7 Myths About Services

Australian Services Roundtable

Seven Myths about Services
Andrew McCredie and Darryl Bubner

10

Seven Myths about Services
That for some time now the services sectors cover just about everything that counts in modern economic growth has been obscured by a number of pervasive myths.1 We have to erase these myths in order to achieve sound evidenced based policies. The seven most dangerous myths are the subject of this paper. 1 2 3 4 5 6 7 Services are non-productive – they don’t create wealth Essential services must remain public services The expansion of services in the economy is a result of reclassification, not substantive changes in our economy Service sector jobs are low skill and low wage The expansion of the service sector drives down productivity growth Service sector innovation investment is low Public sector innovation can’t be measured

Most of these myths have persisted long after their expiry from the economic literature and the weight of evidence should have expunged them. Even though services now account for two thirds of Australia’s national economic activity2, myths get in the way. The growth in services is a statistical artefact; the real economy has not changed. Services privatisation, regulatory reform and trade liberalisation are framed by ideological debates rather than economic analysis. Services are low wage and low skills, and services productivity growth is non-existent or unmeasurable. Policies and programs that support services industry growth and innovation are simply picking winners and rent seeking – an extension of bad agricultural and manufacturing policies. In recent years new and more accurate national account structures have been developed. They have been implemented in the US, endorsed by the UN and the OECD3 and are likely to be implemented in many countries. Analyses based on these new structures and data sets are now solving mysteries about growth productivity that long puzzled economists. But weaknesses in the previous structures led to measurement errors that generated the puzzles and fed and sustained the myths. Even while the effects of old measures live on; new more ambitious measurement frontiers are being been opened up. These frontiers focus on the relation between economic measures and wellbeing. There are active work programs at the OECD and in the US and the EU on improving GDP as a measure of national wealth and welfare, and on accounting for a new range of intangibles such as the value of innovation, impacts on the environment, societal conditions and
1

2

For a definition of services see the end notes. ABS, 1301.0 - Year Book Australia, 2009–10, Derived from Table 15.1 INDUSTRY GROSS VALUE ADDED AND
Dale W Jorgenson (Ed) 2009 The economics of productivity Edward Elgar Publishing Ltd 2009

GROSS DOMESTIC PRODUCT
3

dimensions of wellbeing including employment, equity, health, leisure, security and happiness.4 These activities and the changes to national accounts are instances of emerging and significant, if little noticed, service innovations. Despite the myths it is increasingly understood that the 21st Century will reward those countries with the most dynamic, productive and internationally engaged services sectors; and a rush of countries in our region including India, China, Malaysia, Singapore and Korea are developing policies reflecting that belief. The increased value of services in the global economy is of enormous potential benefit to Australia. We have long had sophisticated services sectors such as architecture, education and engineering. Leading practitioners from these sectors are known and respected internationally. The tyranny of distance for services is less significant; Australia’s Asian time zone and regional people-to-people linkages are a real advantage. It is time to dispel the myths that are preventing us from taking action to secure and strengthen Australia’s place in the Asian region and the global services economy.

4

The OECD has a Global Project on Measuring the Progress of Societies, there is a staff report from the US Bureau of Economic Analysis on GDP and Beyond: Measuring Economic Progress and Sustainability April 2010, In 2007 France commissioned Joseph Stiglitz to lead an international research project Report by the Commission on the Measurement of Economic Performance and Social Progress, 2009

1. Services are non-productive – they don’t create wealth
The idea that the production of goods contributes to national wealth while services are ancillary and, more often, frivolous and insubstantial; dates back at least as far as Adam Smith’s Wealth of Nations. He wrote, decisively and without qualification:
The labour of some of the most respectable orders in the society is, like that of menial servants, unproductive of any value.... The sovereign, for example, with all the officers both of justice and war who serve under him, the whole army and navy, are unproductive labourers. … In the same class must be ranked, some both of the gravest and most important, and some of the most frivolous professions. … Like the declamation of the actor, the harangue of the orator, or the tune of the musician, the work of all of them perishes in the very instant of its production
5

Smith’s assertions reflected the dominant thinking of his time, but aspects of this thinking continue to influence public discourse and public policy, even though the importance of intangible resources in modern knowledge based economies is widely reported. On this subject Smith’s logic was flawed. For example, accepting for the sake of argument his proposition that the performance of music leaves nothing of value; how could it be that the production of a musical instrument creates wealth when the only use of the instrument is to perform music? The very significant economic impact of the intangible is well illustrated by Adam Smith’s primary contribution to economic thought, the concept of the ‘invisible hand’ in the operation of markets. His concept has made a greater and more lasting contribution to global wealth than the work of scores of production workers. The supposedly non-productive/non-wealth creating attributes of services was also a feature of the early 20th Century “Economic Base Model”, which combined the view of services as being unproductive with a heavy dose of mercantilism. In its original form this theory postulated that the wealth of a nation was entirely a result of the competitiveness of its goods exporting industries and that economic activity within the region, especially consumption of services, merely reflected the wealth created through the export activity. Never accepted by mainstream economists on theoretical grounds6; economic base theory attained adherents in regional planning and geography, with an active literature continuing into the late 20th century. More modern variants accepted that export services could be included as part of the wealth creating base, but continued to insist that the bulk of services activity was consumption and non-productive of wealth. Economic base thinking also influenced international aid and economic development policies for poor nations. Until recently, poorer nations were urged to focus first on developing their agriculture sector, then their manufacturing sector and the services sectors last – if at all. Inefficient, labour intensive services were accepted, if not valued, for their role in job creation. The contemporary understanding of the link between the productivity of the services sectors and national wealth and welfare is only beginning to be reported in the development literature.7

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Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations Book II, Chapter III, Of the Accumulation of Capital, or of Productive and Unproductive Labour, 1776 6 A number of empirical studies further debunked the theory. See Andrew C. Krikelas, Why Regions Grow: A Review of Research On the Economic-Base Model, Economic Review, Federal Reserve Bank of Atlanta, July/August 1992, pp. 16-29 www.rri.wvu.edu/WebBook/Schaffer/Chapter%203%20S11%20for%20WVA.pdf 7 See for example, Ghani, Ejaz and Homi Kharas (2010), “Service Led Growth in South Asia: An Overview”, in Ejaz Ghani (eds), The Service Revolution in South Asia, Oxford University Press, India.

Although departed from academic research, economic base theory continues to influence public policy, providing a rationale for some of Australia’s annual industry assistance which the Productivity Commission estimates at over $17 billion in gross terms.8 This influence is but one of many examples of lags and overhangs in economic activity, research and policy making that feed the myths exposed in this paper. In this case, industry assistance policies shaped by the economic base theory thrive long after the theory has been discarded and virtually forgotten. Christopher Lovelock in his 2004 review of the economic history of services notes that: “by the mid-twentieth century, most economists... tended to dismiss the distinction between productive and unproductive labour as irrelevant and obsolete. In particular, economists came to see the ultimate end of economic activity as consumption rather than capital formation, thereby validating the economic contribution of services that could be sold at a price because they offered consumers value-in-use.”9 The importance of services to national wealth is illustrated by economic historian Stephen Broadberry (2006) in his analysis of the comparative economic performance since the 1870s of Britain, the United States and Germany. He shows that it is differential trends in service sector productivity, not manufacturing, that explains most of the movement in comparative performance.10 In particular he shows Britain was economically ahead of the US and Germany in 1870 only because of its lead in services. By 1890, the US had caught up economically and subsequently forged ahead through improved services productivity. According to Broadberry the US gained and grew its lead in services productivity over Britain through the more rapid uptake of new 11 management and organisational methods , exemplified by Henry Ford’s production line. Errors of interpretation have arisen in analyses of economic growth and productivity as a result of limitations associated with the length of the periods analysed, even for periods spanning several decades. Broadberry’s findings are significant because of the long 150 year period that he studied. Later in this paper we challenge myths about service productivity and innovation with recent evidence of the real levels of service productivity and the potential for continuing productivity growth. But even as service sector productivity grows, levels are likely to remain lower than for the manufacturing sector. Services will always seem inferior if productivity alone is taken as the ultimate measure of wealth. The question is whether productivity and GDP per capita should be the ultimate measures. The price of cars, clothes, furniture and home electronics have dropped significantly as a result of productivity gains in manufacturing. A growing number of consumers are satisfied with the goods that they have. They gain little marginal benefit from buying more, or better, goods – and prefer to spend their money on services ranging from fast food, fine restaurants, online dating, gambling, investments, travel or education. Whether consumers invest in improving their human capital or their financial capital or in entertainment and leisure, the shifts in consumer spending remind us that productivity is not the ultimate measure of what consumers and citizens want. When material needs are met sufficiently well; services, supported by technology and manufacturing, become the focus on consumer interest and the main source of economic value and growth.
8 9

Productivity Commission 2009, TRADE & ASSISTANCE REVIEW 2007-08

Christopher Lovelock, Evert Gummesson 2004 “Wither services marketing? In search of a new paradigm and fresh perspectives” Journal of Service Research 7 (1), 20-41. 10 Stephen Broadberry 2006, Market Services and the Productivity Race, 1850-2000: British Performance in International Perspective, Cambridge University Press 11 Chandler, Alfred 1990, Scale and Scope: the dynamics of industrial capitalism, Harvard University Press, Introduction

The belief that services are not-productive may be rooted in a deep awareness that food, water clothes and shelter are vital necessities of life. However given healthy economies the broader question is not whether services are “productive” but what mix of economic activity consumers want and value, and how resource availability12 will impact on those wants and needs. A related question is what people as citizens, members of communities and societies, want. The issue of attending to ends as well as means and the links between productivity and wellbeing, has attracted serious attention in recent years. Perhaps the biggest challenge in these areas, whether one is measuring service productivity, innovation or the links between economic activity and wellbeing; is the task of developing elegant measurement models, enabling efficient and cost effective data collection, analysis and reporting. Joseph Stiglitz had made a major contribution and focused international attention on the issue as Chairman of the Commission on the Measurement of 13 Economic Performance and Social Progress, set up by the French President. In his report Stiglitz provided a framework and an agenda for improving the statistical information on the links between economies and societies. One of his central themes, and a theme of this paper, is that we should not only measure what is easy to measure, things that have a monetary value, but also measure what is most important for our economic wealth and social wellbeing. Stiglitz suggest several ways of dealing with the deficiencies of GDP as an indicator of economic performance; including more use of well-established indicators.14 He also discusses quality of life measures under the headings of health, education, personal activities, political voice and governance, social connections, environmental conditions, personal security and economic security. Services have a major role and contribute to our wealth in all of these areas.

