of innovation. This is an issue that has been investigated in research in the USA, UK and Italy. These studies tend to suggest that SMEs rely much more on networks with other businesses and public entities than do large firms. In view of this, our results may indicate that networks are important for innovation in Australian SMEs. Again, there is a question of whether this association is largely due to firm size. Larger SMEs show a higher probability of networking (see Figure 3.5), and also report higher levels of innovation (see Table 2.8). Disaggregating by size, we find the relationship between innovation and networking holds clearly across the size groups. Table 4.5
Use of networks and innovation
Does the business network with other businesses?
No Yes Total
Innovation No
Yes
Total
82.9 63.8 78.6
17.1 36.2 21.4
100 100 100
Lastly, Table 4.6 shows the relationships between firms that benchmark and innovation. Of the businesses in the survey that do conduct benchmarking, around 33% also innovated, compared to only 17.7% of those that did not 36
benchmark. The ‘outward looking’ nature of benchmarking is likely to be a factor in this association, since the process of innovation is intimately linked to understanding the external world. This includes knowledge of other processes and products used by other businesses. Benchmarking, therefore, may not only yield benefits in terms of improving (current) businesses processes, but may also raise innovativeness and future performance. The fact that the use of benchmarking increases with firm size (as does the proportion of innovators) is, again, a factor behind these results. However, once more, disaggregating the firms by size reveals that the relationship still holds. Table 4.5
Use of benchmarking and innovation
Does the business benchmark with other businesses?
No Yes Total
4.4.
Innovation
No
Yes
Total
82.3 66.7 78.6
17.7 33.3 21.4
100 100 100
Industrial relations There is a large body of research concerning how industrial relations, especially the role of unions, interacts with the process of innovation. Although we do not review the issues in full here, some simple observations are appropriate. Since innovation may be associated with various changes to working practices and conditions, the role of employee-management relations is important. Poor relations may hamper efforts to change through innovation or, in the limit, stop the management even attempting innovation. In the case of process changes, good relationships between employees and management may well improve the implementation of an innovation. Behind this idea is the fact that innovation is not a simple ‘one-off’ process; instead the actual process of implementation may result in new learning and feedback that alters the nature of the innovation itself. Motivated employees, and good communications within the business, may thus foster the innovation process. Employee-management relations, however, do not only enter in the implementation stage of innovations. As discussed above, innovation relies on the capitalisation on information and ideas from customers, suppliers, employees and other sources. Employees may have a major contribution in this regard as management will, at times, rely on useful information flows via employees, including new ideas on products and processes used in the business. One of the areas investigated by other researchers is the role of unions in business innovation. A traditional view might suggest that unions would 37
attempt to block innovations. This, however, is an outdated view of the role of unions and some authors have suggested unions may in fact boost innovation by improving employee-management information flows. Table 4.6 shows a cross tabulation of union density (the proportion of employees in a union) and innovation. The table shows no consistent story; the highest proportion of innovators is for those businesses with up to 50% of their employees in a union. This result may reflect the fact that a variable such as union density is too limited to explore these issues. That said, there is no evidence that businesses with no employees in unions have higher rates of innovation. Looking at the firm-size subcategories reveals no particular patterns either: in small firms, more unionisation corresponds with less innovation, while in larger medium firms, the most unionised firms display more innovation (albeit at a low significance level). For smaller medium firms, results are mixed. Table 4.6
Unionisation and innovation
Union density (% of workers)
None Up to 50% More than 50% Total
Innovation No
Yes
Total
79.6 74.1 79.1 78.6
20.4 25.9 20.9 21.4
100 100 100 100
The issue of the type of employment contract on offer by the business was also investigated. Ultimately, the aim here is to uncover the possible relationships between the types of contracts and incentives faced by employees and the process of innovation. As a step towards this aim, we have used the survey questions on the numbers of employees covered by State and Federal awards only, registered enterprise agreements, unregistered enterprise agreements, and individual contracts or agreements to construct categories of ‘none’, ‘some’ and ‘all’. Table 4.7 shows a cross tabulation of use of awards and innovation. The results show that businesses in the survey that have ‘Some’ employees on awards report the highest level of innovators. A first observation to make is that larger SMEs tend to be more likely to be in this category (a greater number of employees suggests less chance of all employees being on awards). This may be a factor behind the association shown in Table 4.7. The table also shows that businesses with no employees on awards also have a higher proportion of innovators. This result may be due to industry differences or other factors. Examining the firm-size subcategories shows mixed results.
38
Table 4.7
Use of awards and innovation
Proportion of employees on awards
None Some All Total
Innovation No
Yes
Total
72.8 68.3 80.3 74.5
27.2 31.7 19.7 25.5
100 100 100 100
Table 4.8 shows a similar table, but this time it is based on the use of individual contracts or agreements. Again, we see that those businesses that have ‘Some’ employees with this contracts show the highest proportion of innovators in the survey. However, in this case the businesses that have ‘All’ employees in these contracts show a higher rate of innovation than those that report ‘None’. Table 4.8
Use of individual contracts or agreements and innovation
Proportion of employees on individual contracts
None Some All Total
Innovation No
Yes
Total
78.6 67.6 72.7 74.5
21.4 32.4 27.3 25.5
100 100 100 100
The analysis on the prevalence of registered and unregistered agreements also shows some differences in the proportion of innovators. Concerning the ‘proportion of employees on registered enterprise agreements’, we find that a third of those firms with ‘All’ employees on these are innovators, while a quarter of firms with ‘No’ employees on these reported an innovation. The story is similar for ‘unregistered agreements’; except here those businesses with ‘None’ have a higher reported number of innovators. However, the statistical tests do not find that these results are significant (at the 1% level), so we place little stress on these results and do not present them.
4.5.
Government programs Chapter 3 set out the different types of government programs that we consider in this report. The classification of firms is based on whether they use any government program, an AusIndustry program, an Austrade program, an EFIC program or an employment program. Figure 3.6 shows that the use of these programs is related to firm size: a higher proportion of larger businesses tend to use government programs. This is likely to influence the cross tabulation results shown below.
39
Table 4.9 shows that for firms in the survey that participated in any government program the proportion of innovators was 38.2%. This compares with 17.6% for those firms that used no government program. It is important to stress again that these proportions are based on (unweighted) survey data, and are not population estimates. Again this result may be linked to the firm size issue discussed above, but the size decomposition reveals the result still holds: so it also seems likely that innovative firms may be more aware, and more accepting, of government programs.
Table 4.9
Use of government programs and innovation
Participate in government program
not participate participate in any Total
Innovation No
Yes
Total
82.4 61.8 78.6
17.6 38.2 21.4
100 100 100
An analysis of the link between the use of AusIndustry programs and innovation shows a strong link: 49.5% of businesses that used an AusIndustry program also reported an innovation. Since some of the AusIndustry programs are directly associated with innovation this is not surprising. If does, of course, suggest that the types of businesses using AusIndustry programs may not be representative of Australian business, at least as far as innovation is concerned. Table 4.1 showed the association between exporting and innovation. Given this, it is perhaps not surprising to find a similar pattern between use of Austrade programs. Businesses that used an Austrade program had a 46.8% innovation rate, as compared to 20.2% of non-users. A similar pattern, although not as extreme, was found for participation in EFIC programs. Lastly, we also found an association between participation in an employment program and innovation (36.6% of those using an employment program were innovators).
4.6.
