Turbocharging Australian productivity

Adrian Blundell-Wignall, former director of the OECD and professor at Sydney University, says:

  • “It’s not enough to tweak R&D incentives – we, Australia, need a detailed plan that will change the whole climate for smart investment and productivity growth
  • During the (recent) election, the focus was on tax cuts and “having a go”
  • The Treasurer has since talked about tax incentives for R&D
  • But improving productivity growth will need a broader framework of reforms to avoid our cloudy future”

Past government plans have resulted in the shrinkage of manufacturing in high-labour-cost economies:

  • In Australia, from around 16% of GDP in 1975 to 6% today
  • In the USA, the decline was less – 16% down to 11% of GDP

Australia made up this gap via finance and mining – and we became ‘contented and complacent’

Since China joined the WTO at the end of 2001, the commodity super-cycle pushed up the terms of trade – the real purchasing power of the income generated by domestic production was boosted by 9% of GDP, the equivalent of three full years of economic growth – the resulting feel-good factor got credit booming and house prices rising – finance and insurance now make up over 9% of GDP versus only 7% in the USA

However, mining and finance now both face headwinds:

  • In mining, China’s massive investment-focused strategy has resulted in diminishing returns – investments need to earn good returns if rising debt is to be paid for – China is on the classic path for financial crises – it suffered something of a recession in 2015, and more will follow – China is slowing structurally and diversifying its resource supply – both will hurt Australia
  • In finance, house prices and debt levels are too high and must unwind in the years to come, not least because of low interest rates.

And, outside these two industries, Australia’s labour productivity growth looks terrible – so much so that there’s a good case for excluding them from the measure:

  • Mining, because it’s so large and heavily capital intensive – relatively few people are employed there and the huge costs in equipment are someone else’s output
  • Finance, because it’s imputed from interest rate spreads

Excluding them both reveals even lower productivity growth – a meagre 0.3% p.a. over the past six years – compare that with the UK, an economy without resources, which is doing much better

So Adrian says having a plan is essential – a plan which creates an environment for more diversified and higher value-added growth covering three broad groups of firms:

  • Those with strong productivity growth accompanied by significant sales at home and in foreign markets
  • Large incumbent companies in the domestic market with negative productivity growth and weak penetration of foreign markets
  • A large number of medium-sized companies characterised by moderate growth and a domestic sales focus.

We know digitisation and technological innovations allow some firms to quickly saturate their domestic markets – however, only those that take advantage of economies of scale by expanding into foreign markets are able to remain in the strong productivity growth group

In addition:

  • Fast growers are always those firms with the highest levels of R&D expenditure
  • Mergers and acquisitions (M&A) activity are a recurrent theme in their business models

R&D innovations that drive productivity do not (outside Silicon Valley) come from small start-up companies “having a go” – R&D is a huge cost activity – in the main, it comes from larger companies with barriers to the appropriation of their IP by others, and which have access to external finance, particularly equity finance, essential for risk-taking and for entering foreign markets – their ability to restructure quickly, opening and shutting down plants via M&A, is critical – small firms lack capital and resources to carry out meaningful R&D and to launch products based on it

Overall, the “plan” should be to develop a research-and-innovation culture and policy certainty – one which recognises that better education output is central to future productivity growth – one which supports an equity culture for risk taking – so it must remove barriers to domestic and cross-border M&A

The plan must also resolve the problem that while economies of scale depend on foreign sales, tax avoidance by firms also relies on cross-border activity – IP, trademark and other earning rights should not be owned by foreign subsidiaries in tax havens

Adrian thus concludes that Australian productivity requires many hard reforms rather than just tinkering with R&D tax incentives.

Adrian Blundell-Wignall is also author of ‘Globalisation and Finance at the Crossroads’

The Secret Sauce for productivity?

Greg Hanover, CEO of Liveops Inc, claims organisations that tap into the power of the gig economy see much increased (% ?) workforce engagement and productivity

Why so?