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The potential impact of “peak oil”, dramatic rises in the price of fuel, transportation and travel expected within a decade as demand for oil outstrips global supply, is one of a number of major factors bearing on economic and service sector policies. 13 Commission on the Measurement of Economic Performance and Social Progress Draft Summary June 2, 2009
14

For example he proposes net national disposable income, and net rather than gross, domestic product ibid, pp. 11- 13

2. Essential services must remain public services
A major trend over the past thirty years has been privatisation of public services coupled with a loosening of the rules controlling service delivery – deregulation. Functions formerly delivered by government are now delivered by business; and changed regulations encourage competition by providing businesses with greater operational flexibility and freedom to innovate. Virtually none of the “essential” services in Australia have been immune to this trend, eg power, water, education, health, finance, communications, transport, housing, culture, law and security. The task is not complete, the Productivity Commission estimates that the further reform dividend from competition policy and red tape reduction per year could add a further $17 billion or 1.8 per cent to the Australian economy.15 Many of the proponents of privatisation and deregulation (eg Thatcher and Reagan) were motivated by an ideological belief in the virtues of smaller government, the inherent superiority of private enterprise and the importance of personal freedom and individual responsibility. Critics of this ideology point to “a steady erosion of social cohesion as competition and the naked pursuit of self-interest invade more and more of our social life, with obvious winners and losers”.16 The weaknesses of these old ideological positions are apparent. For example, why might garbage collection be more essential than the supply food and clothing? Were more jobs created with the tax payers money used to protect the shrinking textile industry over the pat twenty years? What value was there in keeping engineers confined to public works departments serving highly cyclical regional markets? Australia is now home to half a dozen international engineering firms in the global top 100, largely founded by ex-public works engineers. Yet ideology rather than evidence still frame public debates around services considered to be essential. Australian businesses are growing and exporting health and education services, even though most markets in these sectors are dominated by government. Indeed the role of the Australian government in establishing an appropriate market, payment systems and incentives for a range of public and private institutions to attract foreign students has been a major factor in the success of Australian international education. Australia leads the world in this business as proportion of GDP. In many countries, education institutions cannot charge sufficiently high fees and/or retain a sufficiently high proportion of those fees to provide much of an incentive to attract foreign students. The financial sector is often caricatured as the exemplar of excessive greed and extreme capitalism, and the global financial crisis has given credence to this image. There is evidence that the speed of globalisation of financial markets had outpaced regulation and the crisis had drawn attention to the moral hazards17 that were always present. But it is important to view this industry in context. The overturn of the medieval strictures against usury in Europe that culminated in the financial revolution of the 17th century in Holland and the UK created capital markets with interest

15 16 17

Gary Banks 2008, Riding the third wave: some challenges in national reform, opening plenary session of the Melbourne Institute Economic and Social Outlook Conference, „New Agenda for Prosperity‟, Melbourne, 27 March 2008

Quiggin, J. (1997), 'Economic rationalism', Crossings, 2(1), 3-12.

Moral hazard occurs when a party insulated from risk may behave differently than it would behave if it were fully exposed to the risk

rates almost an order of magnitude lower than had previously existed.18 This spurt of European financial innovation was developed by business, preceded the industrial revolution, and arguably was as significant as the revolution itself.19 In part as a result of Australia’s success in weathering the global financial crisis, a bipartisan view has emerged that a sound regulatory framework is essential for the efficient operation of financial markets. This suggests a less ideological, more empirical approach to the operation of “essential” services markets.20 The focus on the role of ideology in services privatisation and deregulation in the 1980s and 1990s also has tended to obscure the enabling role of new technology.21 For example, the establishment of electricity markets was in a practical, efficient sense not feasible until the 1990s. Looking ahead, when consumers have better means, such as smart meters, to vary their consumption in relation to price signals, the major theoretical benefits of electricity markets are likely to be achieved. The Australian Government’s agreement on health and hospital funding with most of the States and Territories - to introduce activity based funding, the use of the Independent Pricing Authority, and the funding relationship with local hospital networks – is in line with recommendations made by the Productivity Commission for the greater use of the market in the structural reforms of our health system. The development of ehealth systems, such as electronic personal health records will facilitate greater use of the market in delivery of health services in ways that previously would not have been cost effective. How a greater role for the market is to be introduced into the delivery of health services is a current debate.

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Productivity Commission modelling suggests for every 1 per cent decline in the productivity of financial services, and 1 per cent rise in its cost, global output would be 0.5 per cent lower than otherwise (equivalent to $350 billion), Gary Banks, Back to the future: restoring Australia’s productivity growth, Presentation to the Melbourne Institute Economic and Social Outlook Conference, ‘The Road to Recovery’, 5 November 2009 19 Niall Ferguson, The Ascent of Money: A Financial History of the World. New York: Penguin, 2008 20 The general issue of government regulation was explored in depth at a conference in 2008, published in Government and Markets: Toward A New Theory of Regulation eds Edward Balleisen, David Moss Harvard University, Massachusetts, November 2009 21 Robert Reich writes: “They *neoliberals+ did not cause the shift; at most, they legitimised it” p12 Supercapitalism: the transformation of business, democracy and everyday life Scribe Publications 2008.

3. The growth of services contribution to GDP is a result of reclassification, not substantive changes in the real economy
A further persistent myth is that the observed growth in the services sectors is, in effect, artificial resulting from statistical reclassification. The fact that the services parts of the economy have become more important in generating wealth was rarely considered until recently in the economic and business literature. As shown in the Appendix, the growth and changing nature of services has changed the structure of the economy, the way we do business and the methods recently developed to measure economic activity. Proponents of the reclassification view point to the growth in outsourcing of services related to goods production. For example, manufacturing companies over the last twenty years have increasingly outsourced functions such as accountancy, cleaning, security, marketing to specialist services company firms. They consider that a number of large companies such as Dell and Billabong that national statistics offices define as wholesalers (a services sector) should be classified as manufacturers. Some have argued that “correcting” such classifications could account for most of the reported rise in the services sectors’ share of the economy. The magnitude of undertaking this reallocation task and the definitional complexities makes this an essentially untestable proposition, but more fundamentally it is the wrong proposition. The key issue is why these services functions have increasingly become performed separately from manufacturing (and agriculture and mining) functions, and what this indicates about the nature of the contributions the sectors to which they have shifted to the value adding process of various supply chains. A European economic study detailing the sources of productivity growth in the business services sector is included in Appendix 1 because it explains the changes. Several manufacturing sectors illustrate this point. Music publishing used to be classified in national accounts as manufacturing - originally the manufacturing cost was greater than the services component −payments to authors, distribution and marketing, and so on. With records becoming CDs and then migrating to iTunes and other internet delivery modes the manufacturing dimension from music publishing has largely disappeared. As a result this economic activity is now increasingly located in the Information Media and Telecommunications Industry Division of ANZSIC 2006, and not the Manufacturing Division. With the rise of Kindles and iPads the same process is occurring in book publishing. The reclassification involved is not artificial; the manufacturing component has simply withered away. Pharmaceuticals are another manufacturing sector that illustrates the increased contribution from services to the value chain. Most of the profit and turnover of the large pharmaceutical companies comes from the drugs on which they hold exclusive patent rights to produce. In turn the success of these drugs is dependent on the company’s R&D effort, its product assurance and efficacy testing, its regulatory approval and pricing negotiations, its marketing and distribution and its management of product liability issues. The manufacturing value-add and cost dimension of most modern drugs is a relatively small part of the total value chain. These points are well illustrated by the “smiling curve” used by Acer Computer’s CEO Stan Shih to convince Taiwan’s President Chen Shui-bian to permit Acer laptops to be manufactured in China.

Figure 1 Source: Business Week International online extra, May 16, 2005, Stan Shih on Taiwan and China

Mr Shih said: “Hollowing out of tangible things is not critical. Hollowing out of intangible things is really critical.”22 The value chain of Apple’s iPod has been well analysed, and illustrates Stan Shih’s curve. Every iPod sold for $299 in the US increases the reported trade deficit with China by about $150 − the factory cost of the iPod plus the cost of shipping. Yet the value added to the product through manufacture and final assembly by a Taiwanese manufacturing services company operating in China is $3. The $299 handed over by each customer is shared between Apple ($80) and US retail and distribution ($75), with the Japanese, Taiwanese and Korea component suppliers gaining between then margins totalling around $44 above the $100 cost of inputs (labour and goods), including the $3 for assembly.23 The increased contribution of services to business is also illustrated through the recent history of IBM which has transformed from being a predominantly manufacturing company to being a predominantly services company, with the combined value of services and software reaching 78 per cent of IBM’s worldwide revenue in 2008, see graph overleaf.

22

Business Week International online extra, May 16, 2005, Stan Shih on Taiwan and China http://www.businessweek.com/magazine/content/05_20/b3933021.htm 23 Greg Linden, Kenneth L Kramer and Jason Dedrick; “Who Captures Value in a Global Innovation Network? The Case of Apple’s iPod” VOL. 52 No. 3 March 2009 COMMUNICATIONS OF THE ACM

Figure 2: IBM Worldwide Revenue - by Segment 70 60

50

64%

Services & software accounted for 78% of IBM‟s revenue in 2008

57%

Percent

Hardware
40 30 20 10 0
'90
Software revenue exceeded hardware revenue for the first time in 2008

Services
16% 14% 6%
'91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03

21%

Software
19%

Other
'04 '05 '06

3%
'07 '08

Year
Source: IBM Financial Reports

The debate about whether companies like Dell, CISCO, Apple, Li & Fung and Billabong are actually manufacturing companies (even though they do not for the most part manufacture any of their product) also misses the essential point that these firms’ success is not related to manufacturing value-adding, but rather to a range of services activities. Trends in our mining industries are generating changes in some areas that have yet to be reclassified as professional services rather than mining. The professional work of Australia’s mining companies is 25 26 increasingly located overseas or associated with overseas projects. This trend has intensified over the 27 past decade; for example, ASX-listed companies are now managing 928 overseas mining projects. As work on overseas projects is not associated with goods produced in Australia, it can be appreciated that an increasing proportion of the high skill, high wage jobs provided by the Australian mining companies would be more accurately classified as professional services.

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Li & Fung Group is a Hong Kong based multinational group of companies with three core businesses - export sourcing, distribution and retailing. The group provides another example of how value adding has shifted up the supply chain from manufacturing to serviced sector activities. Li and Fung “produces” more than two billion pieces of apparel, toys and other consumer items every year and owns great brands including Toys R Us. Yet Li & Fung does not own a single factory. Instead it is a “network orchestrator.” It manages global supply chains involving more than 8,300 suppliers served by over 70 sourcing offices in 40 countries and territories. The company indirectly provides employment for more than two million people in its network of suppliers, but less than 10,000 of these are on Li & Fung’s payroll. With a lean structure, each of the company’s employees generates about US$1 million in sales, earning a return on equity of more than 38 precent per year. From: Victor K. Fung, William K. Fung, and Yoram (Jerry) Wind COMPETING IN A FLAT WORLD: BUILDING ENTERPRISES FOR A BORDERLESS WORLD.

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Oliver Mapongaa and Philip Maxwell 2000; The internationalisation of the Australian mineral industry in the 1990s Resources Policy, Volume 26, Issue 4, December 2000, Pages 199-210. 26 An indication of the significance and scale of Australian offshore mining investment is provided by the UNCTAD’s World Investment Report 2007: Transnational Corporations, Extractive Industries and Development which reports employment associated with the foreign affiliates of Australian mining companies as being 322,000 in 2002; total Australian mining employment was around 85,000 in 2002 (ABS6203.0) 27 DFAT 2010 Review of Australia’s Relationship with the Countries of Africa, Submission to the Joint Standing Committee on Foreign Affairs, Defence and Trade, Inquiry into Australia’s relationship with the countries of Africa, 17 March 2010, http://www.aph.gov.au/house/committee/jfadt/africa%2009/subs/Sub%2046.pdf

4. Most service sector jobs are low skill and low wage
As the services sectors provide jobs for 84 per cent of Australians in employment, it is true that these sectors provide many low skill and low wage jobs. Its role at the low skills end of the spectrum attracts attention because of the importance of increasing participation in employment. It is less well known that the service sectors provide most of the high wage and high skill jobs; see table 1. In 2009, 93 per cent of Australians with qualifications equivalent to a university degree or above were employed in the services sectors.28 The better salaries and employment outcomes achieved by university graduates in Australia (which have been sustained despite the substantial growth in the proportion of Australians with degrees) primarily reflects the increased demand over the past twenty years by the services sectors for their skills.
Table 1 Australian Workforce and Graduate Distribution, and average salaries by industry, 2009

Industry Agriculture Mining Manufacturing Electricity, Gas, Water and Waste Services Construction Wholesale Trade Retail Trade Accommodation and Food Services Transport, Postal and Warehousing Information Media and Telecommunications Financial and Insurance Services Rental, Hiring and Real Estate Services Professional, Scientific and Technical Services Administrative and Support Services Public Administration and Safety Education and Training Health Care and Social Assistance Arts and Recreation Services Other Services All Industries

Workforce proportion of all industries (percent) 3.3% 1.5% 9.2% 1.2% 9.0% 3.9% 10.9% 6.8% 5.2% 1.9% 3.7% 1.7% 7.6% 3.4% 6.2% 7.6% 11.1% 1.8% 4.1% 100.0%

Graduate plus grad dip. or above in industry workforce 6% 21% 14% 27% 7% 15% 11% 9% 10% 35% 41% 18% 50% 18% 36% 64% 36% 25% 11% 25%

Average Weekly Salary Not surveyed $ 1,999 $ 1,199 $ 1,482 $ 1,380 $ 1,213 $ 950 $ 905 $ 1,238 $ 1,476 $ 1,433 $ 1,258 $ 1,466 $ 1,233 $ 1,338 $ 1,288 $ 1,236 $ 1,107 $ 1,038 $ 1,277

ABS 6302.0 - Average Weekly Earnings, Australia, Nov 2009; 6278.0

Several services sectors lead in terms of both rates of pay and employment of university trained staff. These sectors include Information Media and Telecommunications, Financial and Insurance Services, and Professional, Scientific and Technical Services, which in total employ 13 per cent of those Australians in employment and over a quarter of all graduates.

