AI increases productivity

By 2021, artificial intelligence (AI) will allow the rate of innovation of Filipino companies to increase by 1.7 times, and nearly double employee productivity gains in the Philippines, according to a study titled Future Ready Business: Assessing Asia Pacific’s Growth Potential Through AI

The study made by Microsoft and IDC (International Data Corp.) surveyed 109 business leaders and 100 workers in the Philippines

An article in The Manila Times which reported on the results is reproduced below as it will be of great interest to many businesses in many other nations, East and West

While close to 90% of business leaders polled agreed that AI is instrumental for their organisation’s competitiveness, only 45% of organisations in the Philippines have embarked on their AI journeys – yet those companies that have adopted AI expect it to increase their competitiveness by 1.5 times in 2021

“AI is the defining technology of our time that significantly accelerates business transformation, enables innovation, boosts employee productivity and ensures further growth – economies and businesses that have yet to embark on their AI journey run a real risk of missing out on the competitive benefits that are enjoyed by leaders” said Ricky Kapur, Microsoft Asia Pacific’s Enterprise and Partners Group general manager.

Why adopt AI?

For organisations that have implemented AI initiatives, the top five business drivers to adopt the technology were, in priority order:

  • Better customer engagements (27%)
  • Accelerated innovation (24%)
  • Higher competitiveness (16%)
  • More productive employees (10%)
  • Higher margins (8%)

“Last year, organisations that have adopted AI saw tangible improvements in those areas in the range of 22% to 44%” said Randy Roberts, IDC Philippines head of operations

“They forecast further improvements of at least 1.5 times in the three-year horizon, with the biggest jump expected in higher margins, accelerated innovation, and more productive employees”

The study evaluated six dimensions critical to ensuring the success of a nation’s AI journey – data, strategy, investments, culture, capabilities and infrastructure – it uncovered that the Philippines needs to focus on improving all areas, particularly its investments and data to accelerate its AI journey

“The Philippines needs to substantially improve its readiness – organisations’ leaders should make AI a core part of their strategy and continuously invest in this transformative technology for the long-term success, sometimes without immediate returns” Roberts said

Business leaders who are adopting AI face three top challenges:

  • Lack of thought leadership and commitment to invest in AI
  • Lack of skills, resources, and continuous learning programmes
  • Lack of advanced analytics or adequate infrastructure and tools to develop actionable insights

The study showed that, to move ahead on their AI journeys, businesses have to create the right organisational culture – a significant proportion of business leaders and more than half of workers surveyed believe that cultural traits that support AI journeys, such as risk-taking, proactive innovation, as well as cross-function partnerships among teams, are not pervasive today

“Overall, workers in the Philippines are more sceptical than business leaders about the cultural readiness of their organisations” said Roberts.

The study also found that business leaders and workers in the Philippines hold positive viewpoints about AI’s impact on the future of jobs – the majority (74%) believe that AI will either help to do their existing jobs better or reduce repetitive tasks

Kapur said: “Microsoft’s vision for AI is first and foremost about people – AI technology cannot progress without them – this means that millions will need to transform themselves into skilled workers as well as learners that an AI future needs – it is heartening to see that 88% of businesses prioritise skilling and reskilling of workers in the future – they plan to invest as much, or even more, in human capital than in new technology”

“The jobs of today will not be the jobs of tomorrow, and we have already seen demand for software engineering roles expand rapidly beyond just the tech sector – however, building an AI-ready workforce does not necessarily mean an acute need for technological skills”

The top future skills required by business leaders in the Philippines include digital skills, IT and programming skills, adaptability and continuous learning, as well as analytical skills

At present, the demand for these skills is higher than the existing supply

New technology needs new models

According to the WEF – World Economic Forum – manufacturing executives today are confronted with an enormous variety of promising new technologies, ranging from artificial intelligence to connected machinery to 3D printing, all of them offering some combination of cost savings, quality improvements and increased flexibility

They then say it’s tempting to think that a manufacturer could modernise itself simply by replacing its old processes with new ones that feature these technologies – but the historical record suggests that isn’t enough

N.B. The following text reproduces most of the interesting detail in a recent WEF article


For an analogy, consider the late 19th century when managers rushed to electrify their factories – electrification seemed an obvious productivity boost but it failed to produce any notable gains for more than three decades – just before 1920, that began to change; gains from electrification accelerated, and it accounted for half of all productivity growth in manufacturing during the 1920s

The Stanford economist Paul David found that managers at first simply overlaid “one technical system upon a preexisting stratum” – factories of the late 19th century used “group drive” systems, with a waterwheel or steam engine driving large groups of machines through systems of pulleys and belts – in the first wave of electrification, managers simply replaced the old power sources with new electric motors, which continued to drive large groups of machines through these pulley systems – they enjoyed modest cost savings on fuel as well as slightly improved control, but their factories continued to function exactly as before

A new generation of factories in the early 20th century began to use electrification differently – rather than instal centralised motors that drove machines through rotating shafts, they began to use “unit drive”, in which a single electric motor is installed in every machine, driving it independently – the advantages of electrification turned out to be profound in ways that early electrifiers hadn’t imagined

Because group-drive shafts lost energy to friction quickly over distance, early factories were arranged around the transmission of power rather than the flow of labour and goods – they needed to be compact to keep machines close to power sources, and any reorganisation was a cumbersome process

By contrast, the 1920s and 1930s saw the birth of factories without group-drive shafts bolted to their ceilings – factories could be bright, airy facilities with efficient single-floor layouts that could be rearranged quickly in response to market demands – electrification thus changed manufacturing, but only after managers became willing to redesign their entire businesses around the fundamental capabilities of electric machinery


Computers have since become another example – it seemed obvious that they should boost productivity, but those gains didn’t materialise at first

In the 1960s and 70s, businesses simply moved individual functions like payroll, inventory management and invoicing to computers, treating them more like glorified databases and printers – this led to some modest efficiency improvements, but no more – the real gains would be realised not from saving a little money on record-keeping and printing but by reorganising entire companies and industries around computers – indeed, from the mid-1990s onwards, we’ve seen the rise of an entire generation of valuable companies that invented new, fundamentally digital business models around computers and networking

Today’s manufacturers are in a position similar to that of the semi-digitised businesses in the early 1990s – individual technological solutions are available for a wide range of problems that manufacturers experience:

  • Artificial intelligence can save worker costs on tasks like quality assurance
  • Connected machinery can reduce downtime by warning of maintenance needs in advance
  • 3D printing offers rapid prototyping, flexible production, and savings on small- and medium-run manufacturing

Manufacturers that adopt these technologies without a plan for reinvention will earn only incremental improvements and fail to realise the full value of such new technologies

Ways to reimagine manufacturing

Manufacturers should look to early successful examples of digital factories driving changes in products and business models

These include:

Mass customization, in which products are designed and fabricated around individual consumers – first applications have emerged in high-value fields such as medical devices – decreasing costs for digital fabrication technologies promise to bring mass customisation to lower-value products, including consumer electronics, apparel and athletic equipment

Example: Align Technology has treated more than 5 million orthodontic patients with its Invisalign dental aligners – they begin with a 3D scan of a patient’s mouth and continue with a 3D printing-based manufacturing process

Continuous product development, in which physical products are constantly refined in order to offer improvements or address new markets – the flexibility of digital manufacturing reduces product-development and retooling costs and makes continuous product development possible.