He believes it’s because independent workers are empowered to follow their unique goals and create satisfying careers for themselves

And he’s right – it’s not only good for gig employees who prefer that way of working, it’s also good for their employers – it’s why we made good use of what we called ‘associates’ back in the 80s to man most of a consultancy division that way

Nowadays, from Uber to TaskRabbit, gig work has become ubiquitous – indeed, it’s fast becoming the dominant staffing model – for example:

  • In 2016, gig workers comprised some 34% of the U.S. labour force
  • Intuit, a financial services company, forecasts that number will reach 43% by 2020

This trend is driven in part by studies of full-time workforces which indicate that:

  • The average employee actually spends under three hours each day working productively – yet is paid for eight or more
  • Even the majority of employees themselves believe they could complete their daily workloads in only five hours
  • And, instead of working fewer hours, full-time employees are increasingly compelled to stay later and work longer – a sure-fire recipe for fatigue, burnout and chronic stress

Full-time and overtime environments thus not only generate huge waste, made worse if demand varies significantly from week to week, but often sap employees of the will to be productive – nevertheless, many organisations still choose to employ full-timers only, believing that gives them more control over their workers’ use of time, quality of work and knowledge of company secrets – they overlook the fact that gig-employees also have to be carefully selected, then trained up to company standards and need not be retained if they fail later on – and, re secrets, full-timers also can and do quit their jobs, most without any gagging constraints and often to join the competition

And they don’t fully appreciate that the on-demand gig model has big advantages:

  • It ensures 100% productivity – gig workers only get paid when they are productive
  • Gig workers, not their employers, have to manage the costs of their equipment, real estate, income taxes and insurance premiums
  • Gig workers have control over their lives and careers, and so tend to be more effective, motivated, and creative than many of their 9-to-5 counterparts

The gig economy thus promotes worker satisfaction, loyalty and productivity

By contrast, Greg goes on to say that full-timers’ engagement can vary significantly – “Even employees who love their jobs can experience low energy levels, off days and shifts when there seems to be nothing to do but wait out the clock”

However: “Independent workers seldom face the same problems – if they’re tired or bored, they can log off and come back to work when they’re ready – if motivation suddenly strikes, they can harness it immediately – if they work less than usual one week, they can make up for it another week”

Such flexibility is particularly important for creative workers (writers and designers), parents, people with disabilities, people pursuing more than one career or anyone with any obligation, interest, or lifestyle that doesn’t fit neatly into a traditionally segmented 40-hour workweek – work can happen when people are at their best, whether it’s early in the morning or later at night after the kids are put to bed – plus, on-demand work is adaptable – it can change as workers’ schedules, finances, living situations and other circumstances change

Conversely, monotony can kill engagement faster than rigid scheduling – few people thrive in settings where they are expected to do the same thing in the same way, day in and day out

Gig work can thus expose people to a wide variety of environments, processes, clients and ways of doing business – variety which not only fosters a person’s interest in their work but also increases their value for new and existing clients

When you’ve encountered more than one solution, you can choose, or create, the ideal answer for a given problem – this can save workers and organisations significant time and energy – people can take lessons learned from one gig and apply them to another – and the more opportunities someone takes on, the broader and deeper their knowledge base grows

Overall, Greg believes companies that leverage the gig economy don’t need to worry about engagement and productivity – because workers need to ensure it for themselves

We wouldn’t go quite so far – undoubtedly, partial gig employment has become an important employment model for many organisations in the 21st century – a model which is likely to become even more widespread – but it’s unlikely to fully replace the relative security of full-time employment for many years yet

GDP – Consumer Surpli

Consumer surplus is defined as the difference between the highest price a consumer would have been willing to pay and the price actually paid

It’s the unquantified value customers obtain from tangible stuff they buy – such benefits include taking less time or effort to do things, obtaining more fun and pleasure from life, having more social contacts with friends and family – all things people want, all demand growth areas, but all difficult if not impossible to measure

Nowadays, statistics on tangible outputs, GDP and national productivity levels seem to be flat-lining as we simply replace stuff, once worn out or used up, rather than add to it as in the past as more of our needs were met – and much of it, thanks to productivity improvement, would be available at lower unit prices (thus lowering GDP) if it did not incorporate significant quality and design improvements

For example:

  • In 1985, about 100,000 wealthy families were willing to pay over $1,000 for a set of the Encyclopedia Britannica – some would have paid more if they had to – others would have bought if the price had been a lot less
  • To calculate GDP, government statisticians looked at the market value of actual sales and multiplied the average price by the number of units sold – $100m say
  • Nowadays, Britannica sells about 50,000 online subscriptions at $75 each – a total of less than $4m
  • So the contribution to GDP from Britannica has dropped by 97%
  • But we are better off, not worse off
  • Thanks to the internet, Wikipedia and many other sources of specialised knowledge, the cost of such information consumption has fallen to near zero
  • We’re getting a lot more and paying a lot less