28

ABS 6278.0 – Education and Training Experience 2009

Manufacturing in Australia is a relatively low paid and low skill sector. As shown in table 1, both its level of pay and employment of university graduates are well below the average for all industries and the average for services industries. It provides jobs for 9.2 per cent of those Australians in employment. Mining is the industry sector with the highest rate of pay, but it is a relatively small employer with just 1.5 per cent of Australians in employment. Many of the jobs in the mining sector are difficult, dangerous, dirty and remote; attracting high salaries even though the formal skill level is lower than most other sectors. The high salaries in the mining sector are also related to the increased proportion of graduate and above degree people in the mining workforce. At 21 per cent it is three times that of construction which might be thought of having similar workforce needs, and a third higher than manufacturing.29 Technical mining professionals with degrees are among Australia’s highest paid professionals, commonly attracting salaries above $150,000 in positions advertised on the My Careers website.

29

By comparison the proportion of graduate and above employment in the US mining sector is 18%, which is half the all industries average (36%), similar to construction (13%) and a third less than manufacturing (27%) US Census Bureau Educational Attainment in the United States: 2009.

5. Expansion of the service sector drives down productivity growth
THE MYTH MAKER In 1967 economists William Baumol noted that the productivity of a string quartet was the same as a century before and that it was difficult to think of ways to improve its productivity.30 He argued that, in contrast with manufacturing sectors, which he called “progressive”, labour intensive services were “stagnant” because their potential for productivity gains was very limited.31 He predicted that as the service sector’s share of the economy increased, prospects for economy-wide productivity gains would diminish. His model and hypothesis, which became known as Baumol’s “disease”, seemed to explain the productivity slowdown in the 1970s and early 1980s. In 198532 Baumol strengthened his case by revising his model and classifying services as progressive, stagnant or “asymptotically stagnant”. In doing so he acknowledged that some service sectors, such as telecommunications, achieve high and spectacularly high productivity growth, while continuing to argue that most are stagnant. He contended that high productivity growth was possible in telecommunications precisely because no personal contact was involved. He labelled services such as R&D, that use “some inputs (computers) from the progressive sector and some inputs (the intellectual work of researchers) from the stagnant sector” as hybrid and asymptotically stagnant33. In 2007 he rounded out his revised model by forecasting that the growing costs of R&D labour will eventually reduce demand for these services34. Baumol still considers health and education to be low productivity growth sectors because the work of teachers, academics, doctors and nurses is characterised by the “handicraft attributes of personal contact”35. He continues to promote his hypothesis and it in turn exercises a wide influence on the thinking of columnist, economists and policy makers36. For example, in a recent Age article John Legge wrote: “The possibility of raising the productivity of many activities, including many services, is limited. Manufacturing, on the other hand has unlimited potential.” Legge continued: “It is misleading to talk about a move from manufacturing to a service economy: what has really been happening is the rise of software”37. Baumol’s model appeals to common sense and has cast some light on productivity growth trends for half a century. One cannot dispute that the labour involved in string quartet performances, academic research and in the personal service fields of child care, teaching and nursing either should not be, can’t be or can’t easily be automated. As a result, the productivity growth resulting from many decades of automation of agriculture, mining and manufacturing (capital deepening) is not
30

Baumol, W. 1967, ‘Macroeconomics of unbalanced growth: the anatomy of urban crisis’ American Economic Review, vol. 57, no. 3, pp 415-26. 31 This brings to mind the scene in Notting Hill when Bernie the gauche friend of the Hugh Grant character commiserates about the worthy nature of acting and poor pay with the Julia Roberts character, Anna. Bernie: What's the pay like in movies? I mean, last movie- how much did you get paid? Anna: 15 million dollars. 32 William J. Baumol (with Sue Anne Batey Blackman and Edward N. Wolff) "Unbalanced Growth Revisited: Asymptotic Stagnancy and New Evidence," American Economic Review, Vol. 75, No. 4, September 1985, pp. 806-817 33 Ibid page 811 Examples in brackets were inserted by the writers.
34

William J. Baumol, “On Mechanisms Underlying the Growing Share of Service Employment in the Industrialized Economies,” Chapter3 in Mary Gregory, Weimer Salverda, and Ronald Schettkat, eds., Services and Employment: Explaining the U.S.-European Gap, Princeton, NJ: Princeton University Press, May 2007 Preface, page 10 35 Ibid page 9
36

Baumol quoted by Herszenhorn, D. M.; For Ailing Health System, a Diagnosis but No Cure, The New York Time, 17 January, 2010. prescriptions.blogs.nytimes.com/2010/.../an-economist-who-sees-no-way-to-slow-rising-costs/ 37 John Legge “Without support for industry, we’re no more than a quarry” The Age p 21 Friday August 13 2010

matched in these service sectors. Developments pointing to capital deepening and automation of these services are discussed later in this paper. The first problem with Baumol’s formulation is that, aided and abetted by input, output, price, and therefore productivity measurement errors, his model misleads with deceptively simple and persuasive explanations. The second problem is that it leads directly to the view that is nothing that can be done in terms of policy to foster productivity growth in the “stagnant” service sectors. Baumol’s construction ignores the accelerating impact of new knowledge, embedded in human capital (skills) and technology (hard tools and soft methodologies) on services. For example, the ICT revolution has made it possible to significantly lift the productivity of a string quartet, disproving Baumol’s original example. A real world example is provided by the band that plays every Saturday at the Melbourne dinner dance restaurant, Rasputin. Three performers, a singer and drummer and a pianist, who also “plays” a small computer sitting on his keyboard, generate the sound equivalent to that of a ten to fifteen piece orchestra. Today’s music lovers can also choose from a global store of digital music. Such online retailing illustrates the dramatically increased productivity of the distribution service sector. Poincare wrote that "Science is (made of) facts, just as houses are made of stone....but a pile of stones is not a house, and a collection of facts is not necessarily science."38 Baumol has made significant contributions, particularly to our understanding of entrepreneurs and innovation, but his service productivity model is more like a house than a collection of stones, but in the field of economic growth research it is a dominating house with a strikingly attractive facade that belies shoddy workmanship, structurally weaknesses and crumbling foundation stones. It’s an old house that needs either demolition or renovation from the ground up. SERVICES, INTANGIBLES, KNOWLEDGE AND PRODUCTIVITY Over the last 35 years there has been a dramatic decline in the contribution of manufacturing labour productivity growth to total productivity growth in Australia. The contribution dropped from 82% to 42% while the contribution of services sector labour productivity growth grew commensurately by 40%.39 There are similar if less dramatic trends in Europe and the US.40 However contributions are not only a result of productivity; they are also, and in this instance, mostly, a result of the relative size of each sector. With services now accounting for around 75% of economic activity across OECD countries, these changes are not surprising. However, trends and improvement in measurement methods are gradually narrowing the manufacturing – services gap. To understand why the manufacturing-services productivity gaps have bolstered the myth for decades we need to identify and measure drivers of productivity and growth at the sector level. Doing this (at any level) is rather like unscrambling a dish of scrambled eggs. We may not be able to physically unscramble the dish but we can define the component parts, get a sense of their relative importance and the way they interact and work together.

38

Poincare, J. H. (1903) La science et l' hypothese [The science and the hypothesis]. Paris: E. Flammarion. Own calculations based on data from Bart van Ark and Pieter Woltjer (Eds) The EU KLEMS Productivity Report Issue No 2 2008 Groningen Growth and Development Centre University of Groningen . 40 For example studies of US ICT using industries showed that since 2000 the major contribution to productivity growth has shifted from manufacturing to services and has been concentrated in just eight service and trade sectors. Similar trends and concentrations were found in France, Germany, the Netherlands and the UK, but the levels of investment in ICT and the rates of growth were lower in the mainland European countries. See Jorgenson D (Ed) Economics of Productivity 2009
39

A service is defined by what it is not – an intangible product that is not a good. Services and service characteristic elude easy classification41 because; after agriculture, mining and manufacturing, they make up all other economic activity. The definition of service in the UN System of National Accounts 2008 while accurate is anything but simple and straightforward.42 An intangible is also defined by what it is not - something that cannot be touched, that is not solid. The term also refers to things that cannot be easily grasped, defined, measured or assessed; a point that explains some of the difficulties measuring services. Valuable intangibles that are owned, used, bought and sold are called intangible assets.43 They are of growing importance. Since 1960 the ratio of intangible capital of total capital in Australian public companies grew from 10% to over 22%. 44 In essence the defining characteristic of services; the thing that truly distinguishes service sectors from goods producing sectors, is the intangibility of their output. In accounting, intangible assets are, by definition, stocks while their use, for example in service production and delivery, is measured as flow. The significance of intangibles, measured as the ratio of book to market value has long been recognised in the finance and investment community. In recent decades quite detailed frameworks for measuring intangibles have been proposed and used to augment financial accounts.45 In national accounting systems, measuring the outputs of services is difficult; and has not yet been mastered.46 As the result there are large gaps in the data needed for estimating service productivity. Nevertheless, the impact of intangibles has been estimated at industry sector and national levels. Knowledge is the common and core element of intangible assets and of most services.47 Knowledge is the major element in all but five of the twenty nine intangible assets listed by the US Financial Accounting Standards Board in SFAS 141.48 Knowledge can be in forms including data, information, brands and reputation, legally recognised and protected intellectual property, skills, proprietary knowhow, formal intellectual property and contracts.