Example: Airbus uses over 1,000 3D-printed parts in its newest airliner, the A350-1000 – most must be reconfigured whenever a modification is made to the layout of the cabin – by 3D-printing these components, Airbus avoids retooling costs and supply chain disruptions – meeting a customer demand for a new cabin configuration only requires a digital redesign

Digital supply chains, which combine digital design files with flexible, automated production facilities that are able to fabricate them – by making distributed manufacturing feasible and cutting inventory requirements, they reduce supply chain cost and risk, and make it possible to serve markets in ways that would not otherwise be feasible or cost-effective.

Example: Deutsche Bahn, the German railway operator and global logistics provider, found that 50% of the replacement parts it requires to maintain its trains can be 3D-printed – this reduces its costly inventory of spare parts, currently worth €600 million, increases dependability, and brings flexibility to the railroad’s fleet planning by making it possible to operate trains, some of which are 50 years old, without long-term support from their original manufacturers

Digital supply chains also offer a way to respond to the emerging global tariff regime: product specifications can be transmitted digitally to distributed factories, which can produce them for local markets without exposure to tariffs.


When new manufacturing technologies emerge, it’s easy to think about them in terms of concrete applications—the requirements they’re able to meet, the kinds of products they’re able to fabricate

But the real returns go to those who are able to build new business models that stand on top of them, and are willing to reorder entire industries around them

Puzzle – What puzzle?

“The Miracle Years Are Over – get used to It”

So announced Ruchir Sharma, a contributing opinion writer for the NEW YORK TIMES, in a well-argued article reprinted with only minor tweaks below

Across the world, economists have had to downgrade growth forecasts – but it’s not as bad as it sounds

Last year (2018) looked like the time when President Trump had delivered on his promises to strengthen the economy – his tax cuts appeared to juice growth above 3%, a pace the United States had not topped since 2005 – but the US Commerce Department has since revised 2018 growth downward to below 3%, even as forecasts for 2019 were also trending lower, toward 2%

And it’s not just an American story and Mr. Trump who won’t deliver on promises of 3, 4 or even 5% growth – across the world, economists have downgraded growth forecasts in most years since the global financial crisis of 2008:

  • Defying the hopeful projections, Japan has rarely grown faster than 1%
  • Europe has struggled to sustain growth faster than 1.5%
  • And no one quite knows how fast China is growing, but it’s clear that there, too, the economy is slowing.

So why is the dismal science suddenly guilty of issuing overly optimistic forecasts that set the whole world up for disappointment?

Economists keep basing forecasts on trends established during the postwar miracle years, when growth was boosted by expanding populations, rising productivity and exploding debt – but population and productivity growth had stagnated by 2008, and the financial crisis put a sudden end to the debt binge

The miracle is over

Politicians often promise to bring back a golden age, but serious economists also are encouraging a similar illusion – even during the Industrial Revolution, in the 19th century, the world economy rarely grew faster than 2.5% a year, until the post-World War II baby boom began to rapidly expand the labour force – after 1950, the combination of more workers and more output per worker lifted the pace of global growth to 4% – economists came to think 4% was “normal”

Yet by the last decade, the baby boom had faded out from Europe to Japan and China – even in the United States, younger and faster-growing than most developed countries, growth in the working-age population slowed to a mere 0.2% last year from 1.2% in the early 2000s – and because fewer workers correlates directly with slower growth, that decrease implied a 1-point drop in economic growth

Roughly, economists should have expected that United States economic growth would slow to 2% from 3% — and it has – this is the new normal for the American economy – stimulus measures like the Trump tax cuts can lift growth above this path, but at best temporarily, at the risk of higher deficits and debt

For political leaders, the new age of slow growth is not a problem to solve – it’s a reality they need to accept and explain to the public – especially because it’s just not that bad:

  • When populations are growing slowly, the economy doesn’t need to grow as fast to keep incomes high
  • In the United States this decade, growth in GDP per capita has slowed much more gradually than the overall economy, by half a point, to an average of 1.4%
  • Although Mr. Trump likes to boast about how well the United States is doing against developed rivals, Europe has been growing just as fast in per capita terms this decade
  • And Japan has been growing slightly faster
  • In a rich country, that is fast enough to satisfy most people
  • Indeed, surveys show that Americans have rarely been more confident about the economy.

Slower growth in the working-age population also means less competition for jobs worldwide, which goes a long way to explaining why unemployment is now at record lows not only in the United States but also in the UK, Germany and Japan – surely that’s not a bad thing

Whatever politicians tell the public, their attempts to bring back the miracle years are ill-advised – growth in the economy is driven by growth in the number of workers and in output per worker, or productivity – but since the postwar surges of 1950s and 60s, productivity growth has slowed, also defying government efforts to lift it

For a time, the global economy kept motoring along anyway, fueled by a surge in debt – in the 1980s, central banks began winning the war on inflation, which allowed them to drop interest rates sharply – lower borrowing costs unleashed a worldwide binge that saw debt surging from 100% of global GDP in the late 1980s to 300% by 2008

Then the global financial crisis hit, ruining many private borrowers and lenders, many of whom are still wary of taking on new debt – and after growing faster than the economy for three decades, debt growth in many countries, including the United States, has fallen back in line with economic growth – even China, the one major country that dodged the crisis and experienced a surge in lending after 2008, is now reluctant to build on the mountain of debt that already weighs down its economy

So the postwar miracle is over – economic growth is weighed down by the baby bust and the debt hangover – yet because economists continue to base forecasts on miracle rates of growth — 4% for the world, 3% for the United States — policymakers keep fighting to hit these targets – this is very risky

There are growing calls from economists on both the right and the left to lower interest rates, or increase government spending, to boost growth even if that risks higher inflation – at the Federal Reserve, too, there is an emerging view that letting inflation rise above 2%, long considered a red line, may not be unwise

The underlying assumption seems to be that policymakers must take action because 2% GDP growth is intolerably slow

But must they?

The confidence surveys suggest Americans are quite content with record-low unemployment, benign inflation and 1.4% growth in GDP per capita – why then the rush to pump more money into the economy which risks rekindling its debt problems and inflation?