A similar story can be told for many other modern goods and services e.g. phones, GPS, voice recorders, digital watches, portable music players, video cameras

But economists and national statisticians can’t cope with this consumer surplus – they use measures better suited to the 20th century tangible world, not the new 21st century intangible world

As Matt Hancock, ex Bank of England economist and now UK Health Secretary, suggests, the growth of free apps is benefiting consumers but potentially dampening economic figures: “I don’t think economics has caught up with the impact of zero marginal cost production (digital copies) or products that are free”

Indeed, we believe consumer surpli may well represent a vast black hole in the GDP universe and may well explain much if not all of the so-called ‘productivity puzzle’ currently afflicting most developed nations

As Milton Friedman once famously claimed: “There is no such thing as a free lunch” – consumers always pay, somehow – they have to offer something of value in return for any freebie they get – and freebie services have proliferated over the last decade

Freebie services are used to trap people into revealing private data about themselves and their buying interests, preferences and search criteria – data which is then sold on to a wide variety of suppliers enabling them to focus their marketing and selling efforts on people most likely to buy from them, thus maximising their ‘hit rates’ – so the more attractive a freebie service can be, the more people will sign on to it and divulge their data, making the service even more valuable

Freebie services are thus carrots, modern ‘hidden persuaders’, clever marketing ploys predominantly peddled by IT entrepreneurs – during the past decade alone we’ve enjoyed an increasing variety of them via the iPhone, Uber,  Airbnb and Amazon plus Google, Facebook, Twitter and WhatsApp – and the value of those services to their customers, if counted somehow, would surely have increased national GDP and so productivity levels well beyond those officially announced – perhaps even making up the claimed big differences (18% in the UK) between actual current GDP levels and trend levels

No wonder Professor Hal Varian, chief economist at Google says: “GDP has a very hard time with free”

And Professor Erik Brynjolfsson agrees: “They (freebies) add a lot of consumer welfare but do not show up in GDP”

To underline his view, Erik determined the value offered by many digital services which go unrecorded by GDP:

  • He paid people to stop using freebie services and worked out a ‘reservation price’ for each one – an estimate of hidden GDP
  • He found the average user:
    • Would drop Twitter for nothing
    • Valued Facebook at £85 a month
    • Wanted £470 per month to give up WhatsApp, commenting “I run my life around it – I wouldn’t be able to go to any parties on Saturdays – I wouldn’t be able to contact my babysitter”
  • He concluded that: “Some of these services have become almost indispensable”

Clearly, the actual value of this consumer surplus varies per person as each of us usually has a different maximum we will pay for anything – hence it is nigh impossible to measure it for any nation as a whole

That said, some expert has already claimed that Whatsapp alone would add 3.74% to the Dutch GDP if 10 million of the country’s 17 million people used it (n.b. another statistic that gains credibility because nobody can disprove it)


  • GDP only records transactions at market prices – it’s completely silent on what we might be getting free
  • Volume of sales may have peaked but value of benefits is forever rising
  • G7 economies are currently undergoing radical changes through digitisation but economists and their performance measures are not keeping pace – they focus on tangibles that dominated growth over the last century but this new century is increasingly concerned with intangibles
  • Some new measures are needed for national productivity, prosperity and well-being

Small Businesses measure up differently

Michelle Ovens campaigns for the UK’s 5.5 million SBs in the ‘Small Business’ publication

She notes that small businesses are responding to this time of change and uncertainty with Brexit by keeping faith in the community values that make them the backbone of the UK economy.

She asks: “Perhaps we have been looking at small businesses through the wrong lens? Perhaps there is more to success than traditional measures of productivity, and more to business than the EU?”

The UK’s GDP per hour worked is around £50m less than the EU’s two major powerhouses, France and Germany – so what is it about UK workers and business that means we don’t measure up when the UK seems to be a thriving, digitised, successful economy?

The UK comprises a huge variety of business models and structures, with 99% defined as “small”, over 60% of private sector employment being via these businesses and some £2 trillion of revenues generated – clearly, they have a huge impact – nevertheless. they are forever being told their impact could be bigger if only they improved their productivity levels.