Knowledge is what we know, think and believe. It’s made up of fact and belief.49 Knowledge is the core ingredient of skills or know-how; the major components of human capital.50 While ultimately all knowledge originates from people; a quantum remains “embodied” in each person. Even so;
41

The variety of services is so great that one can easily find flaws attempts to list service characteristics. For example, some writers claim that (1) no transfer of ownership takes place when services are sold, and that services (2) cannot be stored or transported, (3) are instantly perishable, and (4) come into existence at the time they are bought and consumed. However, when a builder builds a house for me, I take ownership of the completed house, which is (hopefully) anything but instantly perishable. Data in databases and e-books can be moved around the world at what seems like the speed of light. Finally, an accounting report can be read and re-read for years after the accounting work is completed and paid for.
42

UN SNA 2008 , Section 6.17 43 Lev notes that an “intangible asset is a claim to future benefits that does not have physical or financial (a stock or a bond) embodiment.” Lev, B. Intangibles: Management, Measurement, and Reporting. Brookings Institution Press, Washington, D.C., 2001
44 45 46

Webster Elizabeth THE WEALTH OF NATIONS: WHERE DOES IT COME FROM? Talk at the Clever Collections Conference, November 2007 The actual period was1960 to 1997

The models are summarised in Thomas A. Stewart Intellectual Capital The New Wealth of Organisations Doubleday 1997 The key problem is that while price data is easy to collect, service volumes are difficult to establish. 47 For example, knowledge dominates educational and government services, but shares a place with tangible inputs and outputs in the construction sector and in the hotel and restaurant sector. 48 Knowledge is not or may not be central in five types of contract based intangible assets: licensing, royalty, standstill agreements, lease agreements, construction permits, operating and broadcast rights and finally, use rights such as drilling, water, air, mineral, timber cutting, and route authorities.
49

One of the major debates in the field of epistemology concerns the ultimate sources of knowledge – in black and white; are we born with some innate knowledge or do only we come to know things through observation? Regardless of where the balance lies it’s a fact that people create and produce knowledge and that they have been doing so for centuries. We might speculate as to whether the ratio of fact to belief can and does change, and if so whether we should be optimistic or pessimistic about evidence based policy. 50 Human capital is the health, strength, education, training, and skills that people bring to their jobs, hereafter, skills. In growth accounting Human Capital is currently treated as a component improving labour quality, rather than an independent factor of production. Education levels which are measurable are used as the main indicator of useful knowledge in economies.

knowledge is most commonly recognised in its seemingly more tangible form, as the contents of books and publications and databases. Knowledge is also embodied in goods through their design, constituent materials and production processes.51 At a fundamental level, there are tangibles and intangible components in both products and services. Services are not pure intangible and agricultural, mining and manufactured products embody and “store” knowledge. However, with this caveat, the intangible content of services is far, far greater than that of goods. Economic growth involves reallocating resources – tangible and intangible – in ways that are more valuable. Growth occurs when economies become smarter and more efficient; as distinct from simply expanding because more people work longer (increasing labour input) and consume more (increasing GDP). Productivity growth is a result of increases in the productivity of labour, capital and other factors, primarily but not only new technology. We want to keep technical language to a minimum, but will spell out these factors. The most common measure of labour input is hours worked. Labour productivity growth is the result of people working harder (but not longer) and smarter, aided by smarter tools and methods. The contribution of human capital, the skills that educated people bring to their work, which is not picked up in hours worked, is now is recognised in standards and can be measured as quality adjusted labour input. Capital input is a measure of the rate at which investments in land, plant, machinery and equipment is used, and used up. In accounting the using up rate is measured as depreciation. Capital productivity is a measure of the efficiency with which the capital is used to produce goods and services. Capital productivity is a result of the interaction between investment levels (more investment leads to capital deepening), contributions from new technology in new vintages of capital, economies of scale, capacity utilisation and labour inputs. In oversimplified terms, if a worker uses a machine for more (less) hours over a period of time, the capital productivity of the machine increases (decreases). During the last 25 years increasing labour costs have led to reductions in labour inputs and increases in capital inputs, triggering declining capital productivity. Labour and Capital by themselves do not explain productivity growth. To cover the remaining influences economists use the somewhat misleading term Total or Multiple Factor Productivity (TFP/MFP) − the intangible influences on labour productivity, such as new ways of organising, improvements in efficiency and new technology.52 TFP growth results from:53
      

firms applying new knowledge available in science and technology publications; firms copying what other firms do, including the “best practices” of leading firms the “leakage” of valuable knowledge and ideas from one firm to other firms through staff movement and informal contacts; imitation of innovations by firms through reverse engineering and simply stealing ideas commercial technology transfer; all training and learning-by-doing that occurs in firms network effects or externalities. For example, an Internet user stands to benefit as an increasing number of their friends start using and communicating on the Internet.

These examples illustrate the fact that knowledge can be spilt-up, copied, carried and moved around both in formal commercial contracts and informally by people; such as when managers from different firms or agencies swap information over lunch. The movement and leakage is called knowledge spillover – because valuable knowledge, unlike indivisible goods, gets used in many ways outside its original intended use. The very existence of cities demonstrates the power of knowledge
51 52

Production here covers agricultural production, mining and manufacturing.

Michael J. Harper, Bhavani Khandrika, Randal Kinoshita and Steven Rosenthal Nonmanufacturing industry contributions to multifactor productivity, 1987–2006 BLS Monthly Labor Review June 2010 53 This list is based on John Revesz, Harvey Anderssen and Dr Lee Boldeman “Productivity growth in service industries” DICITA Occasional Economic Paper, April 2005

spillover. Why do people live together in expensive cities when they could live on cheap land in the country? Because the knowledge spillovers that can take place when people are concentrated in cities is the intangible glue that leads to significantly higher productivity54 that makes living in cities more rewarding. TFP measures the contribution of “free floating” knowledge and economies of scale, in contrast to the knowledge that is embodied in capital and labour. TFP is generally seen as a broader measure, covering more than labour productivity. Over the long term labour productivity and TFP trends tend to track each other. WHY HAS THE MYTH LIVED FOR SO LONG First, it takes a long time to fix mistakes Conclusions, derived from theoretical models built with flawed assumptions and metrics and populated with limited data fostered the prevailing view that service productivity is doomed to lag behind productivity in other sectors. Economists were often puzzled as to why predictions based on their models were proved wrong. An equally intriguing puzzle is why some economists continued to use flawed approaches for so long. The answer involves accounting at all levels: a combination of out-dated accounting standards developed during the industrial era that still fail to treat intangibles correctly; path dependence,55 with the growth model developed in 1957 by Robert Solow an overpowering influence on the (thinking) path taken subsequently by economists; insufficient attention to the significance of the principle of requisite variety,56 and data and measurement errors in national level accounts – errors that while not especially significant in national snapshots of national economic activity become so when used in models to explain service productivity trends. Solow’s model elegantly linked three inputs: capital, labour and another unexplained “residual” factor as the causes of productivity and economic growth.57 The unexplained or “residual” factor was considered to be completely outside of the economic activity covered by the models and thought to be the rate of technological progress. Essentially, technological progress is not hostage to the limits to economic growth imposed by the diminishing returns from all physical inputs to economic activity. Diminishing returns reflect the fact that there are limits to the ability people to work harder and faster, and to the capacity and working life of machines and equipment. As physical resources are used more and more over a period of time the returns from the resources diminish. This was expressed succinctly and even proposed as an economic law, by one of the founders of neo-classical economics, Alfred Marshall: “... while the part which nature plays in production shows a tendency to diminishing returns, the part which man plays shows a tendency to increasing returns.”58 Several real world factors affect model building. There can be lags of a decade or more before new technology delivers benefits; and economic cycles and shocks affect productivity and growth trends. National accounts data, collected primarily for the purpose of calculating GDP, is aggregate and not sector level data. Consequently it limits the study of activity at sector level, and within and between
54 55 56

Not all of which is measured. Decisions and mistakes made in the past influence subsequent decisions; the “historical hangover” can be long lasting and inefficient.

In its original form, a complex system can only be regulated by an equally complex set of controls. W. Ross Ashby, "An Introduction to Cybernetics" Chapman and Hall, London 1956 Despite being highly influential, Ashby is not as well-known as other thought leaders in the field of systems theory and science. 57 Technological progress was a factor entirely beyond the parameters of economic models and as a result these models became known as exogenous models that recognised a factor entirely beyond economic activity; as seen through the economist’s models. Later models that incorporated technological and other change are known as endogenous models). His influence is demonstrated by the fact that his less than intuitive term for technological progress, Total Factor Productivity or TFP is still used to describe the rate of technological progress.
58

Marshall, A. Principles of economics (8th Edition), Macmillan and Co., Ltd. 1920 Book 4, Chapter XIII, Paragraph IV.XIII.11 Retrieved 1/8/2010 from, http://www.econlib.org/library/Marshall/marP1.html Clearly, his followers picked up on some but not other parts of his thinking and writing.

sectors. Finally, even when these factors are addressed, errors in defining and measuring labour and capital at constant prices over time remain. As with all research, economic research gradually builds and improves our knowledge of the world. But not only do lags occur; “wrong turns” and flawed paradigms − long lasting world views − can limit the horizons of researchers for decades. (Following Karl Popper, all research outputs, even those with the strongest evidence, might be regarded as work in progress.) These factors have affected economic model building, testing and thinking; and contributed to the myth for decades. Just a few years after Solow’s 1957 paper some economists sounded warnings about the difficulties of and effects of measurement problems; and they have been sounded, with ever increasing frequency, by an increasing number of leading economists, ever since.59 During the 1960s other economists, particularly Griliches and Jorgenson completed productivity and growth studies using better measurement constructs but, as shown in Table 2 overleaf, despite their advocacy and warnings of others, four decades passed before their approach was taken up by national and international statistical and accounting bodies. The early appeal of the models of Solow and also Kuznets (applied to past and emerging data they seemed to made sense at the time) lured economists into studies limited by poor measurement concepts and data, even as real world growth diverged from the rates forecast by the models. As a result, economists such as Professor Peter Robinson, writing the DIISR study of Innovation Metrics,60 warns policy makers of the limitations of the models and findings based on the models. Unfortunately, flawed theories and models show up in bad policies and the policies become difficult to change. Garry Banks, citing successful tariff reforms as an example of the policy benefits of sound analysis and robust statistics mentioned that there were entrenched opposition to the reforms.61

59

Freeman, C “Continental, national and sub-national innovation systems—complementarity and economic growth” Research Policy 31 (2002) 191– 211 In this paper Freeman praises Stanford economist Irma Adelman for her foresight and courage in identifying the problems in 1963. Leading economists from the US BEA, BLS and Federal Reserve Board, the Australian Productivity Commission, the EC and OECD have written about the problems. 60 DIISR, 2009 DIISR Innovation Metrics Framework Project , 2009, Part 2
61

Ibid pages 9 and 17

Table 2 Major growth accounting studies and models of economic growth; and related improvements in national accounts 62

Tinbergen

1942

Found that growth in capital and labour inputs accounted for about 75% of growth in outputs, with productivity or efficiency accounting for about 25% in the US during the 35 year period, 1870-1914. His findings were at odds with and were completely overshadowed by later studies by Solow and Kuznets

Solow

1957

Highly influential paper providing conceptual clarity and sophistication. It integrated data and models from Douglas (1948), Tinbergen (1942), Abramovitz (1956) and Kendrick (1956) in a model (called Total Factor Productivity or TFP and later renamed multi-factor productivity or MFP by the US Bureau of Labour Statistics) that retained capital and labour as the productivity inputs and also identified technical change as the other factor contributing to productivity and economic growth. Solow measured output as NNP, labour as hours worked and capital input as real capital stock.

Dennison

1962, 1967

Used constant quality measures of labour input (age, sex & education as well as hours) in his pioneering 1967 paper “Why growth rates differ”. Quality is the term used by economists to describe the composition, with a connotation of value, of input factors. Explained productivity change using the “cleaner” concept of GNP rather than NNP as the output measure; constant quality measures of both labour and capital inputs and a new concept of the production possibility frontier replacing the aggregate production function. They also used constant quality price indices. They found that changes in quality adjusted capital and labour inputs accounted for 85% of growth during 1945-65, with only 15% accounted for by TFP growth. Their work supported Tinbergen’s findings for the earlier period that he studied. In 2009 Jorgenson, who has a chair at Harvard, wrote that the their use of constant quality measures “altered, irrevocably, the modelling of the interaction (substitution) between labour, capital and technical change” (Introduction xiii )

Griliches and Jorgenson

1967

Solow Kuznetz

1970 1971

Two papers published independently within a few months of each other reinforced the earlier work of Solow and seemed to reinforce each other. The approach presented in the papers came to dominate growth accounting, the study of economic growth, for decades. The conceptually stronger measurement concepts of Griliches and Jorgenson were ignored. This US government agency introduced a new productivity measurement framework using GNP rather than NNP as the output measure and a constant quality index of capital inputs. However it retained retaining hours as the sole measure of labour input. Seminal book reporting industry-level analysis of productivity growth in the post-war US economy.