Conclusions The process of innovation is complex and will vary greatly between firms. Almost by definition there is little chance of finding the ‘formula’ for innovation, either by case studies of firms, or by the type of data analysis used here. Instead, the aim here is to provide some general background to the potential determinants of innovation. One of the most striking results is the association between exporting and innovation. Businesses that export appear more likely to innovate. The reasons for this relationship are likely to run both ways. An innovative firm 40
may well identify markets overseas and seek to market its products or services internationally. Equally, exposure to international markets, ideas and knowledge may well act as a spur to innovative activity. In any event, the association between the two suggests that increasing levels of export activity—as has been present in Australia over the last decade—will be associated with higher rates of innovation. As we stressed above, however, it would seem appropriate to control for the size of the business in this association before placing too much emphasis on the results. Considering management methods, we found strong associations between use of formal business plans, networking and benchmarking and innovation. What might explain such an association? The hypothesis that it reflected a ‘business size effect’—that large SMEs tend to innovate more and also have higher rates of use of these management methods—was not borne out when separate size subcategories were examined: for the characteristics mentioned above, correlations held up for micro to medium sized businesses. There are, then, reasons for expecting that the actual methods themselves may boost innovation. With regard to networking and benchmarking, the essential issue is that the process of innovation relies on interacting with the external environment. With regard to industrial relations variables, we find that the presence of unions has no clear cut effect on innovation. Similarly, the role of different types of employment contracts is not easily summarised. We expect that many of the results on these variables are related to business size and industry differences. Neither of these factors is controlled for in the above analysis. Lastly, we find an association between use of government programs and innovation. Again, while it appears that this association may be due to the fact that larger SMEs are both more likely to innovate and to participate in government programs, the firm-size decomposition showed the effects still persist for businesses from very small to quite sizeable.
41
5. EXPLORING LABOUR PRODUCTIVITY
This chapter investigates how the selected firm characteristics from Chapter 3 are related to labour productivity among small and medium sized businesses. Productivity as discussed in this chapter (and this report) refers to labour productivity, which is defined as value added divided by the number of (effective) full time employees. As in Chapter 4, we cross-tabulate labour productivity with firm characteristics. However, in this case, as discussed in Chapter 1, we define a business as having ‘High’, ‘Medium’ or ‘Low’ productivity relative to its industry. In particular, the relative productivity of a business is defined as high if it is in the top 30 per cent among all businesses in the same industry. Those businesses with productivity in the bottom 30 per cent are categorised as low productivity businesses, while the rest fall into the group with medium productivity. The comparison for businesses in any given subgroup is against the average for the two-digit industry to which each firm belongs. In other words, we will be able to see easily how similarly a given subgroup of business is distributed, on the basis of productivity performance, compared to the overall sample of firms. In order to present the various comparisons in an easily understandable form, we weight them by the respective proportions and show the results graphically. In plain English, this means that each industry’s productivity distribution (30-40-30) is represented as a straight line horizontal at unity. A subgroup of businesses is separately examined to see how its distribution between these three groups compares to the industry averages. If it is greater /less than the average in any group (low, medium, high) it will record a value above/below one. Thus, looking over the three possible ‘performance groups’ in each case, low to high running from left to right, there are four possible shapes that could occur when mapping any subcategory of firms sharing a given characteristic. •
Upward-sloping: skewed towards the high productivity group.
•
Downward-sloping: skewed towards the lower productivity group.
•
V-shape: skewed away from the middle-productivity group.
•
Inverted V-shape: skewed towards the middle-productivity group
The greater the numeric distance from unity in any group, the more disproportionate the effect the characteristic appears to have (that is, the
42
more the distribution of businesses with a particular characteristic is skewed away from the industry average). Recall that by focusing here on labour productivity rather than total factor productivity we have a less general performance measure. To say that a ‘more productive’ firm performs better than a ‘less productive one’ relies on an assumption that the firms in question have similar production processes (similar capital/labour ratios), which then gives an indication of how efficiently labour is used in that production process across firms. That is, labour productivity greatly depends on capital intensity. Thus, the use of a relative (to each industry) productivity measure, instead of absolute labour productivity, will eliminate some of the capital intensity effects (to the extent that capital intensity is similar within 2 digit industries). The use of two-digit8 ANZSIC industry categories to classify industries may not be detailed enough to capture all the differences in capital intensity and other industry specific characteristics. However, since the sample size is small, it is not feasible to employ three or four digit industry classification in the current analysis.
5.1.
Key Business Characteristics Again, we start by examining some fundamental characteristics of the businesses under examination, and how they affect labour productivity. As expected, young businesses, in general, have lower productivity, while older businesses tend to have higher productivity (see Figure 5.1), a result that holds across firm-size subcategories. The result appears ‘skewed’ in that the productivity of ‘established businesses’ tends to lie close to the average, while the younger business are further from the average. This result is explained by the fact that there are fewer young businesses in the sample, hence their effect on the aggregate is small. This is consistent with idea that low productivity businesses are more likely to be eliminated through competition.9
8
Several industries are grouped together for this purpose because there are not enough observations in those industries. If businesses acquired more capital over time, becoming more capital intensive, that might explain why older firms exhibit higher productivity. However, it is not easy to disentangle the connections between firm size, firm age, and increased capital intensity without reverting to more complex methods of analysis than are suitable for this report. 9
43
Figure 5.1: Productivity by Firm Age
Ratio Relative to Industry Average
2
1.5 Young firms Middle-aged firms Older firms
1
0.5 Low
Medium
High
Productivity Performance
Figure 5.2 shows that family businesses are skewed towards the low productivity group. This again may be an artifact of differences in capital intensity: family businesses may be more labour-intensive, which would affect these results. However, it might be argued that the productivity results here reflect real differences in labour efficiency in such businesses compared to non-family businesses. One reason for such differences might be that employees have less incentive to work as hard in family businesses because of the relatively limited chance of being promoted. We also need to examine the possible firm size effect, since the size of the average family businesses is smaller than that of the average non-family business. 66% of the very small (micro) businesses (1-4 employees) are family businesses, while only 25% of medium sized businesses (100-199 employees) are family businesses. However, looking at the size disaggregation, we find this low productivity result is common to businesses in all the firm-size subcategories.
44
Ratio Relative to Industry Average
Figure 5.2: Productivity and Family Businesses 1.5
Family business Not family business
1
0.5 Low
Medium
High
Productivity Performance
How is productivity correlated with exporting behaviour? The first row of Table 5.3 (see Appendix) shows that 48.4% of exporting businesses belong to the high productivity group and only 13.8% of them belong to the low productivity group; this distribution is skewed considerably upward from the average. Treating the productivity figures as indicators of superior performance— noting that this is something to be cautious about—it might be argued that surviving in highly competitive international markets requires firms to be of above average productivity. It is not clear which way the causality runs: whether productive firms tend to export or exporters have no choice but improve their productivity in order to survive in the highly competitive international market. It is also possible that there is a firm size effect behind the result. From the statistics in chapter 2 and 3, we know that larger firms tend to have higher productivity and also more likely to export. However, when we examine the correlation for the four separate size groups, we find that the link still holds clearly for each size subcategory. Exporting and productivity are clearly associated with each other, regardless of firm size.
45
Ratio Relative to Industry Average
Figure 5.3: Productivity and Exporting Status
5.2.