But when one asks small business owners what is important to them, productivity is not a factor – even profit is not considered as important as creating a living for others – it is a far cry from the ‘greed is good’ days of the 1980s.

Small businesses are putting people and communities first – they are looking to create ‘Good Work’, to foster supportive environments and meaningful working lives for their staff and wider stakeholders – there’s a shift to more flexibility in work, more giving back to the community, and a continued focus on the role of the business in the wider society and economy.

This is not all out of altruism – towns such as Grimsby in Lincolnshire, Holywell in Flintshire, and Frome in Somerset are being rebuilt around thriving small businesses – reducing local employment, increasing house prices, and even increasing local school standards.

The heroes of this small business revolution care less that both they and their employees make less money than their German counterparts, and more that the people in their community have jobs in the first place, and an improved quality of life:

  • Almost three-quarters of small businesses have or would consider keeping on a member of staff even if they did not economically need them anymore
  • There is a recognition of the importance of that job to that person, of that person to the wider community, and of the impact of losing employment
  • This speaks to better mental health, better social mobility and can even impact physical health outcomes.

What would be seen as bad for productivity is almost priceless to the person who keeps their job even when times are tough – hence, staff are appreciating the benefits of working for a small business on a much broader set of criteria than just paying the minimum wage, especially covering the longevity and stability of the work they provide.

Small businesses are thus creating better communities, appreciating their employees as people rather than merely a means to profit, and their communities are appreciating them in return.

Michelle concludes: “It’s now time that the wider world of stakeholders – particularly big business, local and national government, and charities – start to realise the critical and valuable role these businesses play, and reward them for it; not hold them to a set of metrics that simply do not apply”

AI, the future of work and inequality

An excellent article follows, by Daniele Tavani, Colorado State University, USA – reprinted in full

One of the most spectacular facts of the last two centuries of economic history is the exponential growth in GDP per capita in most of the world. Figure 1 shows the rise (and the difference) in living standards for five countries since 1000 AD.

Artificial intelligence, the future of work, and inequality
Figure 1: Gross Domestic Product in five countries, 1000-2015. Source: The CORE-Econ Project. Credit: Colorado State University

This economic progress, unprecedented in human history, would be impossible without major breakthroughs in technology. The economic historian Joel Mokyr has argued that the Enlightenment in Britain brought new ways to transfer scientific discoveries into practical tools for engineers and artisans. The steam engine, electricity, sanitation are examples of technological discoveries that propelled the engine of economic growth, increasing standards of living across the planet.

At the outset of the Industrial Revolution, the Luddite movement rose out of fear of labour displacement in the British textile sector. Famously, the Luddite protesters destroyed the very machines they were using at work, in order to preserve their role as active members of society. Their reasoning was that if automation could double, triple, or quadruple a worker’s output, the economy would need half, a third, a quarter of the current workforce. The Luddite movement was eventually suppressed by military force in 1816, but history has proven them partly correct.

Humans do work shorter hours today than in the 19th century, as shown in Figure 3.

Artificial intelligence, the future of work, and inequality
Figure 2: Luddite protest. Credit: WikiCommons

Many Silicon Valley executives like the Tesla CEO Elon Musk believe that we are on the verge of a new technological revolution that will see Artificial Intelligence (AI) automating a majority of tasks that are currently performed by humans. Just as horses were displaced by motor vehicles, truck drivers may soon be replaced by self-driving vehicles.

Factories producing components for personal computers and tablets are also becoming highly automated. The BBC reports that FoxConn has replaced 60,000 of its workers with robots. An Oxford University study claims that 47% of US jobs could be lost to automation, along with 69% of jobs in India, 77% in China, and 57% worldwide. Machine learning enables robots to make decisions based on a vast amount of data on (sometimes very difficult) decision-making by highly-skilled humans.

Should we then fear that the forthcoming AI Revolution will force humanity to completely rethink the current organisation of our lives?

One of the most important economic thinkers of all time, John Maynard Keynes, wrote in his 1930 essay “The Economic Possibilities for our Grandchildren” that by the 21st century we could fulfill our needs and wants with a 15 hours workweek and devote the rest of our lives to non-monetary pursuits. Fast-forward to 2014, when the late physicist Stephen Hawking told the BBC that “artificial intelligence could spell the end of the human race.”