Bureau of Labour statistics Jorgenson, et al

1983

1987

Bureau of Labour statistics

1994

The Bureau updated its framework by incorporating constant quality measures of labour and a constant quality price index. The Bureau’s 1994 estimates of MFP overturned findings of Solow and Kuznets, but more than a decade was to pass before the measures and framework were progressively prepared, endorsed and applied in international standards.. Published “Measuring Productivity” the first multinational standard, based on the BSL framework. EU funded KLEMS productivity measurement project begins using and extending the OECD measurement methods. The data bases enable measures of quantities and prices of output and of capital (K), labour (L), energy (E), material (M) and services (S) inputs at the industry level. KLEMS categories are now widely used by national statistical agencies in making productivity estimates. Applied their approach and that of colleagues to develop an improved architecture for the US national accounts. This framework also measured capital as a service, instead of capital as a stock. This prototype system is limited to economy wide aggregates and is not yet broken down by industry sector. Fraumeni, et al, describe principles, requirements and structures for sector level as opposed to national level aggregate production accounts, in Jorgenson, Landefeld and Nordhaus (eds) 2006 XXX

OECD EU KLEMS

2001 2004

Jorgenson & Landefeld

2006

Fraumeni

62

The information in this table is based on the introduction in Jorgenson, D (Ed) , Economics of Productivity, An Elgar Reference Collection, Cheltenham, UK 2009

UN Statistical Commission Oulten UN

2007

Approved the incorporation of capital measured as a service into the 1993 revision of the UN system of national accounts (SNA). The earlier exclusion of these measures was a serious barrier to the use of national accounts data to measure productivity and economic growth Showed that Solow’s model was a special case of the Griliches and Jorgenson 1967 model Released 2008 SNA, the new international standard, replacing the 1993 version.

2007 2009

The measurement issues are complex − one example will illustrate the nature of the problems. Relatively speaking, the simplest measure of labour input is hours worked. Using this measure an hour’s work by a kitchen hand is equivalent to an hour’s work by a neurosurgeon or air traffic controller. Common sense suggests that this equivalence is wrong. The 2008 SNA finally describes the problem and a solution − the construction of quality adjusted labour indices that incorporate qualifications and experience to achieve more accurate measures of labour productivity.63 Even with the new standard in place, there are long lags updating national accounts. In 2008 the OECD wrote that “Despite the progress and efforts in this area, the measurement of hours worked still suffers from a number of statistical problems.... In principle, the measurement of labour inputs should also take into account differences in workers’ educational attainment, skills and experience. Accordingly the OECD has started (our emphasis) to develop adjusted labour input measures”.64 In a similar vein, in a recent speech Australian Productivity Commission Chairman Garry Banks said that “notwithstanding the notable achievements of our independent statutory agencies in building a robust body of official statistics, we continue to face debilitating data gaps in priority policy areas”.65 WHY HAS THE MYTH LIVED FOR SO LONG 2 Intangibles are still handicapped

Models are maps. Firm level accounts, national accounts and economic growth models are maps of territories that change constantly. Accounts map the enormous diversity of stocks and flows of assets and the financial resources that pay for them; year to year fluctuations can be large and, while medium terms trends are more stable, the rates of change and the mixes are in constant flux. As scientist Alfred Korzybski observed, the map is not the territory. Accounting maps mislead, especially when they fail to cover the territory they claim to cover and they fail to measure changes correctly. Despite the improvements described above, accounts don’t recognise some intangible assets and more seriously, they mis-measure the life span and thus the value and benefits of many others. While the gap between the book value and the higher market value (capitalisation) of companies was recognised decades ago; the problem received little attention from statistical agencies and economists modelling growth. Financial accounting standards consistently under-value the contribution of intangibles, and therefore of services; and until corrections are made to the standards they are likely to continue to do so. The intangibles that are not recognised and therefore not measured include brand names, reputation, customer lists, data bases, and parts of human capital and proprietary know-how. Some of these items may not seem significant but most of us know the power and value of brands and

63 64 65

UN SNA 2008 Section 19.55
OECD Compendium of Productivity Indicators, April 2008

Ibid, Pages 9 and 17

reputation. For example, firms gain long term profitable competitive advantages from their brand names and reputation.66 Intangible assets that are recognised include67:
     
computer software patents copyrights motion picture films customer lists mortgage servicing rights

     

licenses training import quotas franchises customer and supplier relationships marketing rights

The problem is that these intangible assets are, with the exception of purchased software,68 classified as expenses, rather than assets. By being expensed their value is normally written off in one accounting period, a year, when businesses can reap benefits from the continuing use of the assets for many years. PROGRESS CORRECTING THE MISTAKES Just as there are R&D breakthroughs there are scholarly breakthroughs. Bosworth and Trippet presented their breakthrough research in conferences and papers in the years before the publication of their 2004 book, Productivity in the US Service Sector.69 They demonstrated that service sector labour productivity advanced 2.6% per annum in the six years from 1995, exceeding the 2.3% growth of the manufacturing sector. Over the same period service sector MFP jumped from 0.3% a year in to 1.5% a year.70 Bosworth and Trippet also pointed out that service sector trends were broad based. 17 of the 29 service sectors in the US industrial classifications showed high MFP growth over the period; and 24 of the 29 sectors had positive growth. They acknowledged improvements in the scope of data collected by US statistical agencies enabling them to pursue their research.71 It must be acknowledged that while service productivity trends rose faster than manufacturing trends, manufacturing productivity levels remain higher as shown by this graph (Figure 3) of trends in the US and the EU15 over 20 years.72

66

Philip Little, David Coffee, Roger Lirely, Beverly Little, Explaining variation in market to book ratios: do corporate reputation ratings add explanatory power over and above brand values? Journal of Finance and Accountancy No 2 2006
67

Accounting standards also specify how the assets can be acquired: by separate purchase, as part of a business combination, by a government grant, by exchange of assets, and by self-creation (internal generation)

68 69

Purchased software was first recognised as an asset in accounting standards in 1988 Triplett, J. and Bosworth, B. Productivity in the U.S. Services Sector Brookings Institution, Washington. 2004 70 Ibid Chapter 1, Introduction, pages 1-3 71 Ibid The also listed key factors behind the growth - deregulation, which led to a “major internal reorganisation” of rail transport, the relatively fast uptake of new ICT by the banking and finance sector and in retaining new technology (scanners and IT) and organisational changes that significantly improved efficiency 72 Douglas Koszerek, Karel Havik, Kieran Mc. Morrow, Werner Röger and Frank Schönborn, An overview of the EU KLEMS Growth and Productivity Accounts, European Economy Economic Papers, European Commission Directorate for General for Economic and Financial Affairs, Number 290 – October 2007 and Bart van Ark and Pieter Woltjer (eds) “ THE EU KLEMS PRODUCTIVITY REPORT, An Overview of Results from the EU KLEMS Growth and Productivity Accounts for the European Union, EU Member States and Major Other Countries in the World”, Groningen Growth and Development Centre, Issue no. 2, December 2008 and own calculations.

LABOUR PRODUCTIVITY GROWTH RATES

5 4 3
2 1 0 80 - 85 us manuf 85 - 90 90 -95 96-2000 US service EU15 services Figure 3 2000-05 EU15 manuf

The graph shows that the relation between service and manufacturing productivity levels has been quite stable, with manufacturing figures being about 2.5 % higher in the US and about 2% higher in the EU15 for the first decade with services closing the gap gradually in the EU15 and at a faster rate in the US. Data for Australia is expected to be available in the coming months when the PC releases its report “Investment in intangible assets and Australia's productivity growth - sectoral estimates” The stability of these trends is quite striking when seen against most comparisons of growth between sectors and across countries. The variations and fluctuations often point to factors other than labour and capital, such as government policies that explain differences in growth rates. Services may be closing the productivity gap but they are still handicapped by the productivity figures attributed to them as a result of measurement error. Recent US data shows that from 1987 to 2006 MFP growth was negative in 15 of the 40 service sectors for which data is now available.73 There are real world reasons for negative trends, such as the slow decline of mature and dying industries. However the negative figures still puzzle statistical agencies - they don’t fit with evidence of investments in R&D and ICT in the sectors. In the paper quoted here, senior Bureau of Labour Statistics economists report statistical experiments to uncover the source of the puzzles. Their conclusions, in line with those of others who conducted similar tests, suggest that that the MFP measurement error could be around 0.26%, and that actual MFP could be a quarter of a per cent higher than currently reported. They conclude “that problems may remain in measuring some outputs or that something else is responsible for negative productivity”.74 A different approach, focusing on quantifying the effect of including intangibles in national accounts, is taken by US Federal Reserve Board senior economist Carol Corrado and her colleagues.75 They develop a model that has different assumptions to Solow’s model and, in particular, include equations on how people make saving and investment decisions. Their work shows that there is no

73

MFP growth also declined in a smaller number, 28 of the 86, or 33% of, US manufacturing sectors according to the BLS News release-

MULTIFACTOR PRODUCTIVITY TRENDS FOR DETAILED INDUSTRIES , 2006. In addition the number of service sectors with positive rather than

negative or zero MFP growth has increased from 21 to 28, or 66% of all sectors, since 1995. Both of these points support our argument that the manufacturing service gap is declining. Source: Michael J Harper, Bhavani Khandrika, Randal Kinoshita and S Rosenthal, Nonmanufacturing industry contributions to multifactor productivity, 1987 – 2006. Monthly Labour Review, June Page 23. 74 Ibid, page 29
75

Corrado, C.A., Hulten, C.R. and Sichel, D.E. (2006) ‘Intangible Capital and Economic Growth’ NBER Working Papers 11948 Cambridge, MA: National Bureau of Economic Research; 2006

theoretical basis for treating intangible capital differently to tangible capital in accounts. They test their theory by identifying intangibles in the business world. Their list is shown in Table 4 below.76
Group Type/form of knowledge (k) capital Status - US National accounts Included, software is capitalised Most spending, e.g., R&D is expensed Not recognised 640
Expenditure $ Billions 99-2000

Computerized information Innovative property

K embedded in computer programs and databases K acquired through both R&D and non-scientific creative activities K embedded in firms – human capital, reputation, brands, building, improving organisational structures, training

155

425

Economic competencies

All groups

1,200

Their last step is to estimate expenditure on the intangibles from 1988 to 2000 and use their model to determine which expenditures should be classed as fixed business investments (capital rather than expenses) in their national accounting system. They find that expenditure on intangibles doubled in dollar terms over the 12 year period and increased from 10% to 13% of GDP.77 They also find that overall businesses invested in intangibles at roughly the same rate at which they invested in tangibles. Applying their estimates to national accounts they conclude that if intangibles were fully recognised measured productivity might increase by around 0.25% per year. They endorse the value of work being done by statistical agencies to develop satellite accounts (frameworks for more detailed data collections covering particular sectors or assets) and recommend the development of satellite accounts for “as many of the categories of intangible assets as possible”. The very similar estimates of measurement error by Bureau of Labour Statistics economists and Corrado; 0.26% and 0.25% of labour productivity and overall productivity respectively, may be coincidental but more likely will be shown to be related. While the papers cite US data they also refer to international standards and practices. Similar magnitudes of error can probably be expected for Australia and other countries. Given that services are mostly intangibles; on a conservative interpretation services productivity growth is under-estimated by 0.2% and on a bullish interpretation, by up to 0.5%. Three different forecast for the EU15, the US and Australia all estimate MFP growth of around 1% per year for the next 20 years78, a figure slightly lower than in previous decades, except for the EU15, where it is much lower. When measurement errors are eventually corrected it’s possible that the growth rate will be corrected up to a rate that at least maintains long term (100 year) historical rates for the US and Australia. However this is little cause for optimism. In its “Intergenerational Report 2010”, the Australian Treasury documents the negative effects on the wellbeing of our aging

76 77
78

Ibid page 23

Ibid, pages 34, 38 Figures rounded to the nearest percentage. See: [1] Carone, Giuseppe, Denis, Cecile, Mc Morrow, Kieran, Mourre, Gilles and Roger, Werner Long-term labour productivity and GDP projections for the EU25 Member States, , MPRA Paper No. 744, August 2006, page 44 Online at http://mpra.ub.uni-muenchen.de/744/ [2] Robert J. Gordon, Revisiting U. S. Productivity Growth over the Past Century with a View of the Future, NBER Working Paper No. 15834, March 2010 Gordon’s estimate of 1.05 MFP growth is considerably loer than his previous estimate of 1.8 and yet is still described as “relatively optimistic” *3+ DCITA, Forecasting productivity growth: 2004 to 2024, Occasional economic paper, March 2006, table 1, page 4 1.0

population of a failure to not merely maintain but to increase long term growth rates.79 The services sector is perhaps the most heterogeneous of all economic sectors. There have been huge variations in both the growth and the productivity growth rates of different services across the sector over the last 15 years. Over this period service sectors that are not thought of as productivity leaders; were. Across the EU15 and the US, four sectors with very high growth rates of 3% to 7% – wholesale trade, financial services, other business services and retail trade – dominated labour and productivity growth for most of the period.80 These sectors grew at a much faster rate in the US than in Europe because of greater deregulation and because the businesses in the sectors actively adopted and adapted to the surge of new ICT. As mentioned earlier, there is a similar pattern in Australia.81 Over the same period the productivity growth rates of large service sectors such as education, health and government services were barely above zero. Apart from the impact of improved measures, the degree of optimism or pessimism about productivity improvements across the entire sector and especially in these sectors has little to do with their labour intensity and a great deal to do with the existence and effectiveness of policies that promote trade, deregulation, labour mobility, and innovation.