1.5
Does export Doesn't export
1
0.5 Low
Medium
High
Productivity Performance
Management characteristics As noted in Chapter 3, a majority of SMEs have a single key decision-maker. However, whether businesses have a sole decision-maker or not has little impact on their relative productivity. Breaking it down by business size, it seems that small enterprises with sole decision-makers are skewed towards low productivity, and medium sized enterprises are skewed towards higher productivity. This may simply be reflecting differing capital/labour ratios in larger firms. There is observed lower productivity for businesses where the sole decisionmaker is female (Figure 5.4). This pattern tends to be consistent over firm size groups. Whether this is due to females tending to manage more labourintensive businesses (for example, in the service industries)10 or whether it is indicative of discrimination faced by women managers or some other distinguishing factors is unclear. These results, it should be noted, are derived from a small sample, with only 165 enterprises having a female decision-maker.
10
There is some evidence of women managers being concentrated in industries with lower than average capital intensity but it is not overwhelming. 46
Ratio Relative to Industry Average
Figure 5.4: Productivity by Director's Gender 1.5
Female director Male director
1
0.5
Low
Medium
High
Productivity Performance
Education is another aspect we examine in relation to productivity. The data show that businesses with tertiary qualified sole decision-maker are overrepresented in the higher productivity group (Figure 5.5). The highproductivity skewness is even more obvious when the sole decision-makers have business related tertiary degrees. By and large this pattern holds when size groups are disaggregated.
Ratio Relative to Industry Average
Figure 5.5: Productivity by Director's Education 1.5 High school Certificate 1
Tertiary Nonbusiness Tertiary business
0.5 Low
Medium
High
Productivity Performance
5.3.
Management Methods We turn now to examine some of the things businesses do in order to improve their performance. As listed in earlier chapters, these involve planning (as a formal business process), benchmarking (comparison with other businesses), and networking (establishing business liaisons with other enterprises). For businesses that undertake formal business planning, there is a distinct over-representation in the high productivity category, balanced by underrepresentation in the low productivity category (Figure 5.6). 47
For businesses that engage in networking and benchmarking with other businesses in 1997, there is a similar ‘top-heavy’ pattern: they are skewed towards high and medium productivity (Figure 5.7, 5.8). This is of course another set of characteristics where the relationship between management method and relative productivity may be dominated by firm size effects or not. Tables 2.5 and 3.5 show positive correlation between productivity and firm size as well as between management method and firm size. However, again the correlations are maintained even when size is taken into account. (This is particularly robust for the businesses undertaking formal planning.) Thus, practicing these management methods may be contributing to increasing productivity. Although the higher capital-labour ratio in big firms is the most important factor for their higher productivity, better management methods in firms of varying sizes may also be an important factor.
Ratio Relative to Industry Average
Figure 5.6: Productivity and Formal Planning 1.5
Does plan Doesn't plan
1
0.5 Low
Medium
High
Productivity Performance
Ratio Relative to Industry Average
Figure 5.7: Productivity and Networking 1.5
Does network Doesn't network
1
0.5 Low
High
Medium
Productivity Performance
48
Ratio Relative to Industry Average
Figure 5.8: Productivity and Benchmarking 1.5
Does benchmark Doesn't benchmark
1
0.5 Low
Medium
High
Productivity Performance
5.4.
Industrial Relations There were mixed results on how unionisation of a firm’s workforce affected its innovativeness. Here, looking at how the degree of unionisation in the workplace impacts upon a firm’s recorded labour productivity, we also find a relationship between the two. The main result is that unionisation does seems to be positively correlated with productivity. One argument for this result is that unionisation may be associated with lower labour turnover, so that more specific human capital is accumulated by workers, leading to higher productivity. Alternatively, a possible explanation is known as the ‘voice model’: the union provides a forum for communication between workers and employers to improve practices and morale. Again, however, as with a number of other characteristics, there could be a size effect at work: larger firms are generally more unionised, and more productive, than smaller ones. When looked at by size group, the result above does seem to change except for larger medium firms, but this is not statistically significant. For the other size groups, the effects are mixed. This provides some indication that firm size effects matter. Looking at the key form of labour agreement (award, contract etc.) it is the businesses where individual contracts predominate that show higher labour productivity, and by a distinct margin (Figure 5.10). Firms where awards or unregistered enterprise agreements are most common under-perform by the productivity criterion, being more heavily represented in the middle productivity group (awards) or the low productivity group (unregistered agreements).
49
Ratio Relative to Industry Average
Figure 5.10: Pay determinant and productivity 1.5 Awards Contracts
1
Unregistered agreements Registered agreements
0.5 Low
Medium
High
Profitability performance
Government programs Looking at firms that are involved with government programs, there is a clear pattern of higher productivity in businesses that participate in government programs. In certain cases (Ausindustry, Austrade and EFIC programs), the effect is dramatic, with 45-55% of participating firms in the high productivity group. There is no immediately obvious explanation for this result: that is, government programs may improve the productivity of participating firms, or else it may be that firms with higher productivity can more readily participate in such programs. In particular it may be that larger firms, which exhibit higher productivity, are the ones that tend to undertake such programs: in other words, the observed results may be due to the firm size effect that we have investigated elsewhere. Examining the results by business size, there is little evidence to support this: the results tend to hold by firm size. In some cases only a small proportion of firms—especially in the small-firm subcategories—participate in programs, but those that do are almost always high productivity firms.
Figure 5.11: Productivity and Government Programs Ratio Relative to Industry Average
5.5.
1.5
Participant Non-participant
1
0.5 Low
Medium
High
Productivity Performance
50
Ratio Relative to Industry Average
Figure 5.12: Productivity and Ausindustry Programs 1.5
Participant Non-participant
1
0.5 Low
Medium
High
Productivity Performance
Ratio Relative to Industry Average
Figure 5.13: Productivity and Austrade Programs 1.5
Participant Non-participant
1
0.5 Low
Medium
High
Productivity Performance
Figure 5.14: Productivity and EFIC Programs
Ratio Relative to Industry Average
2
1.5
Participant Non-participant
1
0.5
0 Low
Medium
High
Productivity Performance
51
Ratio Relative to Industry Average
Figure 5.15: Productivity and Employment Programs 1.5
Participant Non-participant
1
0.5 Low
Medium
High
Productivity Performance
Ratio Relative to Industry Average
Figure 5.16: Productivity and Other Government Programs 1.5
Participant Non-participant
1
0.5 Low
Medium
High
Productivity Performance
5.6.
Conclusions Some strong associations have been found in the data between particular firm characteristics and the measure of labour productivity constructed for each business. Again, a ‘high score’ on the productivity scale may be indicative of more efficient usage of labour by that business; on the other hand it may reflect differences in the production process in which case we should be cautious about drawing interpretations about firm performance from such results. That said, some of the results are striking and lend themselves to an interpretation about business performance, even if a somewhat tentative one. One such striking result concerns firms that export. It is not clear why capital-intensive firms—which ‘all other things equal’ will exhibit higher labour productivity—should be heavier exporters than less capital-intensive firms, unless it is a reflection of business size. That is, bigger firms are typically more capital-intensive, and typically better equipped to explore and exploit exporting opportunities, thus explaining the correlation between exports and productivity. However, this explanation was refuted when it was found that the link between exporting businesses and high productivity levels was consistent across firms from micro to medium-sized enterprises. 52
While it is not possible to pin down a causal effect from such an observed association, the robustness of the result is enough to suggest that the exporting firms may genuinely be ‘better performers’. Whether it is the case that ‘better-performing firms export’, or that ‘exporters become betterperforming firms’ is yet to be seen. Other results are worth commenting on. Younger (newer) businesses and family businesses both tend to exhibit below-average productivity. The gender of the ‘managing director’ or sole decision maker (where there is one) appears to be important, in that female-run businesses are associated with below-average productivity also. Education also matters. Tertiary education is associated with high productivity, especially for business-related tertiary degrees. The degree of unionisation of the workforce and the use of contract agreements also turn out to matter, although there is some suggestion that the union effect is partly driven by firm size effects. The management methods we investigated—benchmarking, networking and formal planning—are also strongly associated with above-average productivity. Again, what might be explained away by a correlation between these characteristics and firm size turns out to be robust across firm size subcategories. The same can be said of participation in government programs: the businesses that participate do tend to be higher-productivity businesses, a result consistent across firm size subcategories.