Artificial intelligence, the future of work, and inequality
Figure 3: Annual hours worked, 1870-2010. Credit: The Maddison Project 2013

Should we see AI as liberating or as a destructive force?

Economists have debated the effect of technology and automation on jobs for a long time. The first set of questions regards labour displacement and whether there is any future for work at all. The second set of questions has to do with how automation impacts income and wealth inequality. On the one hand, if the ownership of robots is concentrated in the hands of a few—let’s call them the 1%—and a majority of jobs disappears for the 99%, it will exacerbate inequality, which is already on the rise (see Figure 4). On the other hand, technological change is also creating polarisation in the labor market among the 99%. According to the MIT economist David Autor, between 1989 and 2007 job creation has occurred mostly in low-paying and high-paying jobs, while middle-class jobs were affected by job destruction on net.

It is true that automation displaces workers in some sectors, but workers relocate to other sectors over time. Historical examples in advanced economies like the US are the transition from rural agriculture to urban manufacturing during the first half of the 20th century, and from manufacturing to services in more recent times.

The net effect on jobs does not seem to lend support to Hawking and Musk’s gloomy predictions. This is even more true if one considers another beneficial effect of technological change: everyday objects like dishwashers, vacuum cleaners, washers, and dryers, reduced the burden of housekeeping and freed up time for women to seek employment. Indeed, the labor force participation rate was 67% in 2000, compared to 59% in 1948. The increases in labor productivity brought about by technological change spill over into higher wages for workers

Artificial intelligence, the future of work, and inequality
Figure 4: Top 1% Fiscal Income share, US, 1913-2015. Credit: World Inequality Database

This is not to say that technological change has no costs for society. The transition from agriculture to manufacturing took time, and in the short run produced economic anxiety, unemployment, and poverty among former agricultural workers. The same applies to the transition to a service economy, although there are some troubling peculiarities about the latter process. The transition to manufacturing was associated with higher wages and employment protection through union membership. The transition to services not so much: low-skill service workers tend to be paid less than workers of similar skills in manufacturing, and service workers are in general not unionised: consequently, they do not take advantage of the benefits of collective bargaining in terms of wages.

Another cause of concern is that automation and technological change is characterised by increasing returns: doubling the scale of production enables production at more than twice the output at the initial scale. Increasing returns lower the cost of production as the scale of a firm expands, which leads to firm concentration toward few, large firms with lots of market power. And it is not by chance that the most visible hi-tech industries, such as Apple, Amazon, Google, and Microsoft are all basically monopolies.

The decline of manufacturing employment and the increasing concentration of the US corporate sector are two reasons why average wages have not kept up with the increase in labor productivity over the last 40 years. An average 2015 worker is almost 2.4 times more productive than an average 1948 worker, but her wage is only 110% higher. Wages were growing on par with labour productivity up to the 1970s; starting in the 1980s, productivity has been growing at a much faster pace. The process of job polarisation has fed into this pattern, determining rising employment in low-paying sectors and high-paying sectors, at the expense of good middle-class jobs.

Although it is possible that AI is completely different from the technological advances of the past, we should be sceptical that automation will mean the end of work. Jobs—or even specific tasks—will be displaced, but workers will relocate to different jobs or take up different tasks. For low- and medium-skill workers, it is likely that the relocation will occur in jobs of lower quality, meaning either lower pay, or fewer benefits, or a combination of both. Workers who possess skills that are complementary to new technologies, on the other hand, will benefit from the advent of automation by reaping most of the productivity increases in the form of higher wages. And the very few CEOs of successful tech companies will see their incomes skyrocket.

Hence, citizens and policymakers concerned with the rise of automation should focus on its effects on inequality. During the 1980s economists have embraced supply-side views that inequality is beneficial for growth, but recent academic research has shown that the opposite is true. The work by economists Thomas Piketty, Emmanuel Saez, and Gabriel Zucman has suggested that high marginal tax rates on top incomes—that is, tax rates levied on every dollar earned above a certain, typically very high threshold—would go a long way in reducing inequality without distorting the individual incentives to work hard. Indeed, the US economy grew fastest between 1950 and 1970, when the top marginal tax rate was well above 70%. The corresponding tax revenues could be used to provide unemployment compensation or foster the provision of public goods such as education and training for workers to relocate when their jobs are automated.