79 80

The Treasury, Intergenerational Report 2010, Page 99

Douglas Koszerek, Karel Havik, Kieran Mc Morrow, Werner Röger and Frank Schönbor An overview of the EU KLEMS Growth and Productivity Accounts, European Commission Directorate General for Economic and Financial Affair. No 290 October 2007. Pages 17-20 81 House of Representatives Standing Committee on Economics INQUIRY INTO RAISING THE PRODUCTIVITY GROWTH RATE IN THE AUSTRALIAN ECONOMY, Commonwealth of Australia April 2010 Quoting a study by Hughes and Grinevich, Page 56

Myth 6

Investment in innovation in the services sector is low

In this section we debunk a group of myths surrounding service innovation and draw attention to the dangers of ignoring them. We put service innovation in a historical context, review progress and problems measuring innovation, present evidence of the level of investment in service innovation in the private sector, and outline productivity and innovation trends in the education sector. A cluster of myths surround service innovation The myth that investment in service innovation is low relative to innovation investment in other sectors is one of a constellation of beliefs about service sector innovation. Services innovation is considered to be of little economic consequence and merits little attention. Two beliefs contribute to this view. First, the extent of service innovation is seen as quite limited. Second the impact and value of service innovation is not measured and is not considered measureable in any practical way. These beliefs have the effect of putting much service innovation beyond the ambit of public policy. The growing importance of service sector innovation has received attention from government. The executive summary of the 2008 PMSEIC Working Group on Science and Technology-Led Innovation in Services for Australian Industries82 provides a succinct overview:   Services are critical to the prosperity of the nation – employment (85%), GVA (78%) and community well being Services are growing rapidly internationally and are an increasingly dominant and pervasive feature of advanced economies. Knowledge-intensive services are an important area of growth (particularly for developed nations) Services innovation is a critical, but often unrecognised, contributor to productivity, economic growth and the competitive advantage of firms and nations The contribution of science and technology (S&T) to services innovation is poorly understood, a serious deficiency given the growing global services revolution

 

The report zeroed in on major opportunities for Australia: “Significant services innovations in the 21st century will develop through the interaction of science and technology innovation and customer-driven process, organisational and managerial innovation to create novel solutions to market opportunities and to transform businesses”83. The same opportunities have been highlighted by leading American and European economists84. The report provided a classification of service innovation but presented no firm policy directions or agenda. We suspect that the myths blunted its impact. There is another reason why these myths live on. Over the past twenty years much growth accounting research has been concentrated on the emergence and impact of ICT. The focus on ICT is not at all surprising and the work done to measures the contribution of ICT to growth has been valuable. At the same time the focus on ICT has effectively hidden the new elephants in the economic engine – innovation and services. Things are changing. Within the last three years the
82

PMSEIC (Prime Minister's Science, Engineering and Innovation Council) Working Group on Science and Technology-Led Innovation in Services for Australian Industries, Final Report April 2008 83 Ibid page iv
84

Dale W. Jorgenson (Ed) Economics of Productivity op cit, Preface pages ix to xxiv

spotlight has been put on the elephants. They are now being studied, dissected and measured in a growing number of studies from the OECD, the UK innovation agency NESTA, the UK government and Innova, an agency of the EC Commission for Enterprise and Industry. These reports provide sufficient evidence to demolish the myths surrounding service innovation. Innovation in a long term context Why is service innovation attracting more attention? The biggest hint comes from the popular phrase, the knowledge based economy. This phrase captures what makes modern economies different from earlier economies – the massive stock of knowledge which has been accumulating at a rapid rate for around two centuries. And, as shown earlier in this paper, knowledge, intangibles and services are closely connected. Innovation contributed to growth for centuries, but its role was rarely recognised. Other factors – population, natural resources, trade, investment, workforce participation and technological progress – dominated economic explanations of growth and decline. But as the stock of knowledge and the pace of change accelerated over the last two hundred years the scope and significance of innovation has also grown. US economic historian Bradford De Long shows the dramatic economic effect of knowledge accumulation and innovation in Figure 4.85 But gains achieved by one or two generations are taken as given by the next. And impressive as this picture of wealth creation may be Australia’s continuing standard of living is not assured. In the Intergenerational Report quoted previously the Australian Treasury best estimate forecast is that average annual GDP per capita growth will fall from 1.9% over the past 40 years to 1.5% over the next 40 years, representing a fall in income86. A contributing factor is a forecast fall in productivity growth87. Policy settings and management practices that influence the level of innovation and productivity across the economy and in the service sector, including the public sector, can help to improve productivity growth and our standard of Figure 4 living. The question is whether they will. How do we measure innovation’s contribution to growth? Innovation is a purposeful change process; a process of creating and applying knowledge and ideas that creates value.88 It is both a mechanism and a source of productivity and growth because knowledge is modified and developed during the innovation process. Solow was heading in the right direction when he suggested that the residual in his productivity formula was the rate of technological progress. Technological innovation is one type of innovation. Growth accounting research is now focused on the related topics of intangibles and innovation. Research, building on the work of Corrado, summarised in the previous section, is gathering

85 86

J. Bradford DeLong, University of California, Berkeley & NBER, January 1997http://econ161.berkeley.edu/TCEH/Slouch_wealth2.html The Treasury, Intergenerational Report 2010, Page 1

87

Productivity Commission Long Term Trends 1974 -2008 http://www.pc.gov.au/research/productivity/estimates-trends/trends and DCITA, Forecasting productivity growth: 2004 to 2024, Occasional economic paper, March 2006, table 1, page 4 88 This definition focuses on the ultimate sources of innovation, knowledge, including ideas; and the end result of successful innovation, the creation of value.

momentum and becoming a research hot spot. In a study funded by NESTA89 Haskel took the bold step of using intangible assets as a measure of knowledge capital and defined innovation as the contribution of knowledge capital to economic growth. His work provides the basis for a new model90 of the place of innovation among the factors of production and productivity in knowledge based economies. Figure 5 Key factors of production in the industrial economy

Figure 6 Key factors of production in knowledge based economies.

[Human Capital]

KNOWLEDGE

CAPITAL
In this diagram intangibles have been shifted from capital to knowledge capital. What does the diagram say? It says that knowledge contributes directly and indirectly to production in firms. It emphasises not only the direct but also the pervasive, indirect contribution of knowledge. In many service sector businesses knowledge is the major raw material and knowledge production is the core activity. This is most obviously illustrated by newspaper and media businesses. Knowledge contributes indirectly in all business, through the skills people apply, the information they use; and as a result of being “embedded” in the design and construction of capital items - tools, equipment and other technology. It’s relevant to note that considerable progress has been made in measuring knowledge stocks and knowledge accumulation at a national level. See the end notei for details. Knowledge can be a major or minor, direct or indirect factor of production without contributing to productivity growth. Accountants, lawyers, public servants, journalists, teachers, researchers91, doctors and nurses draw on their stock of knowledge capital every day at work. Does this make their
89

Haskel et al in “Innovation, knowledge spending and productivity growth in the UK” Interim report for NESTA Innovation Index project, November, 2009 We have simplified the Haskel model 90 The model is taken from a working paper by one of the authors.
91

A related myth confuses merges research and development with innovation. There is no question that R&D sits at the front of possibly half of all innovation; the problem is that R&D itself is sometimes perceived as innovation. Most research contributes to expansion and improvement of the stock of knowledge. Only a small quantum of research creates highly valuable knowledge in the sense that it represents significant new discovers or genuinely new, different and significantly more valuable ways of seeing and understanding the world. Research institutions can foster more creative and innovative research, but few do so in a systematic way.

work innovative? Not necessarily; and mostly not. Knowledge contributes to productivity growth through two interrelated processes: non-innovative improvement and innovative improvement. Improvement is the broader concept. Relatively small amounts of well managed innovation and much larger amounts of non-innovative improvement contribute to growth. Both make distinctive and complementary contributions to growth. Oftentimes innovation is the essential first stage of long lasting processes consisting of the scaling-up (replication and expansion) of the innovative output of the first stage. The expansion process can extend across firms, industries and economies over decades – known as diffusion of innovation. For example, it took the 3M technician Art Fry a year or two to perfect “repositionable notes”. Among other things he had to invent new glue that didn’t stick very well. Thirty years later, the patents have expired but the diffusion of what became known as Post-it notes is still continuing. In short term timeframes well managed innovation increases the rate of improvement in the workplace when it injects creative thought into continual improvement processes. A problem with the measures of innovation used for over a decade in the European Community Innovation Surveys (CIS) was that innovation was regarded only as product innovation. In addition it was not clearly distinguished from non-innovative improvement processes. Similarly, Haskel’s use of knowledge capital as an indicator or proxy for innovation overstates the amount of innovation. This issue is recognised in subsequent NESTA research and, after a lag of more than a decade, the CIS definition of innovation has been broadened. A subset of knowledge capital; innovation capital, provides the most accurate measure of assets invested in innovation. Innovation capital is made up of those assets - including money, research facilities, purpose designed machines, tools and workspaces, and specialist staff - tailored, dedicated, and allocated to innovation. This is illustrated in Figure 7 below.
.