53
Appendix to Chapter 5 Table 5.1
Age of business and relative productivity
Age of business
Labour productivity (relative) Low Medium High
3 years and less more than 3 to 10 more than 10 Total
54.7 32.7 25.4 29.7
Table 5.2
23.3 38.7 42.6 40.0
22.0 28.7 32.1 30.3
Labour productivity (relative) Low Medium High
No Yes Total
24.3 35.3 29.7
38.9 41.2 40.0
36.9 23.5 30.3
Labour productivity (relative) Low Medium High
No Yes Total
33.7 13.8 29.7
40.6 37.7 40.0
25.7 48.4 30.3
100 100 100
Total 100 100 100
Gender of sole decision maker and relative productivity
Gender of major decision maker
Labour productivity (relative) Low Medium High
Female Male Total
39.4 28.6 29.7
Table 5.5
Total
Export status and relative productivity
Does business export?
Table 5.4
100 100 100 100
Family business status and relative productivity
Family business
Table 5.3
Total
40.0 41.0 40.0
20.6 30.4 30.3
Total 100 100 100
Education of sole decision maker and relative productivity
Education of major decision maker High school Certificate Tertiary Tertiary (business related) Total
Labour productivity (relative) Low Medium High 29.9 43.7 26.4 35.5 39.4 25.1 29.2 38.6 32.3 20.1 40.4 39.5 29.7 40.0 30.3
54
Total 100 100 100 100 100
Table 5.6
Formal business plan and relative productivity
Have formal business plan?
Labour productivity (relative) Low Medium High
No Yes Total
34.1 20.7 29.7
Table 5.7
40.2 39.7 40.0
25.7 39.7 30.3
Labour productivity (relative) Low Medium High
No Yes Total
32.2 21.2 29.7
39.3 42.4 40.0
28.5 36.5 30.3
Labour productivity (relative) Low Medium High 32.6 39.2 28.2 20.3 42.7 37.0 29.7 40.0 30.3
100 100 100
Total 100 100 100
Union density and relative productivity
Union density (% of workers)
Labour productivity (relative) Low Medium High
None Up to 50% More than 50% Total
33.1 19.2 18.6 29.7
Table 5.10
Total
Benchmark with other businesses and relative productivity
Benchmark with other businesses? No Yes Total
Table 5.9
100 100 100
Network with other businesses and relative productivity
Network with other businesses?
Table 5.8
Total
39.1 43.2 42.9 40.0
27.9 37.7 38.6 30.3
Total 100 100 100 100
Pay determinant and relative productivity Labour productivity (relative)
Which type of employment contract most common
Low
Medium
High
Total
States/Federal awards Individual contracts unregistered enterprise agreement registered enterprise agreement Total
29.1 23.7 36.6 33.4 29.7
44.9 37.0 39.7 34.3 40.0
26.0 39.3 23.7 32.3 30.3
100 100 100 100 100
Table 5.10a
Federal/State awards and relative productivity Labour productivity (relative)
Proportion of employees on Federal/State awards
Low
Medium
High
Total
None Some All Total
26.0 21.3 33.1 27.5
38.0 44.5 43.6 41.9
36.1 34.1 23.3 30.6
100 100 100 100
55
Table 5.10b
Individual contracts and relative productivity Labour productivity (relative)
Proportion of employees on individual contracts
Low
Medium
High
Total
None Some All Total
32.9 19.1 24.0 27.5
43.1 43.5 36.3 41.9
24.0 37.4 39.8 30.6
100 100 100 100
Table 5.10c
Unregistered enterprise agreement and relative productivity Labour productivity (relative)
Proportion of employees on unregistered enterprise agreement
Low
Medium
High
Total
None Some All Total
26.3 29.8 38.1 27.5
42.5 36.2 41.2 41.9
31.2 34.0 20.6 30.6
100 100 100 100
Table 5.10d
Registered enterprise agreement and relative productivity Labour productivity (relative)
Proportion of employees on registered enterprise agreement
Low
Medium
High
Total
None Some All Total
28.8 12.5 20.5 27.5
42.1 40.1 41.0 41.9
29.1 47.5 38.5 30.6
100 100 100 100
Table 5.11
Government program and relative productivity
Participate in government program
Labour productivity (relative) Low Medium High
No Yes Total
31.0 24.1 29.7
Table 5.12
29.7 32.9 30.3
100 100 100
AusIndustry program and relative productivity
Participate in AusIndustry program No Yes Total
Table 5.13
39.4 43.0 40.0
Total
Labour productivity (relative) Low Medium High 30.0 40.1 29.9 16.2 38.1 45.7 29.7 40.0 30.3
Total 100 100 100
Austrade program and relative productivity
Participate in Austrade program
Labour productivity (relative) Low Medium High
No Yes Total
30.5 13.4 29.7
40.0 41.2 40.0
56
29.6 45.4 30.3
Total 100 100 100
Table 5.14
EFIC program and relative productivity
Participate in EFIC program
Labour productivity (relative) Low Medium High
No Yes Total
29.4 11.8 29.6
Table 5.15
40.7 32.9 40.1
29.9 55.3 30.2
Total 100 100 100
Employment program and relative productivity
Participate in employment program No Yes Total
Labour productivity (relative) Low Medium High 29.9 39.3 30.8 28.6 45.4 26.0 29.7 40.0 30.3
57
Total 100 100 100
6.
EXPLORING PROFITABILITY This chapter follows the previous two in exploring connections between particular firm characteristics and firm performance, in this case profitability. As outlined in Chapter 2, two measures of profitability are used: the EBDIT margin, and the return on assets (ROA). Using each of these measures, we follow the procedure in Chapter 5 and divide businesses within a given industry into three groups that we label ‘High’, ‘Medium’ and ‘Low’ profitability, relative to that industry. As before, the ‘High’ group is the top 30%, ‘Medium’ is the middle 40%, and ‘Low’ is the bottom 30%. We note again that the industry classification we use to divide the sample is the set of two-digit ANZSIC categories, subject to the caveats already raised. As in the previous two chapters, we cross-check the results against size subcategories and report instances where the aggregate results are confirmed or contradicted by this disaggregation. In this chapter, we also distinguish between the profit performance of incorporated and unincorporated firms, since there are some accounting differences between these types of businesses. Most notably, payment to the business owner may be classified as profit or as wages depending on the legal status of the enterprise.
6.1.
Key Business Characteristics How does a business’s length of operation affect its profitability? As in the previous two chapters we first analyse this question using a cross-section of observations over one year, seeing how the age of different businesses is correlated with profitability. The youngest firms (less than three years old) are, unsurprisingly concentrated in the ‘below average’ profitability groups. It seems reasonable to expect that businesses will take some time to show profits while they are in the stages of establishing themselves. However, note that there is not a clear negative relationship: there is a slightly above average representation of young firms in the high profitability category. By contrast, firms of what might be called middle-age (3 to 10 years) exhibit a very modest negative relationship, while older firms (over 10 years) exhibit an upward tilt, although both these age groups are close to the average.