The world does not need more debt and more inflation to counter trends of declining population growth and high indebtedness

Instead, economists need to adjust their forecasts and politicians need to rethink their polices to match this reality, because trying to recreate a bygone golden age is a shaky way to build the future

Gallup’s ‘most profound’ finding

The Wall Street Journal suggested there could be a single fix for many of the big problems that companies experience – hiring better middle managers

They based this on a Gallup study that found a company’s productivity depended on the quality of these crucial leaders – managers don’t just influence results, they explain a full 70% of the variance — something Gallup called “the single most profound, distinct and clarifying finding” in their 80-year history
Quint Studer, founder of his eponymous Community Institute, says: “Great middle managers are the key to creating great companies”
He goes on to say: “The irony is middle managers often have the smallest training budget of any group in the organisation – given their level of responsibility, their impact on organisational performance and their facing pressure from all sides (bosses, employees and customers) this makes no sense”

Hence, it’s important to hire and promote the right people for these critical positions, and to train them well

To do this, one first needs to understand exactly why middle managers are so important

Quint suggests the following reasons:

  • Managers control the culture of the company – they model the habits and patterns of behaviour expected of all employees and ensure others live up to them as well
  • They’re key to employee engagement for they determine how employees feel about their jobs – “People don’t quit the company, they quit their boss”
  • They know where performance problems are and who’s doing well
  • They have a huge impact on attracting as well as retaining talent
  • They’re responsible for bringing out the best in people – they need to inspire and nurture creativity, innovation and teamwork
  • They’re on the front line with daily processes so should know where and how things can be improved
  • They manage a multitude of tasks and projects and control whether they get done efficiently or not
  • They make change happen by moving people through the various stages
  • They determine what gets reported to senior management

In short, middle managers have hugely important and difficult jobs

Hence, all organisations should make sure they receive the resources and training they need to do them well

Sadly, at present, few organisations regularly review existing skill sets of their managers and the development needed – so it’s little wonder most employees rate their current managers as ‘bad’

Productive recidivists

Over 70% of offenders re-offend within one year!

Why is this?

Most prisoners are locked up for most of the day and not treated well by the officers which surely makes them worse than they were at the start, not better – in these Universities of Crime, they learn to despise authority, not learn a trade for when they’re released and that there’s better ways of living

According to Rod Aldridge, founder of Capita:

  • 66% of people going to prison have no job
  • 75% of people leaving prison have no job to go to
  • And 30% of them don’t even have a place to live

It’s not rocket science to see that, if they have no job, no job experience and nowhere to live, they have a major incentive to go back to offending – what options do they have?

As a result, some enlightened UK companies have set up schemes to train and employ offenders and thus give them a second chance – and they’ve found only 7% of those that take this opportunity re-offend, a huge benefit to society at large

Many other benefits also accrue to the employer organisations involved – Adrienne Selko has just reported in Industry Week about the same initiative taken in the USA

Nehemiah Manufacturing’s workforce comprises former felons, returning citizens as they are called, who are creating great results for the company

Richard Palmer, president of the company, wanted to provide jobs for people in Cincinnati who were unable to find them – the company’s mission is to rebuild the city of Cincinnati by giving people jobs and a “renewed hope for the future”

When he opened the doors, one of the first people who came to him looking for a job was a former felon – Palmer hired him immediately – it worked out so well that currently 90% of his workforce comprises returning citizens – and one important way they show they are welcome is to stop asking them about their past – they delay criminal background checks until later so that potential candidates are not immediately dismissed.

Palmer says: “The loyalty of these workers and their productivity is just amazing – it’s been a great business decision with a high rate of retention, which is especially important given the labour shortage:

  • They’re some of the hardest working people we’ve ever seen
  • In proving themselves, these employees become fiercely loyal, insistent on high quality, positive teamers who help each other, hard chargers who self-sacrifice for the success of all”

Offering second chance opportunities to returning citizens has now been going on for years in the USA – and with almost 700,000 people released from prison each year, it’s a very large pool of workers to fish in


  • Clearly the upsides here are significant not only for employers and returning citizens involved but also society in general
  • The downside concern is the relatively small % of ‘no-hoper’ felons who have a long record of stealing or worse – if this is not known upfront, they may well abuse such altruism and spoil matters for all others

Catching the right fish

With globalisation, all organisations can fish for new recruits in the one big pond

But the most successful anglers are they who hire on merit, not in their own image, according to Tomas Chamorro-Premuzic, a professor of business psychology at Columbia University in his book Why Do So Many Incompetent Men Become Leaders

An article by Rosamund Urwin in The Times goes on to quote Tomas saying bosses need “data-driven assessment i.e. CVs, psychological tests and analysis of past performance” to identify potential winners who might be humble and understated in interviews compared with other self-aggrandising applicants

At present, most organisations tend to haul on-board the wrong candidates and then promote them up corporate ladders:

  • “Boss interviewers are not great at judging competence and can be unaware of interviewees’ limitations
  • Many can charm people initially but don’t make good bosses, being more prone to bullying and harassment, and resistant to negative feedback
  • They blame others for their mistakes and take credit for others’ achievements”

Hence survey after survey show a majority of employees believe they have a bad manager

Tomas says: “People get rewarded for sucking up when leadership should be about managing down – turning a bunch of people into a high-performing team – but bosses don’t care about people development, they’re more focused on politics and, sadly, this works for them”

One major hurdle, according to Tomas, is that most people think a good leader is overconfident, narcissistic, inspirational, even bullying – citing Steve Jobs and Philip Green as examples – when the qualities that make a great leader are humility, integrity and competence – virtues (he believes) which are more readily found in women like Angela Merkel – they lead in a more transformational way, are less likely to be absentee leaders and have more emotional intelligence

Tomas’s concludes with: “The people who are best at something tend to be very self-critical”

Customer measures needed

Once, supply of most goods and services people wanted to buy was limited – suppliers thus had the whip-hand – for example, Ford could offer their ‘Model T’ cars using the strap line “any colour so long as it’s black”

Those days are long gone

Mid 20’th century and on, competition between suppliers started to become serious – first nationally, then globally – seeing profits being made by a few vanguard suppliers, others quickly moved in to their markets, some with new and better ways to do things and offerings for customers

The result of all this competition between suppliers is that customers now determine what and how much they sell (except when the supplier is a public sector monopoly offering an essential service the public cannot do without)

Initially, price was their main criteria for making their buying decisions – but, in the 70s/ 80s, quality and then service levels offered soon grew to be equally as important to most customers

Value for money was what most sought – if the price was high, quality and service levels were expected to be high – pay for a five star hotel and expect Savoy or Ritz standards, for example – pay for a one star B&B and expect a good night’s sleep, clean linen and a tasty ‘full English’ breakfast – both would be deemed ‘good value for money’

Hence, all suppliers, given the star rating they target, need to know what rating their customers give to the price charged versus the quality and service levels received – and, if the quality and service levels are below ‘top box’, they should seek to identify those specific criteria (used by customers, not assumed by suppliers) where they’re judged not to have performed well – and put them right quickly before their sales suffer badly

And the only way suppliers can do this is by conducting regular surveys of a sample of their customers whilst being wary of the many pitfalls connected with doing this

Last, a new major buying criteria has recently become important to many customers, inevitably attracting yet another TLA (Three Letter Acronym) – this time it’s CSR (Corporate Social Responsibility)

More and more customers will avoid suppliers who apparently show no concern for human or animal welfare, dodge paying fair taxes, damage the environment or sell products which are bad for their customers in order to feather their own nests

To counter this possibility, some companies now make ‘ethical statements’ in their annual accounts – and Big Four accountancies such as PWC and KPMG now claim they can measure corporate reputations enabling them to identify where they’re wanting

P.S. There are many other more detailed customer measures possible (e.g. sales or profit per customer, % repeat business) but they all depend on a supplier getting the above cardinals right in the first place

Customers don’t measure up

Supplier organisations have two sorts of customers – external and internal:

  • External customers pay for the goods or services private or public sector organisations offer them – it’s their money alone which keeps businesses in business and public services alive, and pays every employee and shareholders dividends – they decide whether or not to buy or use products or services based on price, quality and service levels offered them
  • Internal customers are fellow-workers further ‘down-the-line’ who depend on you to supply them with raw materials, semi-finished goods or paperwork so they can complete their work – they want to be passed stuff which is ‘right-first-time, not faulty or late causing them extra work and delays which also increases costs

Given such importance, one would expect all organisations to closely monitor how satisfied their customers were with what they were being offered – but most don’t know such important details

Instead, they rely on measures of sales volume, value and trends plus their own internal views on how good they are – if they’re not negative, they assume all things must be hunky dory – hence, many are surprised when existing customers become ex-customers

To be fair:

  • Some survey their customers’ satisfaction levels, albeit most do this badly – too infrequent or too often, thus irritating customers – sample sizes and/ or methods used are unrepresentative or inadequate – hence, they rarely identify important areas where they’re going wrong
  • Some analyse customer complaints, returns and warranty claims but they’re usually half-hearted and little effort is put into making amends, thus missing a golden opportunity to convert unhappy customers into repeat sales – they can also suffer a double whammy here as unhappy customers usually tell more people about their bad experiences than happy ones do about theirs, so more potential sales are lost
  • Some count customer footfall and monitor their average total purchases

However, most organisations have no good idea what their customers really think about what is offered them

In addition, most don’t fully understand the nature of the demand for their services – they assume all demand is equally valuable

But much demand (e.g. output units, episodes, incidents) is repeat demand for the same good or service simply because the original demand was not met ‘right first time’ – goods or services provided were flawed in some way requiring extra costly time to put things right whilst earning no extra revenue or funding e.g. units having to be replaced or patients catching MRSA whilst a patient in hospital

In many organisations, such failure demand can be over 50% of their total, but few know it – and even fewer believe it when told

And the cost of such failure demand to the customers is never considered – e.g. the cost of their wasting time, suffering longer, having to cope with broken down units or making repeat visits to hospital for the convenience of consultants, not them


  • The business mantra, nowadays is “Put Customers First”
  • Such words are easy to say, and even easier to ignore
  • What most organisations actually do is put themselves first – they look inwards, not outwards – and they see only what they want to see
  • Hence it’s no surprise when experts claim there to be a long long tail of underperforming organisations in ALL sectors

Waste murders productivity

Whilst few managers measure productivity well, even fewer measure their waste of outputs and costly inputs

Waste arises both internally and externally:

  • Internal waste = When things are not done RFT (Right First Time) and work is rejected or has to be reworked
  • External waste = When things are delivered to customers either not as ordered or not to their satisfaction, so extra costs are incurred for no extra revenue e.g. replacing returns, making second deliveries, dealing with customers more than once to sort out their problems/ queries, patients catching MRSA whilst in hospital being treated

Such waste can be hugely costly viz:

  • Analyse customer demand, especially on service organisations and one can find at least 50% and up to 90% of all customers’ contacts are repeats because their queries were not dealt with right first time
  • To save money, eight London hospitals outsourced thousands of letters to India to be typed from dictaphones, but it didn’t go too well:
    • ‘Eustachian tube malfunction’ became ‘Euston Station Tube malfunction’
    • ‘Below-knee amputation’ became ‘baloney amputation’
    • ‘Phlebitis, left leg’ became ‘flea bite his left leg

The result of such waste is lost sales, extra input resource costs and/ or investment in additional output capacity well before needed

So what are the key measures of waste needed:

  • A% = % Availability of an input resource e.g. % labour not off sick and available for work, % materials in stock, % plant ready to go
  • U% = % Utilisation of said resource e.g. % of total time when at work spent productively rather than sat idle or attending a pointless meeting, say
  • E% = % Efficiency e.g. % actual total good output produced (after rejects, rework or replacements) versus the maximum possible – its capacity – e.g. how fast or slow it worked

The waste multiplier then comes into play viz:

AUE% = % Total net output/ Maximum output possible

It usually gives most managers some very unwelcome surprises – for example:

  • If labour A% was 90%, U% 80% and E% 70%, then the multiplier AUE% would be a mere 50%
  • And if a machine A% was 95%, U% 40% and E% 80%, then the multiplier would be a miserable 30%

In both cases, which are optimistic for many organisations, managers would surely be prompted not only ‘to do something’ but also have a steer on where best to act for biggest effect

But most managers do not have this information – they don’t realise such lost potential is typical, not unusual

In the NHS, for example, managers forever say: “Everyone is working hard and long hours so more cannot be done unless we get more resources” – so the government finds billions more to fund these extra resources, otherwise votes will be lost – taxpayers thus have to pay many more taxes than needed – meanwhile ministers and their advisers dabble with more NHS reforms without understanding what’s really needed or possible

And all because they lack basic measures of the waste of existing resources

Current productivity fog

All managers need to get the most out of all costly input resources they employ:

  1. In the private sector, to beat their competition in meeting customers’ needs whilst minimising unit costs and maximising sales and profit margins
  2. In the public sector, to optimise the number and quality of services on offer

However, most managers do not measure productivity well – they have plenty of financial productivity measures, perhaps too many, but little else – the result is most of them are far from getting the most out of their existing input resources

Why so – why should simple ratios of outputs over inputs not be found everywhere?

Survey after survey produce the same results – managers think productivity is too difficult to measure, not a boardroom issue, not the main determinant of their financial success, relevant only to the shop-floor or someone else’s problem

At the same time, leading management organisations and business schools ignore the subject – there’s no productivity headings or focus on their websites, nor any courses offered on it either – a sure indicator of the true importance they attach to the subject

So, given this paucity of measures and lack of interest at the top, is it any wonder that current productivity growth in most organisations and nations is (apparently) flat-lining

A big change in management attitudes is urgently needed

And, for this to happen, six major failings with existing productivity measures need to be addressed:

  1. A partial, not total, productivity picture painted:
  • Valued outputs nowadays are not just sales volumes (cars or insurance policies, say) but quality and service level outcomes for customers that go with those sales – yet performance measures that cover the latter are as rare as hens’ teeth
  • Wasted outputs i.e. repairs or replacements for no extra revenue, say – are often included in total output figures
  • Costly inputs include labour, materials, capex and IT systems – however, labour volumes (hours or FTEs) are the only ones measured – labour quality (i.e. skills, experience, education levels, qualifications) can make a big difference to productivity levels but is ignored
  • Wasted inputs – the time and resources spent on work which was not right first time, say, are usually submerged in total input figures
  • Hence, most productivity measures quoted can be seriously flawed and offer only a fraction of the ‘big performance picture’ needed – as a result, many productivity problems and improvement opportunities pass by unseen

2. A lack of useful benchmarks:

  • External best practice information is usually only sought by better private sector companies – and rarely by any public sector unit, even though such information should be in the public domain and readily available to them
  • However, caution is needed before copying another firm’s best practices – there will always be differences preventing exact copying but knowing others do things differently with better results should make managers ask whether they could be doing as well
  • Most organisations have no idea what their overall output capacity is because of the variety of goods and/ or services they offer – this means they don’t know how much more they could do with the resources they’ve already got – as a result, many invest big money in extra capacity well before needed