Innovation Capital

KNOWLEDGE

CAPITAL
Both stocks and flows are measured in financial and national accounts. Measures of flow indicate how well stocks are managed and utilised over a period. And just as firms vary in their ability to manage assets they vary in their ability to manage innovation assets. So for a complete picture measures of innovation management processes are needed along with measures of innovation assets. Used as a noun, innovation describes outputs such as products, policies and programs. Arguably, innovation used in this sense is not merely something new, even though this “new” is at the core of innovation surveys such as the European Community Innovation Survey. (Were your last pair of new shoes innovative?) Innovation is something new, different and more valuable than the nearest

existing alternative92. Innovations stand out from other types of improvement because, whether incremental or radical, they embody elements of originality, novelty, creativity and imagination. (Einstein’s assertion that imagination is more important than knowledge comes to life in innovative processes.) It is these very elements that make innovation a more risky and potentially higher payoff activity. It is these characteristics that set innovation apart from non-innovative improvement. Does this distinction matter? If it is ignored improvement and innovation become one and the same. Recent innovation measures have improved, with a broader coverage across products, processes, marketing and organisational innovation93. However the risk remains that without sufficient rigor we end up with measures that fail to distinguish innovation from a vast expanse of incremental improvements. How much do services invest in innovation? Compared with businesses in other sectors, services use far less tangible capital and far more knowledge capital. A few years ago it would have been very difficult to demonstrate the extent of service sector investment in innovation, but research commissioned by NESTA over the last three years provides the data. Early research used expenditure on intangibles as an estimate of expenditure on innovation, however in the most recent report94 the possible errors in this approach are acknowledged and the authors provide indications of the size of the errors by comparing their figures with data from three other reports of survey innovation-related expenditures. The Report “Findings from the UK Investment in Intangible Asset Survey”95 details the level of spending and life lengths of private sector investments in intangible assets, based on a survey of a representative sample of UK firms. The survey found that the overall level of intangible spend is considerable at around £39 billion. This is divided between: software, about £11 billion, branding £10 billion, R&D £10 billion, training £7 billion and design and business process improvement £1 billion each. In-house spending is, on average, 55 per cent and purchased 45 per cent. The survey found that non-R&D intangible spending is much more widespread than R&D spend, with 50% of UK firms spending on non-R&D assets against 8% spending on both R&D and non-R&D assets96. It also found that non-R&D spend is much more common in services relative to manufacturing, especially in financial services. It states that “much of the incidence of innovation spending in the service sector, a major part of the economy, is not captured in the R&D statistics”97. Figure 8 below98 compares expenditure (in thousands of UK pounds) on the major intangible asset categories for the manufacturing (called production) and service sector. With the exception of expenditure on software, manufacturing firms spend slightly more on each category than service
92

Firm level innovation surveys assume that consumer purchases of a new product is evidence of the value of the new product. In one sense this is true. However, from the perspective of productivity growth the question is the extent to which the new product is found to be more valuable by consumers. New products and services that are demonstrably more valuable achieve stronger reputations and more repeat purchases. A refinement to the surveys would seek data on the duration and relative success of new products. 93 Innovations in marketing would only be expected to have a significant impact in combination with products, existing or new, that deliver value to purchasers. 94 Gaganan Awano Mark Franklin Jonathan Haskel and Zafeira Kastrinaki “Investing in innovation” Findings from the UK Investment in Intangible Asset Survey NESTA Index report July 2010. The other reports referenced reported statistic collected and surveys by UK government agencies between 2007 and 2009. 95 ibid
96 97

Ibid page 4 Ibid page 3 98 Ibid Figure 6, page 18

firms and five times more on R&D than service firms. The important point is that to the extent that expenditure on intangibles is an innovation indicator, service sector innovation is not insignificant.

Figure 8

Findings on “life lengths”, the period over which intangibles deliver value to firms, show a similar pattern. Table 3 below99 compares the findings of the survey conducted for the Investing in Innovation research, called IIA, and the findings of an earlier pilot study by Whittard. Ranges are given for 95% confidence intervals. Whittard used a broader definition of the life of projects and assets than the 2010 survey. Once again, regardless of which study is used, figures show that the period over which investments in intangibles/innovation deliver benefits are not too dissimilar for services and for manufacturing.

Table 3 UK Business Enterprise Research and Development 2008’ London: Office for National Statistics 2009.

Another NESTA study, by Rogers focused on measuring innovation capability and its relation to sales growth in nine areas of the UK economy100. Of the nine sectors, which are listed below, six are labour intensive services and another two, construction and energy have service components. The only non-service sector was automotive.
99

Ibid Table 9, page 27 Roper, S., Hales, C., Bryson, J. and Love, J ‘Measuring sectoral innovation capability in nine areas of the UK economy’ NESTA Innovation Index Working Paper London (2009)
100

• Architectural Services • Accountancy Services • Business Consultancy • Legal Services • Software and IT services • Construction • Energy • Design Services • Automotive The study used a novel three part model of innovation of innovation capability. The three parts are: accessing knowledge, building innovation (core innovation processes), and commercialising innovation. Compared with most models of capability the NESTA model has two distinct features – a focus on commercialisation, which is missing from most models and the use of the concept of variety or diversity. For example there are questions on variety of sources of ideas, variety of people seeking external knowledge, the variety of skills and roles in innovation processes and the variety of customer relation methods. The survey used questions on expenditure and new products sales as well as the questions requiring judgement by the managers who responded. The survey data uncovers the extent of innovation activity across the service sector. Tables 4, 5 and 6 below show the percentage of firm reporting each of eight different types of innovation101 for the accounting, architectural and software and IT services sector. Table 4 Innovation in accountancy firms

Table 5 Innovation in architectural firms

101

Ibid Pages 26, 31, 45

Table 6 Innovation in software and IT firms

Our purpose here is not to analyse the data, but to show that a considerable amount of innovation activity takes place across firms of all sizes in this sample of service sectors. The variety of innovation is one thing. How well it is managed is another. The survey explores this question and summarises its findings in Figure 9 below. Survey data was weighted and normalised allowing for comparability across sectors. The green, yellow and red circles represent the gap from best practice, across all sectors, for each stage for each sector. The letters; H, M and L indicate whether innovative abilities for each stage are narrowly or widely dispersed across firms within each sector.102

In Table 4 below we provide a simplified summary of the chart above. The gap scores for each sector has been weighed and tallied to provide a single capability score for each sector.
Accounting Architectural Services Automotive Construction Consultancy service Energy Legal Software & IT services Specialist design

3

6

7

3

9

8

4

9

7

Innovation in the intensely globally competitive automotive sector is known to be quite high. So this manufacturing sector may provide a useful reference point for assessing how well these service sectors are managing innovation. It’s clear that some sectors compare favourably while others compare poorly.

102

Op cit Figure 14, Page 69

Even while the measures are still being refined the NESTA research provides evidence of substantial innovation and innovation investment in the services sector. Innovation, productivity and performance measurement in education The role of human capital in economic growth is well established. Of all the models seeking to explain growth on a global scale, Romer’s is perhaps the most enlightening. In a 1992 study he and his colleagues found that just three factors explained over 80% of the difference between the rates of economic growth of countries, from the most advanced to the poorest. The factors were population, savings and human capital accumulation103. Education maintains and develops human capital. In Australia education is mostly funded and delivered by government, although private sector provision is growing rapidly. In this brief review our focus is on primary and secondary education. Despite the importance of education for economic growth, and the growth of the sector, it is not possible to measure educational productivity using national accounting data because there are no output measures. Not surprisingly educational productivity and innovation have barely reached national accounting research agendas. Outputs are defined as equal to inputs. There are good reasons for this convention as there are no monetary measures of educational outcomes. The lack of attention to educational productivity in growth accounting does not mean that that it is being ignored within the sector. Leading education systems are getting better at managing and measuring improvement. In Australia, a combination of investment in soft skills (principal and teacher training), and research and ICT applications that have barely been tapped, make for potentially high performance and productivity gains. Earlier this year the Ministerial Council for Education, Early Childhood Development and Youth Affairs, a body made up of Commonwealth and State Education Ministers established the Australian Institute for Teaching and School Leadership with a brief to “provide national leadership in promoting excellence in the profession of teaching and school leadership.”104 Funded by the Commonwealth Government, the body is focusing on standards development and setting as well as professional training. In addition most states education departments are spending more on leadership and professional development. For example, a few years ago the Victorian Education Department (DEECD) set up The Bastow Institute of Educational Leadership. The Institute delivers a suite of programs for emerging leaders, new and experienced principals, leadership teams, rural school leaders and early childhood professionals. More so than in other sectors, professional development in the education sector has a strong focus on leadership – from leadership of schools to instructional leadership in classrooms. Turning to educational research, it will be no surprise that there is a huge body of academic research on success factors in classrooms, schools, education systems, gifted students, new technology, student with learning difficulties and many more aspects of education. Among this mass of research
103

Mankiw, Gregory; Romer, David and David Weil (1992), “A Contribution to the Empirics of Economic Growth”, Quarterly Journal of Economics, 25, 275-310 Since Romer‟s paper economists, particularly Philippe Aghion and Peter Howitt, have developed and tested more sophisticated models of the interactions between education, innovation and growth. They distinguish the effects of tertiary as against primary and secondary education, between the stock of human capital and the rate of accumulation of human capital, and the influence of a country‟s distance from the technological frontier.
104

Australian Institute for Teaching and School Leadership Limited, Letter of Expectation 2009-2010 The Hon Julia Gillard MP Minister for Education, 14 December 2009

there are key findings from a few significant studies. For example, we know that the teachers have slightly more impact on student learning outcomes than any other factor in school systems; and that the performance and impact of teachers varies greatly. The evidence for the contribution of teachers was assembled by the former Secretary of the UK Department of Education, Sir Michael Barber in McKinsey’s ground breaking2007 global study “How the World’s Best Performing School Systems Come out on Top.”105 The McKinsey report also illustrates the interplay of evolutionary improvements and innovative initiatives in educational research. The report could not have, for the first time on a global scale, “linked qualitative results with quantitative insights on what high performing and rapidly improving school systems have in common”106, without the gradual development of the OECD’s PISA measurement model and database. Educational leaders and administrators also know (or can easily learn) which, out of 138 educational improvement strategies and methods, have the most and the least impact, and what minimum target to set when assessing the impact of improvement strategies. This is known because of the work of Professor John Hattie, who in 2009 published a book synthesizing the results of over 800 meta-analyses of educational improvement. He used a statistical technique, effect size, which standardises and enables meaningful comparisons of the results of around 11,000 studies. These two studies alone provide standard setting and professional development bodies with valuable knowledge and greater clarity about better ways to improve performance. These educational R&D findings can not only guide strategic directions and priorities but also reduce risks in a sector where there can be long lead times between investments in professional development and measurable results. Hattie’s findings are a source of improvement and his measures provide one way of distinguishing between non-innovative, low impact improvements from higher impact innovative improvements. It will be some time before educational outcomes and productivity are measured precisely. However, there is a growing consensus that a conceptually sound measure of school performance is the extent to which it maintains and improves the academic performance of its students, measured over the period of student attendance. Interestingly, this measure has been given an economic name - value added. There is also a robust debate on the relative importance of academically focused measures such as value added; and of measures with a broader focus on life, relationship and citizenship skills. While academically oriented measures remain the prime measures most education Departments use several indicators – attendance, student performance and well-being and transition management – when evaluating school performance. Will the “handicraft attributes” of teaching limit productivity growth? New ICT technology has to date had only a limited impact on educational effectiveness and productivity. The computer and Internet revolution seems to have passed by many classrooms. That is likely to change over the next few years as the cost of computers and networks goes down and their usage by teachers, administrators, students and parents increases. Real gains are likely to result from investments in better, customised software. Twenty years ago, how many readers foresaw the growth of the

105

Barber, Sir Michael, How the World’s Best Performing Schools Come out on Top, McKinsey & Company, September 2007 Needless to say other important factors are student ability, principal’s leadership and the contribution of parents 106 Ibid page 9

Internet, the emergence of social networking sites such as Facebook, and ipads? Over the next twenty years robots may well enter the classroom. The future arrives at different times in different parts of the world. In some laboratories robots are doing quite well engaging with autistic children. In fact, “The most advanced models are fully autonomous, guided by artificial intelligence software….which can make them just engaging enough to rival humans at some teaching task” At the University of California, a computer with arms is teaching Finnish to a three year old boy. The computer has a fixed happy face smile and a bandanna around its neck. It has been programmed to cry when its arms are pulled and when it does so the three year old gives it a hug. Outside the laboratory “several countries have been testing teaching machines in classrooms. South Korea, known for its enthusiasm for technology, is “hiring” hundreds of robots as teacher aides and classroom playmates”107. As the research and field trials continue the equally important debate about the benefits and risks of robotic teacher aides should continue. Common sense suggests that as computers, whether looking like notebooks or robots, assist more with administration and basic teaching, teachers will have more time for advanced teaching, to spend with the children with special needs (including gifted and those with learning problems) and to help students improve their results. In summary, education sector leaders are improving and measuring educational productivity and performance. Given the complexity of the task, while a great deal has been achieved over the last decade much further work remains to be done. Three factors appear pivotal in determining the rate of progress - the degree of autonomy108 that educational bureaucracies give to schools that perform well, the extent to which leadership training equips principals and teacher to effectively manage change and innovation; and the extent to which existing measures are maintained, extended to cover individual student progress and enriched with innovation measures.

107 108

- Benedict Carey and John Marko, “I, robot, am your teacher” The Age, Monday 2 August, 2010

nd

The Western Australian Education Department has launched an Independent Public Schools program giving selected schools a considerably higher level of autonomy.