58
Ratio Relative to Industry Average
Figure 6.1: EBDIT Margin by Firm Age 1.5
Young firms Middle-aged firms Old firms
1
0.5 Low
Medium
High
Profitability Performance
Looking at whether incorporation affects the profit performance, we find that it does: in the ‘youngest firm’ category, unincorporated businesses are skewed away from the high-profitability category, but for the incorporated businesses, they are if anything more heavily represented in that category. Whether the firm is a family business or not has little impact on profitability performance, by either measure. Whether the firm exports has some, but not much, effect at the aggregate level: there is a slight over-representation in the high profitability group using the EBDIT measure, although the ROA measure tells a somewhat different story. However, neither exhibit statistical significance at the aggregate level. Looked at by firm size, there is some positive correlation between export status and profitability especially for medium-sized firms.
6.2.
Management Characteristics The majority of SMEs consists of sole-director businesses—those with a single key decision-maker—as seen in Chapter 3. Is there any reason to expect that sole-director businesses should perform better or worse with regard to profitability? There is no obvious a priori reason to expect any particular difference. Looking at the evidence, it appears that sole-director firms are about ‘average’ in terms of profitability. However, the other firms exhibit, if anything, profitability a little above average, and the difference is statistically significant. The same pattern is revealed when looking at incorporated and unincorporated businesses.
59
Ratio Relative to Industry Average
Figure 6.2: EBDIT Margin and Decision Makers 1.2
Single decisionmaker
1
No single decisionmaker
0.8 Low
Medium
High
Profitability Performance
If the sole decision-maker is female, are there differences in profitability? Empirically, businesses with female-decision makers are clustered towards the middle and low profitability groups. By and large, the negative relationship between profitability and female management of the business is maintained for both incorporated and unincorporated firms, although the difference between performances by gender is more marked for the unincorporated enterprises. (In fact, for incorporated businesses, female-run firms do rather well on the ROA measure.) It is not clear what the reasons for this might be. It may be as simple as that discrimination is at work, whether from upstream or downstream firms who treat such a business ‘differently’ by virtue of the gender of its manager. It may be that women are in labour intensive service industries where there are fewer barriers to entry, so that the competitive conditions result in lower profit rates, and a need to be innovative to ‘stay in the game’. Alternatively, it may be that women ‘manage differently’, and a lack of managerial experience and female role models leads to poorer performance on the profitability yardstick. It may also be that female managers deliberately ‘manage differently’, seeking a satisfying work life and harmonious workplace even if at the expense of some potential for higher profits. There is no particular means to test between these alternatives with the available data.
60
Figure 6.3a: EBDIT Margin by Director's Gender Ratio Relative to Industry Average
1.3 1.2 Female directors
1.1
Male Directors
1
Non-single director firms
0.9 0.8 0.7 Low
Medium
High
Profitability Performance
Ratio Relative to Industry Average
Figure 6.3b: ROA by Director's Gender 1.2 Female Directors
1.1
Male Directors
1
Non-Single Director Firms
0.9 0.8 Low
Medium
High
Profitability Performance
Evidence of how educational attainment is correlated with profitability is mixed, and somewhat surprising. What might be expected is that greater education is correlated with better performance on the profitability measures (as it was for productivity). However, while the evidence reveals that high school graduates/leavers are concentrated outside the high performance group for both profit measures, managers with business-related tertiary qualifications also appear to perform relatively poorly. This may reflect the high intensity of competition in the industries that require managers with such formal skills, but this is only speculation at this stage. (The story does not appear to change when incorporation is taken into account.)
61
Ratio Relative to Industry Average
Figure 6.4: EBDIT Margin by Director's Education 1.2 1.1 High school Certificate Tertiary business
1 0.9 0.8 Low
Medium
High
Profitability Performance
Ratio Relative to Industry Average
The experience of the manager in a senior position seems likely to influence how well a firm is run and how profitable it is. Yet the correlation found in the data is not what this logic would imply: it appears that if anything, businesses whose managers report having ‘less’ experience appear to outperform businesses with more experienced managers. This is a surprising result, and there may be a firm size effect at work. Recall that larger businesses tend to have managers with fewer years’ experience than do smaller firms. Yet we should also recall that on average, larger businesses have lower profitability than smaller ones. This suggests that firm size effects, if they existed, would generate a rather different result than that found. As it is, there is some slight evidence of a negative correlation between experience and profitability when we perform the disaggregation by size, but not across all size groups. Looking at incorporation status does not appear to influence the general result.
Figure 6.5: ROA and Manager Experience 1.2 1.1
Below 3 years 1
3 to 10 years Over 10 years
0.9 0.8
Low
Medium
High
Profitability Performance
6.3.
Management Methods In previous chapters we examined what we have labeled ‘management methods’, by which we particularly refer to whether firms benchmark 62
against other firms, whether they formally plan, and whether they network with other businesses. These methods were seen to be correlated with both innovation and labour productivity, and we now investigate them with respect to profitability. Firms that compare, or benchmark, their performance actually appear unremarkable when compared against other firms: if anything, they are overrepresented in the ‘middle-profitability’ group. The under-representation in the high-profitability group is actually more marked for the unincorporated firms. It is not clear why this result is occurring, and it may have something to do with a size effect. That is, if larger firms (which tend to have lower profitability) are the ones best equipped to undertake costly benchmarking activities, may we be picking up an indirect association between the characteristic and the performance measures? Looking at the size breakdown, the effects are mixed and the degree of statistical significance on the results is not high. This suggests that there may be some degree of size effect that is influencing the results.
Ratio Relative to Industry Average
Figure 6.6: ROA and Benchmarking 1.2
Does benchmark Doesn't benchmark
1
0.8 Low
Medium
High
Profitability Performance
For the formal planning characteristic, there is also evidence of the distribution of businesses that plan being negatively related to profitability. The low profitability category has the highest representation of planning businesses, while in the high profitability category such firms are clearly underrepresented. Regarding networking characteristics, there is no obvious relationship with profitability measures, nor are there any marked effects by firm size when the disaggregated subcategories are examined.
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Ratio Relative to Industry Average
Figure 6.7: ROA and Formal Planning 1.2
Does plan
1
Doesn't plan
0.8 Low
Medium
High
Profitability Performance
The minimal effect of these business characteristics on profitability is in stark contrast to the strong relationships with innovation and productivity. The question is, why the discrepancy? It may be that firms that are innovative and productive, but yet not exceptionally profitable, may be the ones for whom networking, benchmarking and planning are most valuable: to find ways to convert performance on one scale into better performance on another. It may also have to do with time lags: that ‘pro-active’ forwardlooking firms that innovate and network etc. will see higher profits in years to come. Moreover, industries with intense competitive pressures may face lower profit rates but, simultaneously, an imperative to maintain productivity and innovativeness. Thus, there is a range of possible explanations as to why productive innovative firms that benchmark, network and plan may not exhibit higher than average profitability. Only a more detailed and rigorous analysis with time series data would be able to help distinguish between the various alternative hypotheses.
6.4.