3. A lack of regular, timely measures:

  • Important performance information is often produced quarterly yet a manager may need it weekly, say, for her to take appropriate action in good time

4. Few clear links down/ up to lower/ higher levels of management:

  • Managers cannot drill down their set of measures to identify problem causes – nor up and across to those of others and check on the impact of any proposed changes
  • Different management levels often use quite different performance measures, so they don’t talk the same language, so they don’t understand each others’ problems
  • There should be just one set of performance measures linking all managers to all levels

5. Too short a time horizon often used:

  • Some companies have to invest heavily upfront (e.g. oil wells, coal mines), make big money with stuff that’s easy to extract, then less and less per barrel or ton as extraction becomes more difficult until all peters out
  • In such cases, whole-life productivity measures are needed

6. Too much aggregation destroys value of information:

  • One cannot mix quite different outputs, or inputs, to calculate overall productivity levels unless they’re converted into cash
  • This aggregation problem increases the higher one goes in any organisation
  • It’s worse still at national level where official productivity measures have been described as ‘pointless and unusable’ – they spread misinformation, not knowledge


  • Overall, productivity must be measured well if it is to be managed well
  • At present, few managers do this well – most are ‘flying blind’ and so not in good control
  • Managers need to make their decisions based on good performance data, not opinions
  • It’s at organisation level where most (80%?) national productivity improvement potential lies – and it’s managers, not workers, at that level who have the power to make the improvements necessary
  • But, to do this, they first need to know where, when and how to act

NHS targets have had their day

Lord Prior of Brampton is reported by The Times as saying: “NHS staff suffer from learned helplessness in a dysfunctional system”

So what prompted this mystifying statement?

A&E units are currently reporting their worst numbers of patients waiting longer than four hours, many on trolleys as no beds were available – the NHS Confederation of Managers says the system is “buckling under the strain of rising patient numbers”

Prior claims this is because targets, competition and a reliance on inspectors have led to a ‘disjointed system and demoralised staff’ – breaking up the NHS into autonomous hospitals has made ‘ driving an integrated strategy across the service almost impossible – you could not have designed something that had at its heart more dysfunction’

Chaotic organisation and overuse of targets has led to ‘a disempowered culture, a learned helplessness culture, a top-down looking upwards culture, a very hierarchical culture’ – targets which once worked well when waiting times were unacceptable ‘have had their day’

At present, hospital bosses are under such enormous pressure to hit their targets that there is now ‘widespread gaming of the system’ – frantic efforts are made to get patients out after three hours and 55 minutes waiting, but care stops once the target is missed

Hence, Prior seeks ways to address these cultural issues and bring back the vocation he remembers doctors and nurses once had viz:

  • Junior doctors would say ‘at the end of our day when we’re about to go home, we’d always walk back to A&E to lend a hand if there was a problem – now we go home’
  • When GPs and nurses qualified for their maximum pension, most would stay on for at least another two years – now they simply say: “I’m going”

The issue is how to regain that engaged spirit ‘which would take care of so many of the NHS’s other problems’

But Taj Hassan, president of the Royal College of Emergency Medicine, offers a cautionary note – “If policymakers and governments choose to scrap these targets, they must be held accountable for any impact on patient safety and the added risk of harm or avoidable death”


Targets still have a role to play in the NHS – the issue is not whether they’re needed (some are, some are not) but how the important few are used

A wholesale NHS management culture change is indeed needed – targets should never be imposed from ‘on-high’ and managers told to ‘get on with it (or else)’ – they need to agree their targets first, taking account of the resources available to them, and then be left alone to figure out how best to meet them – words like challenge, reward, empowerment and accountability should replace the current central control, hit-squads, penalties and micro-meddling that many hospital bosses fear at present

Zipf’s Law

Zipf’s law is a mysterious, empirical law – it’s also linked to Pareto’s rule:

  • It suggests limits on the size of companies and their share of markets
  • According to Annalee Newitz, the editor of i09, in 1949 linguist George Zipf noticed that people used a very small number of words most of the time – we minimise what we need to convey our messages
  • In particular, Zipf found that a pattern emerges – the most popular word is used twice as often as the second most popular, three times as often as the third, and so on
  • A mere 135 words cover 50% of all the words we ever use on a regular basis
  • The most popular three words are:
    • The = 7% of occurrences
    • And = 3.5%
    • Of = 2.3%
  • Zipf’s law must surely offer a clue as to how Alan Turing and his Bletchley Park team were able to break the Enigma code – although I’m guessing here
  • Zipf then found his law also applied elsewhere:
    • To income and wealth distributions in any given country, where the richest have twice as much money as the next, and so on – much as Pareto observed many years before him
    • To the size of cities, where the city with the largest population in any country is generally twice as large as the next biggest, etc. – this only applies where cities are economically integrated, with common language, laws and institutions, as in any nation – it does not apply to any group of nations like the EU
    • To the size of firms in any sector – the biggest firm is twice the size of the next one, three times the size of the next, and so on – hence, it’s inevitable to end up with a group of Big 4, 5 or 6 companies in any sector
  • Other interesting applications include:
    • Books borrowed from libraries
    • Web sites visited
    • Earthquake sizes
  • Quite why the pattern is followed so closely is not understood
  • However, it offers useful predictability for economists and businessmen alike

Wealth gains and distribution

US Fed Chairman Jerome Powell believes our two greatest challenges for the next decade are ‘the widening wealth gap and sluggish productivity’

But Lawrence Fuller, in an article for Seeking Alpha, claims the Fed’s attempts to create a wealth effect by inflating the value of financial assets has mostly benefited the top 10%, and even more so, the top 1% of the population

New found wealth has not trickled down to the middle classes in the form of wage gains – according to data from the Congressional Budget Office, income gains since 1980 are as follows:

  • 242% – Top 1%
  • 79% – Next 19%
  • 78% – Middle 60%
  • 46% – Bottom 20%

Given the above, one might have thought a surge in investment in plant, equipment and employees would follow

Not so

Instead, corporations have returned capital to shareholders via stock buybacks and dividends – some even took advantage of very low interest rates to borrow capital to fund them

This simply filled the pockets of managers and shareholders but did little for productivity improvement and employees’ wages and their quality of living – it also reduced the latter’s demand for more goods and services and so the revenue and earnings of those same corporations

Hence the wealth moat between the very wealthy and the rest has been widening over the last few decades

However, Fuller expects ‘wealth disparity and income inequality to revert to the mean’ over the coming decade:

  • Current trends are not sustainable 
  • Economies should work for everyone
  • Strong headwinds are expected as capital shifts from the ownership to working class

“Our economy cannot be considered healthy when 40% of adults can’t come up with $400 in the case of an emergency”

4 day weeks to boost productivity

new report by Autonomy – a thinktank focusing on the future of work – argues that a shorter working week should be a central pillar of our economic future.

They say calls for a shorter working week have gathered pace in recent years, with the TUC, the Green partylarge and small unions and now the shadow chancellor, John McDonnelljoining the chorus.