Myth 7

Public sector innovation can’t be measured

The public sector accounted for 22% of GDP in Australia in 2007-08 and represents over a quarter of the service sector. Innovation is now on the public sector management agenda, especially at the national level. Paradoxically, as agencies are being encouraged to invest in building innovation capabilities, the biggest risk comes not from innovation itself, but from the attempt to foster it without measuring it, arising from the myth that there are no suitable tools for measuring public sector innovation and the view that measurement should be deferred until experts agree on a framework. Since the 1970’s reforms have transformed the sector and significantly increased productivity. The reforms were driven by a sustained focus on outputs, efficiency and effectiveness. For example, between 1987 and 2000 the APS workforce was reduced by almost a quarter.109 Business management methods were introduced and functions and services were subcontracted. Information technology was adopted more rapidly than in many countries and has contributed to efficiency. The merits of some changes, for example, stronger political control and less openness, are open to debate. For most of this period the concept of innovation was on the periphery. Then, within the last two to three years the level of interest in public sector innovation rose rapidly. Presently, apart from some excellent case studies of large scale innovation110, no one knows how much innovation takes place in public agencies, how well it is managed, what benefits it delivers and whether it delivers more or less benefit relative to other types of managed change and improvement. Given the recent arrival of innovation on public agency agendas this is not surprising. However, perhaps because of the seeming mystery of innovation, there is a pattern of taking initiatives without measuring progress or results. Risks and outcomes seem to be measured at the project level; the blind spot is at the organisational level. The DIISR Report on Innovation metrics noted that experimental surveys have been conducted in several countries including Canada, France, the UK and Korea. (In addition, in what may have been a first, several public sector agencies took part in a successful innovation benchmarking project run by the Australian Quality Council in 2001.) The Report states: “To date, there is no agreement on a framework for measuring public sector innovation, or if the Oslo Manual guidelines for measuring private sector innovation can be directly applied to the public sector”111. The report echoes the view, held here and overseas, that measurement should wait until experts112 agree on a measurement framework. Given that action is already underway in the APS and the Victorian Public Service, this view may become a recipe for action without accountability. Earlier this year the APS Management Advisory Committee, chaired by the Secretary of the Department of Prime Minister and Cabinet, released a report that it commissioned, Empowering

109

Changes in the Australian Public Service 1975-2003, Rose Verspaandonk; Revised by Ian Holland Parliamentary Library of Australia, Politics and Public Administration Group 2, June 2003 Since 2000 numbers have increased particularly in the areas of tax administration and security. 110 ANAO, Innovation in the Public Sector: Enabling Better Performance, Driving New Directions, Better Practice Guide, December 2009 The report has ten case studies 111 DIISR, Innovation Metrics Framework Project Consolidated Report, 2009 Page 111 112 One of the problems is that there is no consensus among the experts. Another problem is that most experts are experts in research and technology indicators who have ended up working on public sector innovation metrics.

Change: Fostering Innovation in the Australian Public Service113. The excellent report provides a realistic review and appraisal of structural and cultural factors affecting innovation. It emphasises the key role of leadership and makes a detailed set of recommendations. The report states that “The Australian Government’s annual State of the Service Report has repeatedly indicated significant enthusiasm among APS employees for new ideas and a positive attitude towards finding better ways of doing their job. Among staff, however, there is a perceived lack of opportunity and support for creativity and innovation within the APS. To date, there has been an ad hoc, rather than an ongoing, approach to innovation in the APS. There has been no systemic approach to recording and evaluating innovative methods or to sharing relevant knowledge and learning across the APS”114. Further: “Innovation rarely features in an agency’s performance measurement system, and what is not measured (or measurable) is usually not seen as important”115 and “Without measurement, it is hard to judge the success of an organisation’s innovation effort”.116 It then lists an assortment or process indicators that could be used. When the recommendations of the Empowering Change Report were considered 117 action to trial or develop measures was deferred. It was decided to instead follow the lead of agencies in the UK, Scandinavian countries and the OECD, where a considerable amount of work is being done on developing measures of public sector innovation. This decision raises several questions. What progress is being made towards measuring innovation in these places and when will measures become available? Busy activity on the other side of the world can give misleading impressions. A recurrent problem seems to be that when action is taken, measurement is forgotten or put on the back burner. For example the UK government took a pioneering initiative back in 1998 when it set up a £300 M endowment called NESTA, the National Endowment for Science, Technology and the Arts, to foster innovation in the UK. The endowment worked effectively in a range of areas since its establishment, but nine years passed before NESTA began working on innovation metrics, with a focus on developing a national innovation index. Since then several studies have been completed, but it is not clear how many years will pass before tools for measuring innovation in UK public sector agencies will be developed. The UK Office of National Audit has also run surveys, finding, as might be expected that there are no systematic approached to managing innovation. Between 2002 and 2006 ten universities participated in an EU funded research project on public sector innovation. The project was called PUBLIN (short for public sector innovation) and was coordinated by a Norwegian public sector agency. While the analysis was not as sophisticated at the analysis in the Empowering Change report, there is overlap between its findings and those of the Australian Report. The PUBLIN report recommended “The development of extensive and appropriate measures of innovation activities, performance and characteristics at the microlevel”118. It mentioned the OECD/EUROSTAT Oslo Manual as providing a possible framework. An indication of the rate of progress since the PUBLIN project comes from two sources. The first is the Summary Note on the Conference on Measuring Public Sector Innovation. The Conference was held in Copenhagen early this year and attended by public servants and academics from the UK, Europe and the OECD with an interest in the topic. The papers and discussion notes from the

113

Empowering Change: Fostering Innovation in the Australian Public Service. Australian Government, 2010 The emphasis on repeatedly is ours.
114

Ibid Executive Summary Page V1 This extract refers to what NESTA calls hidden innovation and what can also be called workforce innovation, involving many ideas with local and small scale applications; in contrast with the smaller number of large scale innovations described in the ANAO Guide. 115 Ibid page 40 116 Ibid page 109 117 DIISR advice to the authors 118 Per Koch, Paul Cunningham, Nitza Schwabsky and Johan Hauknes Publin Report No. D24” Summary and policy recommendations 2006 - pages 7, 63

Conference 119 indicate the early stages of thinking about where to start measuring public sector innovation. The conference was organised by MEPIN. MEPIN is a three year Nordic project led by DAMVAD, a research and consulting company and funded by The Danish Government. MEPIN developed and tested a survey questionnaire on public sector innovation. The questionnaire was based on the framework in the Oslo Manual. It attempts to cover what is being done and with what impacts and seeks a response from one manager from each agency. It also covers sources, expenditure, strategy, capabilities and barriers. The second source is the more recent OECD publication, “Measuring Innovation, A new perspective.” This report sets new standards in reporting innovation indicators. It combines breadth, technical quality, clarity and brevity. It makes sometimes complex, specialised data highly accessible to readers. Over 50 charts cover traditional science and technology indicators and also human capital, entrepreneurs, innovation in firms, returns from innovation and global challenges. However the only chart covering innovation within public sector agencies presents indicators of e-government readiness (Australia is in ninth position). The rest of the section on public sector innovation discusses once again the need for measure and difficulties of the task from an international perspective. It suggests options for international action. Diplomatic and, or technical considerations may explain why the OECD report notes the MEPIN work but does not give it prominence - the quality of the MEPIN work is at best uneven. Given the level of disagreement between those who have secured funds to work on metrics and the complexity of developing harmonised international measures, five or more years may elapse before agreement is reached and a tool is ready. The rate of progress may reflect the limited extent to which those responsible for developing metrics understand innovation management. McKinsey Partner Tom Peters was an innovation thought leaders in the 1980’s and his prescriptions are still relevant120. He emphasised a bias for action and was well aware of the need to manage risk. Writing about the early stages of innovation he recommended lots of small, fast failures. In other words, try lots of ideas on a small scale and terminate the trials as soon as results are clear. If Peter’s recommendation were followed governments would be sponsoring a variety of measurement approaches on a small scale. But, with one exception, there is no evidence of this121. Is it practically impossible to run a larger number, say five, of smaller, short term trials in the public sector? Alternatively, does the current pattern of relatively large scale, slow paced trials show that the public sector is at a quite early stage of learning to understand innovation management? This review points to the real dangers of failing to measure progress towards best practice innovation management. Given that one of the most powerful tools leaders have for monitoring and measuring progress are measures; what are the risks of losing momentum and opportunity as a result of inadequate attention to measurement? With current approaches, it may be up to five years before serious measurement begins and we will not know how much if any progress has been made in the meantime.

119

DAMVAD, Summary Note, Conference on Measuring Public Sector Innovation, 2010 Copenhagen Available at http://www.mepin.eu/ 120 Tom Peter’s best known book was In Search of Excellence (co-written with Robert H. Waterman, Jr.) 1982, His “fast failures” advice was set out in Thriving on Chaos, 1987 121 NESTA commissioned four small pilot trials to measure public sector innovation late in 2009. Interestingly, only one of the four successful tenderers actually conducted trials. The others interviewed public servants and proposed conceptual models. http://nestainnovation.ning.com/forum/topics/measuring-innovation-within

END NOTES
The internationally agreed economic definition of services is set out in the System of National Accounts as ―the result of a production activity that changes the conditions of the consuming units, or facilitates the exchange of products or financial assets. According to SNA 2008, there are two major types of services, namely change-effecting services (which can apply to goods or to people) and margin services (which can apply to goods and services). A feature of margin and change-effecting services is that they are not separate entities over which ownership rights can be established – they cannot be traded separately from their production. By the time their production is completed, they must have been provided to the consumers. In addition to change-effecting services and margin services, SNA 2008 defines as services a range of knowledge-capturing products, noting that they have many of the characteristics of goods in that ownership rights over these products can be established and they can be used repeatedly. Neither tangibility nor ownership is sufficient to distinguish services from goods, and while there are several unifying themes the economic definition of services encompasses a range of complex and subtly different activities. (From McCredie A, Soderbaum J, Drake-Brockman J E, Kelly P, Chou Y, Taborda R, and Hodges R 2010, The New Economic Challenge: Responding to the Rise of Services in the Australian Economy, Australian Services Roundtable and ACIL Tasman, September 2010)

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In 1963 Adelman acknowledged the huge challenge of measuring “society’s fund of applied knowledge”. (Quoted by Chris Freeman in Continental, national and sub-national innovation systems— complementarity and economic growth Research Policy 31, 2002 page 207.) Knowledge is now measured using knowledge intensity and knowledge accumulation metrics. Over the last decade knowledge measures have been developed for firms, industry sectors and countries.  Firms: Guy Gellatly, Allan Riding and Stewart Thornhill; Growth history, knowledge intensity and capital structure in small firms Micro-Economic Analysis Division Statistic Canada, 2003. For measuring knowledge intensity in services three criteria were used: GDP per hour worked, the % of workers with post-secondary education, and the industry average wage. Industry sectors See: Jong Hoo Choi & Seung Hee Han, Measuring Knowledge Intensity by Industries in Korea National Statistical Office, 2001. This paper uses direct and indirect R&D investment, ICT intensity, the ratio of higher education workers, ratio of researchers, patents and ratio of knowledge workers in its measure. Countries. See: The OECD Science, Technology and Industry Scoreboard “TOWARDS A KNOWLEDGE-BASED ECONOMY, 2001” The report defines investment in knowledge as public and private spending on higher education, expenditure on research and development (R&D) and investment in software. According to this report in 2001 Sweden, the United States, Korea and Finland were the four most knowledge-based economies. World Bank Building Knowledge Economies: Strategies for Development 2007. The elements of their knowledge scorecard are: performance, economic incentives and institutional regimes, education and HR, innovation system and information infrastructure.

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The World Bank has shown a statistically significant causal relationship between their knowledge accumulation metrics and economic growth for countries at all levels of economic development. A complementary development solving the problem of measuring service outputs is likely to be the addition of CVH (conjoint value hierarchy studies) linking consumer preferences particularly for services such as education and health, with input and output factors.