Industrial Relations It might be thought that unionisation of the workplace would be associated with lower profitability for that business. This might be because workers in highly unionised businesses are able to extract concessions in the form of wages and conditions that would hamper its competitiveness. Alternatively, there might be an association for indirect reasons, such as unionisation being more associated with declining industries, or workplaces using outmoded technologies because the union had resisted newer production methods that would result in job losses. It might also be counter-argued that as unions have evolved and their roles and attitudes have changed, there may be benefits to the firm of a unionised workforce. Higher morale, lower staff turnover and consequently more incentive for workers to invest in firm-specific human capital (something we 64
suggested in the previous chapter on productivity) might all be reasons why a unionised workforce may be good for an SME’s bottom line. The evidence is mixed. There is, according to the BLS data, some apparent negative correlation between the degree of unionisation and the profitability of that business. On the basis of EBDIT margins, it appears that businesses in the medium and high unionisation categories are skewed away from the most profitable group. For the ROA measure, it is the businesses with a ‘medium’ degree of unionisation that are not so highly profitable. Looking at the results by incorporation status, the correlation between greater union density and lower profitability is confirmed for unincorporated businesses using both measures of profitability. For incorporated firms there is some evidence that suggests the more unionised workplaces are more profitable, but there is some ambiguity in terms of statistical significance. However, there may be a firm size issue at work as well here. Larger firms are more likely to also be the more unionised firms, and there is some negative relationship between firm size and profitability. Looked at by size, there is no clear effect for small firms, few of which have high union density anyway. For medium-sized firms, there is no clear association between union density and profitability. For the smaller medium-sized enterprises, the most heavily unionised firms seem to perform best, while for the larger mediumsized businesses, if is the ‘somewhat’ unionised firms that perform best. Based on these results, we would be hesitant to draw any conclusions about unionisation being harmful to firm profitability. The size effect could well explain the observed negative correlation.
Ratio Relative to Industry Average
Figure 6.8: EBDIT Margin and Union Density 1.2 No union members Some union members Many union members
1
0.8 Low
Medium
High
Profitability Performance
Looking at the proportions of workers on awards, and the proportion on contracts, we find associations with profitability, but they are not straightforward to interpret. Regarding employees on awards, there is little effect on firm profitability for the ‘low coverage’ and ‘high coverage’ businesses, but firms with ‘some’ workers on awards are skewed away from the high-profitability category. Looking at the impact of workers on 65
Ratio Relative to Industry Average
individual contracts, those businesses with ‘some’ or ‘all’ of their employees on contracts are also skewed away from the most profitable category, with the ‘all’ group over-represented in the lowest profitability category. (When looked at by size of enterprise, the effects, and their statistical significance, varies, shedding no further light.)
Figure 6.9: Pay determinant and ROA 1.2 Awards Contracts 1
Unregistered agreements Registered agreements
0.8 Low
Medium High
Profitability performance
Turning to enterprise agreements, there is again mixed evidence. Businesses with ‘all’ their employees on registered agreements rate highly on both profit measures, and those with ‘some’ employees on both registered and unregistered agreements are over-represented in the middle-profitability category. However, the statistical significance level of these results is low, no doubt due to the low proportions of enterprises where such enterprise agreements feature.
6.5.
Government Programs From earlier chapters, we know that firms that are involved in government programs are likely to be larger, to be active innovators, and to exhibit higher labour productivity than those firms that do not participate. What is revealed in the data about the profitability of these firms? The key result seems to be that firms that participate in programs are likely to be less profitable than the non-participant firms.
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Ratio Relative to Industry Average
Figure 6.10: ROA and Government Programs 1.2 1.1 Participant Non-participant
1 0.9 0.8 Low
Medium
High
Profitability Performance
6.6.
Conclusions In this chapter we have examined how the characteristics identified in Chapter 3 are related to firm performance in terms of profitability measures. What is clear is that the results are somewhat at odds with, and considerably less striking than, those for innovation and productivity. In terms of what we have labeled ‘key business characteristics’, a variable such as whether the business exports, which was highly correlated with innovation and productivity, is shown to have at best a slight relationship with profitability. Turning to management characteristics, gender (of the sole decision-maker, where there was one) again turned out to matter. Female directors, for reasons not yet clear, perform poorly by our profitability yardstick. Tertiaryeducated managers also featured low on the profitability scale. Looking at management methods, firms that benchmarked their performance against others were skewed away from high and low, and into the middle, profitability group. Businesses that planned were under-represented in the high-profitability category. By contrast, there was no notable relationship between networking and profitability. There was some observed negative correlation between union density and profitability, but that may be an artifact of firm size differences. Finally government programs seemed to be the domain of ‘under-performers’ in terms of profitability.
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Appendix to Chapter 6 Table 6.1
Age of business and EBDIT margin EBDIT margin Low Medium High
Age of business 3 years and less more than 3 to 10 more than 10 Total
Table 6.2
No Yes Total
100 100 100
Low
EBDIT margin Medium High
Total
37.39 41.52 40.05
33.89 28.25 30.26
100 100 100
EBDIT margin Low Medium High
Total
Decision-maker gender and EBDIT margin
Female Male Total
33.94 30.06 29.69
44.24 41.51 40.05
21.82 28.43 30.26
100 100 100
Return on assets Medium High
Total
Decision-maker gender and return on assets
Gender of major decision maker Female Male Total
Table 6.4
32.43 28.94 30.83 30.26
28.72 30.22 29.69
Gender of major decision maker
Table 6.3b
25.68 39.92 41.62 40.05
Sole decision-maker and EBDIT margin
Has sole major decision maker?
Table 6.3a
41.89 31.14 27.55 29.69
Total
Low 35.15 29.85 29.75
36.36 41.93 39.97
28.48 28.23 30.28
100 100 100
Decision-maker education and EBDIT margin
Education of major decision maker High school Certificate Tertiary-general Tertiary-business Total
Low
EBDIT margin Medium High
29.55 29.47 30.94 31.36 29.69
44.71 39.11 38.27 42.27 40.05
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25.74 31.42 30.78 26.36 30.26
Total 100 100 100 100 100
Table 6.5
Decision-maker experience and return on assets
Experience of decision maker Low Medium High Total
Table 6.6
33.38 30.22 26.04 30.28
Return on assets Low Medium High 30.05 28.76 29.75
100 100 100 100
38.64 44.32 39.97
31.31 26.92 30.28
Total 100 100 100
Has formal business plan and return on assets
Business has formal business plan No Yes Total
Low
Return on assets Medium High
28.58 32.15 29.75
38.73 42.53 39.97
Total
32.69 25.33 30.28
100 100 100
EBDIT margin Medium High
Total
Union density and EBDIT margin
Unionisation of workforce No union members Up to 50% members Greater than 50% members Total
Table 6.9
37.51 41.61 42.01 39.97
Total
Benchmarks against other businesses and return on assets
No Yes Total
Table 6.8
Return on assets Medium High
29.11 28.16 31.95 29.75
Business compares against others
Table 6.7
Low
Low 29.81 29.71 28.29 29.69
38.81 43.77 44.57 40.05
31.37 26.53 27.14 30.26
100 100 100 100
EBDIT margin Low Medium High
Total
Payment method and EBDIT margin
Basis on which most employees earn Awards Contracts Unregistered agreements Registered agreements Total
27.8 34.25 28.87 29.49 29.69
43.61 37.48 39.91 36.19 40.05
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28.59 28.28 31.22 34.32 30.26
100 100 100 100 100
Table 6.10
Government programs and return on assets
Business utilises a government program No Yes Total
Low
Return on assets Medium High
29.2 32.24 29.75
39.31 42.99 39.97
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31.49 24.77 30.28
Total 100 100 100
7.