Why so?

Because we are working longer days but for stagnant wages and receding state pensions – and some of the most productive economies in the world work far fewer hours collectively than the average UK worker.

They say productivity relies not just on the sheer number of hours put in but on the wellbeing of the workforce – as well as investment in labour-saving technology.

At present, they claim heavy workloads, work-related stress and anxiety are costing millions each year, with one in four sick days being lost as a direct result of workload pressures – shorter working weeks and greater worker control over working time would mean fewer sick absences, fewer in-work accidents and higher motivation on the job – all of which would be good for business too.

Hence the Wellcome Trust has just announced plans to trial a four-day week without a loss in pay this year, possibly making it the largest company to do so anywhere.

In this same vein, at the recent World Economic Forum (WEF) in Davos, Gitura Mwaura says the world was urged to embrace the four-day working week, busting the notion that long working hours lead to more productivity – a shorter working week not only improves productivity by some 20% but has an overall effect on the well-being and work satisfaction of employees

Examples put forward include:

  • South Korea which ranks near the bottom of OECD (Organisation for Economic Co-operation and Development) countries for labour productivity despite having a culture of working very long hours
  • Greece which has one of the longest working weeks but comes out bottom in the OECD’s measure of GDP per hour worked
  • And Japan which also has a culture of long working hours but emerges bottom of any G7 productivity list – hence, they are now deliberately cutting down on working hours, including overtime, to alter this position

However, other studies show there to be no correlation between long working hours and productivity — Germany is said to be more productive but works fewer hours on average than the UK

And in Sweden the WEF observes that “although employees report an improved quality of life, with less stress and more time to spend with their families, it can also be an expensive experiment for some businesses which have to hire extra workers to make up for the shortfall in hours”

Aidan Harper, the Autonomy report’s editor, concludes:

  • The past century has shown us that automation technologies have more often than not been introduced by employers as a way of simply maximising productivity without sharing the surplus time and/ or the profits with employees
  • The proceeds of automation should be shared evenly — in the form of a working time reduction
  • Machines should liberate us from work, not subject us to ever-increasing inequality
  • But few suggest workers should enjoy any of the benefits

This mindset clearly cannot continue

Companies would do well to consider a report by Minda Zetlin, co-author of The Geek, in an article for Inc. magazine on a company moving to a much shorter working week

Could you run your company just as well if employees worked a five-hour workday instead of an eightt-hour one?

If Australian financial services company Collins SBA is anything to go by, you probably could. And you’d benefit from better work-life balance, higher employee morale, and improved recruiting and retention. Your staff would take fewer sick days, and productivity would likely rise.

It may all sound too good to be true, but Collins SBA has been offering its 35 employees the opportunity to quit work between 1 and 2 p.m. for two years now, and it’s been a resounding success, managing director Jonathan Elliot told TNW. The shortened workday came about because the company, like all companies, was struggling to recruit the talent it needed in a very tight labor market. At the same time, Collins’s wife became ill with cancer. She needed surgery and chemotherapy and went through a long recovery process. The couple also had a 6-month-old daughter, which meant that Elliot needed to spend much more time at home taking care of them both than he ever had before.

He learned to be incredibly efficient. He stopped spending time chit-chatting with colleagues at work. He cut out unnecessary meetings. “I just focused on work and got home in time to look after my family,” he said. 

When his wife got better, Elliot was free to go back to working longer hours. That’s when it struck him that he didn’t really need to. By working shorter hours more efficiently and cutting out meetings and lunches, he’d been able to get the same amount of work done that he’d previously been doing during a full workday. And so, partly inspired by Tower Paddle Boards, which cut its workday to five hours without sacrificing any productivity, Elliot pitched his colleagues and shareholders on trying out the new schedule throughout the company. They agreed.

Elliot didn’t simply declare that everyone could now work five-hour days. The new workday came with a few new rules. First, employees must arrive between 8 and 9 a.m. if they want to leave between 1 and 2. Second, their work responsibilities remain the same, and they must get their work done, even if it takes more than five hours. Third, unless specifically approved, they can’t have any personal appointments during their workday. And finally–of course–they shouldn’t go out for coffee or lunch. Instead, Collins SBA provides coffee and healthy snacks in the office. The company also now holds no one-hour meetings unless there’s absolutely no choice. And all employees have gotten training to help them manage their email more efficiently.

Can Collins SBA employees really get done in five hours everything that they were previously doing in eight? Well, no. Most employees have some workdays that last five hours and others that last six or seven, Elliot told TNW. But they don’t often work 40-hour weeks, or even 38 hours as specified in their contracts. In the end, what Collins SBA offers employees is really a flexible work schedule and the opportunity to leave work after five hours if they’ve finished their tasks for the day. In essence, it’s a powerful motivator to be more efficient, and to home in on the 20 percent of effort that yields 80 percent of results, as the Pareto Principle says. There has also been a 12 percent reduction in sick leave.

Not everyone loves the new schedule. Some employees left because of it. Elliot says the idea has proven surprisingly polarizing. And while most clients have supported the idea, a few have blamed the shorter workday when they were unhappy over other issues. However, this didn’t happen until the change had been widely reported in the press–before that, clients hadn’t noticed it. That in itself proves the new workday is a success, Elliot said. “If we can implement this covertly, we are doing it right.”

As you might expect, those same press accounts caught the attention of prospective employees. Elliot says the company’s candidate pool is bigger than it was, and some candidates are contacting the company to inquire about working there even when they weren’t responding to a specific ad for a job.

It’s also helping the company screen out some candidates who would likely make unproductive employees. “If a job candidate brings up our five-hour workday very early on, that’s a red flag,” Elliot says.

Productivity tops Brexit

An article by Peter Barker, Gui Tao and Xinhua –

Improving productivity, instead of the Brexit issue, is the primary task facing the British economy at the moment, says renowned British economist Jim O’Neill

“The UK being in or out of the EU (European Union) is not the most important thing facing our economic future, and I strongly believe that,” O’Neill, chairman of Chatham House think tank in London, told Xinhua in a recent exclusive interview

For O’Neill, Brexit is very much a short-term problem and he has a longer vision when he looks at economic issues. “Doing something about our productivity performance and our geographic inequality and our inter-generational inequality, these things are way more important (than Brexit),” he says

Britain has suffered a slump in its productivity growth since the financial crisis – (according to official, albeit highly suspect, national data) this slowdown has been more dramatic than that of any major Western economy, with annual growth in productivity falling from an average annual rate of about 2.3 percent before the financial crisis to 0.4 percent in the past decade

“So, even a hard Brexit, which would be very bad at first, isn’t as important as those things. The contradictory part is — why the hell would we deliberately make our productivity challenge even worse by choosing to have no trade arrangements with the single biggest economic trading zone in the world?”

“It doesn’t really make a lot of sense.”


O’Neill cited his own industry of finance and the successful industry of auto assembly as areas where the economy could suffer after Brexit because there could be greater friction and costs to both trade and imports, which would eat into businesses’ profit margins.

In 2017, the British auto industry built 1.3 million cars for export, accounting for 12.8 percent of total British exports, according to motor industry statistics.

“The auto industry, which in modern Britain is arguably the most successful of any traditional industry, produces more cars today than 40 years ago — it would be decimated under World Trade Organization (WTO) rules,” O’Neill says

“Some of the world’s most productive auto plants are in the UK, and if we go out under WTO rules they won’t stay that way because the profit margins are too thin — I’m sure there are many other industries where that’s true.”

“My old industry of finance would have some interesting challenges,” O’Neill adds, and challenges the idea that Britain could leave the EU with a managed no-deal.


“There are a lot of people in the Western world who don’t believe, because of the circumstances they’ve lived in, that the past 30 years have helped them at all,” O’Neill says

“When our Chancellor of the Exchequer often says ‘we didn’t vote to make ourselves poorer,’ actually a lot of people that are poor in the UK might have voted to make themselves poorer, because they want to shake up the system.”

“They don’t really understand the degree of sacrifice they might make, but they don’t mind sacrificing growth in the UK, because they’ve not benefited from (it) anyhow,” he added.

“There’s a commonality with this (thinking) in the U.S., there’s some commonality of it in many parts of Europe, and it is clear that — as fantastic as globalization has been for China and for many other places, and for the elite world that I’ve lived through — there’s a lot of lower-income, working people that have not benefited much from the past 30 years, and it’s easy to blame globalization.”

P.S. Sadly, the powerhouse thinker Lord Jim ignores the fact that many Brexiteers voted to leave, not from a misunderstanding of the economic/ productivity implications as trotted out above but for:

  • Control – over immigration
  • Control – over the laws we live and work by
  • Control – over who rules us – better our second rate Brits than third rate unknown foreigners with Germany driving them from the back seat
  • Control – over who we can trade with, worldwide, and how

And as for making no sense ‘having no trade arrangements with the EU’:

  • The EU market is stagnating whilst the rest of the world (RoW) is growing, rapidly is many parts – however, the UK is currently not allowed to address RoW markets separately
  • The UK exports less than 10% of its GDP to the EU, not 50% say, so it will not be catastrophic, overall, if this % is dented – and as the EU exports more to the UK than we do to them, self-interest on both sides will ensure most of this trade will continue somehow
  • In the short term, there may be import supply problems for some sectors – but that may well encourage many UK start-ups to replace some of these imports  and so be good for us in the long term
  • The EU is essentially a ‘rich members club’ which insulates itself from RoW competition by a mix of tariffs and trade agreements so members become richer still – such protectionism is not only bad for EU (and UK) productivity but also morally abhorrent for putting up barriers to other nations, especially poorer nations, seeking to better themselves and so widening, not closing, prosperity gaps

Clusters need roads

An article by Maria Machancoses,  a director at Midlands Connectvestment, is fully reproduced below

For centuries, good roads have influenced the way we live, work and trade

As a nation that makes over 80% of journeys by road, and whose population is forecast to grow to 75m by 2050, investing in our ageing infrastructure is rightly at the top of the agenda.

Rather than developing proposals in isolation, roads investment is now recognised as a boost to productivity and a catalyst for regeneration.

With this in mind, economic road corridor approaches to investment, new central government spending pots and continual digital innovation are set to take centre stage

Economic corridors – Clusters

The need for the UK to forge a new place for itself in a post-Brexit world and strengthen both domestic and global market access has seen the re-emergence of the corridor approach to economic development.

While corridor-led schemes are prevailing in places like Singapore, India, China and Pakistan, ground-breaking strategic proposals are also being proposed here in the UK. They take encouragement from the continued success of the M4 corridor’s so-called ‘Silicon Alley’, the largest tech cluster in the UK outside London, turning over £10bn each year.

Plans for a London-Stansted-Cambridge corridor, a Cambridge-Milton Keynes-Oxford trade highway and Midlands Connect’s own calls for urgent, co-ordinated development along the length of the A46 are all emblematic of this trend.

By linking economic centres to each other and the rest of the UK, corridor development schemes widen access to labour pools, boost business productivity and increase the reliability and resilience of the network.

Promoting nationwide connectivity and collaboration is essential if we’re to address regional inequalities and rebalance our national economy.

Hence a Major Road Network (MRN) should be formally established by the government, following an indicative funding commitment of up to £3.5bn from 2020-2025.

Creating an additional tier of roads between the Strategic Road Network, managed and maintained by Highways England, and local authority roads, Midlands Connect and other sub-national transport bodies have called for greater influence over where this pot of money is spent, to ensure it draws on regional expertise and prioritises schemes with the highest potential for economic growth.

However, MRN funding pales into relative insignificance when compared to the second Road Investment Strategy pot – RIS2 – which is worth over £25bn. Where, when and how this will be allocated to both the delivery of regional priorities and the development of new projects, could be the most important event of the near future

Digital infrastructure

It is increasingly clear that we cannot address the mobility challenges of the future with construction alone. The greater availability of data provides us with new opportunities to better use our existing infrastructure and build networks fit for the future.

The increased uptake of electric vehicles (EVs) and roll-out of 5G networks will change the way we plan, develop and improve road networks in the coming year and beyond.

EVs aside, expect to see an acceleration in the testing of connected and autonomous vehicles and HGV platooning as 5G connectivity and the internet of things continue to revolutionise the way we travel.

As well as incorporating traditional charging points infrastructure into transport plans, we will also see further consideration of more radical technologies such as electric charging lanes, which are already in use in Sweden.

The future of our road infrastructure is inexorably linked to economic regeneration, emerging technologies and new government funding strategies.

As corridor approaches pave the way for enhanced inter-regional and international connectivity, the UK is well placed to become a more balanced, productive and innovative society

Current knowledge levels

Many, perhaps most, developed nation companies are brainwork companies nowadays i.e. at least 33% of their employees have degrees or equivalent

Clearly, all top jobs require best brains/ problem solvers – there’s little routine work for them – it’s their ideas, decisions, tactics and plans, actions and people skills that are needed most

But the same logic now applies to the majority of workforces – most jobs require more brain than brawnpower – in addition, they increasingly require an interaction with and use of ICT systems for them to do their jobs well

This means perhaps less than 20% of all jobs require mostly brawnpower – simple instructions must be followed – little knowledge/ skills/ experience and so training or experience is needed

Hence, some 80% of any developed nation’s workforce relies on brainpower and considerable training to do their jobs well

But training in what?

At present all UK kids are forced to go to primary and then secondary schools to learn the same range of basic subjects up to age 15:

  • Many then leave either to earn a living or attend some apprenticeship course – the former usually find employment opportunities and pay levels for the unskilled are meagre, the latter are (currently) limited in choice and made to feel second-rate versus any degree course
  • Most that stay on to their sixth form have to choose between studying arts or sciences, but not both
  • And most that go on to university have to further specialise, their choice of subjects usually made on the basis of what they like and/ or are ‘good at’
  • Throughout this whole process, ‘careers advisers’ might get involved but, in my experience, their inputs are either useless or worse
  • Nor do the kids get any significant nudges from either government or industry when choosing what to study

The end result is UK kids emerge into the world of work having cost a fortune being educated, whatever the level, but not knowing much of what they need to know

Is it any wonder businesses forever complain of a mismatch between skills available to them and those they need?