CONCLUSIONS In this report, we have utilized the 1997 data from the BLS dataset to examine the connections (in terms of statistical correlations) between a number of business performance measures—innovation, productivity, profitability— and a set of characteristics associated with the businesses under study. What is most immediately noticeable from our analysis is that while we have derived a series of striking, consistent and robust results about what is associated with innovation and productivity, the results for profitability have differed considerably. The variables that appeared to matter most for productivity and innovation included •
whether the firm exported;
•
the gender and education of its key decision-maker (if it has one);
•
whether it benchmarked, networked, and planned;
•
and whether it participated in government programs.
Many of these relationships may have been explained away by a firm-size effect, as both these performance measures are positively related to firm size, as are many of the characteristics. We control for this by splitting the businesses up into four separate size categories, and checking whether the correlations still held. For the characteristics above, the relationships proved quite robust to our firm-size breakdown. Even given a set of significant and robust results, deducing (or inferring) causal connections between characteristics and performance measures is something we should be cautious about. Looking at exporting and innovation, for example, it may be that innovative firms are more outward looking and so actively seek overseas markets. Alternatively it may be that exporting firms are exposed to both new ideas and to tough competitive pressures and need to change what they do and how they do it in order to prosper. So, does exporting ‘cause’ innovativeness, or does innovativeness lead to exporting? Or is there another explanation we are missing? At this stage, firm conclusions are not possible. Similarly, do government programs lead to higher innovation in businesses that might not otherwise undertake any, or do innovative businesses seek out assistance in the form of such programs? We can only speculate as to the
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extent, even the direction, of causation in the relationships we have identified in the preceding chapters. There are also questions arising about how the various performance measures themselves are linked to each other. Superficially at least, it seems that innovation and productivity are closely linked, in that they both have similar (statistically significant) relationships with a variety of characteristics. Again, how the link works is not clear, but it seems that firms that are innovative have something in common with those that score highly in regards to labour productivity. By contrast, both innovation and productivity do not seem closely linked to profitability, judging by their respective relationships with the characteristic variables. For example, firms that export are also atypically innovative (on average) and productive, by our measures. Yet there is no clear sign that exporting firms are more profitable than non-exporters. On other scores too—e.g. director’s education; use of government programs—the results on innovation and productivity clearly conflict with those on profitability. It is, of course, too much to expect that we would find clear and unambiguous connections between the various performance measures: that the more innovative businesses would also be the more profitable ones, and so on. But what is striking about the results is the extent to which they appear to conflict. Strong positive correlations between certain characteristics and innovation and productivity are not borne out, and sometimes contradicted, when we examine how those characteristics are related to profitability. Obviously, any relationship between innovativeness and profitability is going to be a complex one, involving riskiness and time lags. Not all innovations pay off, and those that do will usually only do so after some time, often measured in years. So there is no surprise in the finding that innovation in one year is not closely tied to profitability in that year. That said, the apparent similarity of the results between innovation and productivity is striking. What of productivity and profitability? Would not enterprises that are productive also be more likely to be more profitable? Again, we note that our measure is of labour productivity, not of total factor productivity, and so it will be influenced by differences in capital intensity as well as by differences in the efficiency of the workforce or the skills of management. To some degree we (indirectly) control for this: our comparisons of productivity are within two digit industry groups, in which there might be expected to be some similarity of production process. Also, by undertaking comparisons controlling for firm size, we remove much of the effect of increasing capital intensity associated with larger enterprises.
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As a result, we believe some of the strong results to do with productivity reflect more than mere capital intensity effects: there is some validity in regarding higher productivity as reflecting better performance. While, as stated, we cannot pinpoint any causal relationship, it is still insightful to have evidence of a connection and to speculate on what it might be. But if we can treat, even cautiously, productivity as some sort of performance indicator, we are again left to note the sometimes stark difference in results between productivity and profitability as performance indicators. Several possibilities present themselves to account for this discrepancy. Value-added, the numerator of the productivity measure, contains the wage bill in it, while the wage bill is deducted from revenue to obtain profits. If the wage bill turns out to be a large proportion of value-added for the more productive firms—for example, because the firm is hiring very productive workers and paying them a premium—then the profit figure will be lower relative to revenue, explaining how high productivity may be associated with low EBDIT margins. And for firms that do have a higher capital/labour ratio—and thus higher labour productivity—the assets on which the ROA measure is calculated will be greater, lowering ROA for any given profit figure. It should also be noted that we are examining small and medium enterprises, in contrast to the larger Australian businesses studied in Dawkins, Harris and King (1999). The degree of market power exercised by smaller businesses is likely to be considerably less, making profitability less of a ‘choice variable’ influenced by management and more of an exogenous variable imposed on the firm by competitive conditions in its industry. Following this reasoning, it may in fact be that enterprises in very competitive industries have to be highly productive and innovative in order to survive in the longer run: and that it may be businesses in such circumstances that seek out export markets, or assistance via government programs. A third possibility is that profitability figures are sensitive to taxation considerations. If certain revenue items can be channeled into other enterprises and thus not recorded on the books of the business being assessed, or if costs can be artificially inflated, then profitability will be lower than it would be otherwise. If it is the case that more successful firms are more inclined, or able, to look for and exploit tax loopholes and minimisation schemes, we might see businesses that perform well on one performance measure (productivity) doing systematically worse on the profitability measures. The same applies if it is labour costs that are being artificially inflated: this will reflect positively on the labour productivity measure and negatively on profitability measures. It will be possible to shed light on some of these questions by looking at data over several years, which we will undertake in the next stage of this project.
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8. REFERENCES Australian Bureau of Statistics (1999), Small and Medium Enterprises: Business Growth and Performance Survey 1997-98, Catalogue 8141.0. Dawkins, P., M. Harris, and S. King (1999), How Big Business Performs: Private Performance and Public Policy, Allen and Unwin, Sydney. Department of Workplace Relations and Small Business (1998), Annual Review of Small Business, Australian Government Publishing Service, Canberra. Industry Commission/Department of Industry, Science and Tourism (1997), A Portrait of Australian Business: Results of the 1995 Business Longitudinal Survey, Australian Government Publishing Service, Canberra. McCann, B., and C. Tozer (undated), “Introduction to the Business Longitudinal Survey”, ABS/OSB internal manuscript. Rogers, M. (1998), “Productivity in Australian Enterprises: Evidence from the ABS Growth and Performance Survey”, Melbourne Institute Working Paper No. 20/98, University of Melbourne. Rogers, M. (1999), “The Performance of Small and Medium Enterprises: An Overview Using the Growth and Performance Survey”, Melbourne Institute Working Paper No. 1/99, University of Melbourne.
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9. TECHNICAL APPENDIX 9.1.
Definitions of performance variables
Variable
Definition (1997)
Notes
EBDIT margin
Earning before depreciation, interest, and tax divided by sales
Return on Assets (ROA)
Leasing expenses adjusted EBDIT divided by assets
Leasing expenses are considered as capital expenses, representing depreciation of leasing capital plus interest of financing it. Assets here include normal assets defined used in accounting plus the imputed value of leasing capital.
Innovator (Yes/No)
Manufacturing firms Did this business in the last financial year develop any new products or substantially changed products or introduce any new or substantially changed processes Non-Manufacturing firms Did this business in the last financial year offer any new or improved products (goods or services) to its clients? Did this business in the last financial year introduce and new or improved procedures for the supply of services?
Labour Productivity
Output (value added) divided by numbers of effective full-time employees
This question varied between years. In 1995, the questions asked were Did the business introduce any new services or significantly change ways of delivering existing services, Did the business introduce any new or substantially changed goods The number of effective full-time employees is defined as numbers of full-time employees plus the numbers of part-time employees time the ratio of part-time to full-time average weekly working hours.
Notes: EBDIT = sales − total expenses + depreciation + interest + tax ROA (leasing adjusted) = (sales − total expenses +depreciation + interest + tax +leasing expenses ) / total assets + imputed capital Other income (including interest income, government subsidies, net profit (loss) on sales of fixed tangible assets, etc.) is not considered as earnings when computing these two profit measures. For non-financial sector, ignoring other income can avoid the earnings being dominated by those one-off financial activities. For financial sector, other income may be an important source of regular earnings. In the future, different definition of profitability measure may be applied for different sectors. Imputed value of leasing capital = leasing expenses / ((1/life)+interest rate) Here, expected life of capital is assumed to be 20 years on average. BIE(1988) assumed 13 years for equipment and 40 on buildings. The interest rate here applies the annual (financial year) average of 10-year Treasury bond rate. 75
Labour productivity = stock adjusted value added output / effective full-time employees. Value added = sales – material purchase + closing stock – opening stock Effective full-time employees (EFT) = FT + α * PT Where FT is the total number of full-time employees and PT is the total number of part-time employees. α is the ratio of average working hours of non-managerial part-time employees divided by the average working hours of full-time employees. The average working hours is based on the information from ABS employee Earnings and Hours Survey. There is no information on working hours for managerial employees. Therefore, the part-time full-time working hour ratio is assumed to be the same for managerial and non-managerial workers here.
9.2.
Definitions of firm characteristics variables
Category
Variable
Description
Management characteristics Business has major decision maker Gender of major decision maker Education of major decision maker Experience of major decision maker Management methods
Have formal business plan? Network with other businesses? Benchmark with other businesses?
Firm characteristics: Age of business
How many years has this business been owned/controlled by the present owners?
Family business Participate in government program Participate in AusIndustry program Participate in Austrade program Participate in EFIC program
Participate in Export Finance and Insurance Corporation (EFIC) program
Participate in employment program Participate in other program Workplace relations: Union density (% of workers) Proportion of employees on awards
Proportion of employees on individual contracts Proportion of employees on unregistered enterprise agreement Proportion of employees on registered enterprise agreement Which type of employment contract most common in the business
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Firms are categorised into three groups all, some, or no workers are paid on Federal/State Awards.
9.3.
Survey design The survey data used in this paper come from the ABS’s Growth and Performance Survey (also called Business Longitudinal Survey) which was conducted annually for firm’s financial accounts from 1994-95 to 1996-97.11 In the initial year of the survey data on 8,911 firms were collated on the basis of a stratified (by firm size and industry) random sample of the population (as defined by the ABS Business Register). The selection of firms for inclusion in the second year of the survey was more complex. First, all firms that satisfied the following criteria were in included: •
those firms that in the 1994-95 survey were identified as innovative, had exported goods or services during 1993-94 through 1994-95 or recorded increasing sales or employment over the two years 1993-94 to 1994-95.
This sample of firms provided around 3,400 firms. The remainder of firms in the first year’s sample – which might be termed ‘low performers’ – were randomly sampled on the basis of between 1 in 2 and 1 in 3. In addition, to the continuing firms a number of new firms were added to the sample in the 2nd and 3rd years of the survey.
9.4.
Weights and population estimates As stated above, the BLS survey is stratified sample of Australian workplaces. The use of this sampling technique implies it is important to use weighted data to construct population estimates for means, medians, proportions and other summary statistics. The central idea behind this is the different strata in the survey are expected to have different means, medians, etc; hence to form an appropriate estimate of the population mean, median, etc, we need to give greater weight to firms in some strata that others. The various summary statistics contains in Chapters 2 and 3 are calculated using weights and therefore represent population estimates (i.e. they are our best ‘guess’ at the values for the entire population of Australian firms). Given that these estimates are based on a relatively small sample of firms we cannot expect complete accuracy. In statistical terms this ‘lack of accuracy’ is referred to as a standard error in the estimate. The size of standard error depends on the sample design, the size of the population and the statistic being estimated. For a proportion (e.g. the proportion of innovators), the standard error is approximately equal to
standard error = [ p (1 − p ) / n]
11
1
2
The survey has also been conducted for the 1997-98 financial year and is intended to finish with the 1998-99 financial year. 77
where, α is the design effect, p the estimated proportion, and n is the number of responses. For example, if we find the proportion of innovators is 0.1 (10%), a sample size of 6,000, implies a standard error of 0.004. The standard error allows use to make statements about the true population proportion. For example, we can be 95% certain that the true population proportion of innovators is ± 2 standards errors from the estimate, in this case, between 9.2% and 10.8%. Using stratified survey data for more advanced analysis presents additional problems. For example, suppose we are interested in the relationship between firm performance and foreign ownership status. Implicit in our question is the idea that there may be some causal link between the two (e.g. perhaps, foreign owned firms get financial support from overseas, allowing them to investment more heavily, which leads to higher efficiency and performance). If we expect this relationship to hold (with equal strength) across all firms in the population then there is no need to weight our observations. Intuitively, a weighted analysis would attach more importance to specific firms and produce some average ‘relationship’ across the population; however, if the relationship is the same across all firms this weighted average will be the same as an unweighted analysis. (This issue is the same for finding a population mean: if the mean is the same across all strata then the weighted and unweighted estimate of the mean will be the same). Of course, it may be that the relationship is not constant across different strata. Surely then there is a rationale for weighting? Unfortunately, this is not the case. Any heterogeneity in the relationship across the population implies that we should look at sub-samples. An ‘average’ measure of the association is both difficult to obtain and also of limited policy use. For example, there may be a negative ownership-innovation relationship among manufacturing firms, but a positive ownershipinnovation relationship among service firms. An ‘average’ of the two effects may be zero, which is clearly not a very useful result. In view of these issues, the analysis of relationships in Chapter 4, 5, and 6 of this report is done on the basis of unweighted data. Note that the performance measures in Chapters 5 and 6 have been adjusted for industry differences, and that we are also only conducting the analysis for SMEs. These adjustments imply that we are making concessions to the idea of heterogeneity of relationships within the population. Despite this, it is important to stress that the analysis is exploratory. The issue of heterogeneity in the population is one reason for this. However, a possibly more important consideration is that cross-tabulations, or contingency tables, only look at the relationship between two variables (although we have adjusted for industry differences in our methodology). More advanced analysis would generally control for a number of other factors when looking at the relationship between two variables.
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9.5.
Distributions and measures of central tendency In Chapters 2 and 3 we use the median as a measure of typical value. The median is the value for the business that is central in the distribution. This means that when we rank all businesses according to, say, labour productivity, the median business is the business that divides the sample in half. Another common measure of the typical value is the ‘mean’. This results from adding all the values (e.g. profit ratios) together and dividing by the number of observations. (Note that this differs from an ‘average’ often used with such data, which would involve summing all the absolute levels of profit and then dividing by the sum of all sales, this measure would give most weight to large firms, whereas the mean gives equal weight to every firm). A drawback of the ‘mean’ is that it can be heavily influenced by extreme values. The BLS data set has a number of extreme observations, especially for the profit ratios, which may be due to survey error. This implies use of the mean could be misleading, hence the use of median.
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