And that’s not the only problem nowadays – whilst many UK universities are ranked among the best in the world, there has been a huge expansion in their total number – this has led to a dilution of degree standards which is infecting the whole tertiary system viz:

  • Given students are universities’ customers, many choose where to apply (if not a top university) partly based on the likelihood of being marked well – hence most universities now award first or 2.1 degrees to well over 50% of their customers
  • Many lowly-ranked UK universities are struggling to attract sufficient numbers of students – aka income – to cover their costs and so are lowering their entry standards, even greatly increasing unconditional offers to poorly performing applicants, which has obvious knock-on effects later on

The inevitable result is that many employers are now strongly biassed towards graduates from the ‘top ten’ or Russell Group universities – hence a first from a tin-pot university may only lead to a life of shelf stacking

A major sieving of the long tail of UK universities is thus needed, and soon, before too many kids rack up too much student loan debt and have their dreams shattered

What’s needed is a raft of technical apprentice colleges, but call them something grand to stop people looking down on them – as per MIT (Massachusetts Institute of Technology) offering courses up to Bachelor and Master degree level in skills not only business but the general public desperately need i.e. not only engineers and computer scientists but electricians and plumbers:

  • A start has indeed been made with many more apprenticeships on offer
  • However, in most people’s opinion, the very word ‘apprenticeship’ downgrades the value of current courses on offer versus any degree
  • And take-up of these courses has also been disappointing to date

Worst of all, there’s still no clear steerage or incentives from business or government for kids to obtain the skills the nation most needs

And the above ‘training gaps’ only relate to kids at the start of their careers – delve into the training most companies offer their employees after their start and the picture is equally pitiful:

  • Some have formalised induction programmes – most are superficial at best
  • Many view training as a few days junket at the expense of the firm, and no use to anyone afterwards
  • The more senior you are, in the West, the less the training on offer despite the rapid changes ongoing these days

Yet most staff at all levels are usually keen to upskill themselves – and they look to their employers to help them do this – however, many companies don’t recognise that employee training should be good for them too by helping their employees be more productive

Overall, companies should thus take note of surveys that show those with formalised training and workshops have at least twice the income per employee compared to the rest – they also enjoy big increases in productivity and sales whilst reducing stress and attrition


At present, there are training pot-holes all over the national road to increased productivity and prosperity – and there seems to be no concerted effort by those in power towards filling them in properly

Knowledge measures needed

Management guru Peter Drucker once said: “In the knowledge economy, everyone is a volunteer, but we have trained our managers to manage conscripts” – he might have added that managers act this way because they lack the measures and understanding needed to maximise the knowledge productivity of their teams

It’s another productivity gap afflicting most organisations and nations

Yet, according to Thomas Stewart in The Wealth of Knowledge: “Knowledge is the most important factor in production (business) and knowledge assets the most powerful producers of wealth – even your toothpaste is more the product of knowledge than any other input resource because R&D and marketing costs comprise more than 50% of its total cost”

Winning companies nowadays are not just cash-rich but corporate-knowledge-rich too – many winners are even ‘tangible-assets-poor’

Corporate knowledge (K) includes:

  • Designs, patents, formulae, copyrights, trademarks
  • Brand names
  • Customer contacts
  • Process knowhow, ‘best practice’ knowhow, learning curve experience i.e. knowing not only what works but what does not
  • People skills and experience

Corporate knowledge (K) is thus said to be the sum of everything everybody in a company knows that gives it a competitive edge (aka a large moat) – and all held either ‘in files’ or ‘in heads’

So what’s K for?

It enables organisations:

  • To invent – to add to or replace existing offerings
  • To innovate – to improve existing offerings
  • To control operations and minimise unit costs
  • To increase output volume, quality and/ or service levels

Hence it’s crazy not to be able to manage and control such a powerful and valuable input resource

To do this, managers first need some indicators which show their teams’ actual K position – at present, most have little idea of where they’re falling short, by how much or the potential they’re ignoring

The least they need are subjective assessments of the availability, utilisation and efficiency of use of the K within their walls viz:

  • KA% = K Availability % = % Actual K available/ Total K needed:
    • ‘In heads’ = % assessment of the actual skills, qualifications and experience within a team versus that needed
    • ‘In files’ = % assessment of the actual important K recorded rather than being resident solely in heads and thus liable to be lost if those heads just ‘walk out the door’ – BP (Best Practice) databases are rarely maintained yet are vital in, for example, the public sector
    • ‘Accessibility’ = An assessment of the % of K resident in heads or files which is readily accessible by others in the team
  • KU% = K Utilisation % = % K used by others/ Actual K available
    • % of K in team heads used by others – team members can be unaware of what others in-house know – they need to know who are champions in specific areas who can be spoken to – and there are few mechanisms to encourage team members to talk to each other and exchange K – some hoard specific K believing ‘knowledge is power’ but organisations need to tear down such K silos
    • % of K in team files used by others – at present, wheels keep being re-invented rather than improved upon
    • % of K outside the team but within corporate walls, whether held in heads or files, used by the team – this requires some form of taxonomy, an information classification and tagging system, to make K easily findable and facilitate sharing
  • KE% = K Efficiency % = % Operational efficiency/ Maximum 100
    • aka Kleverage = How effectively K is used
    • An overall % rating assessment of current products/ services, market share, customer satisfaction levels and key processes reflecting their scope for improvement

Such subjective measurements, if agreed by a group of internal managers rather than just the manager concerned, would not need to be deadly accurate – you don’t need to know someone’s precise weight to know if they’re fat or not

However, the K review process of peer-discussions first followed by peer-agreed results would force any manager to consider her team’s actual K position more than ever before – and thus be more likely to identify where and when big changes were needed to benefit both the team and the organisation

For example, if a team’s results were KAUE% = 80% x 50% x 70% = 28% (an indication of the waste of K ongoing) then clearly ‘something must be done’ – probably by first addressing KU% and the need to share in-house knowledge better

Aggregation hides info needed

Current measures of productivity become less and less useful the higher the level they go:

  • Aggregation increasingly blurs the performance picture
  • Apples get mixed with pears
  • Specific inputs used for specific outputs and outcomes get lost in the mix

At national level, this aggregation problem is at its worst, compounded by much output and most input being uncounted or uncountable rendering official statistics useless for managing the economy and meaningless for any manager struggling within it

At organisation level, different outputs in the private sector can be counted either separately or together if converted into cash

However, the latter is not possible in the public sector where outputs (of most services) are provided free at the point of delivery and so have no price attached – hence official statisticians employ estimates and assumptions to complete their calculations, thereby introducing considerable errors which further blur the picture

And, in all sectors, costly inputs counted are confined to volumes of labour (hours or FTE numbers being easily measurable) whilst quality of that labour (skill levels, experience, morale), raw materials, SFGs (semi-finished goods), capital investments, IT systems and corporate knowledge are all ignored

The result is most national productivity figures cannot be trusted for an ‘accurate fix’ on the current national position, nor trends being followed, nor relative productivity gaps with other nations

Dare to claim this in public and the only credible defence one hears is: “They’re the best and only measures we have”

One response heard is: “If that is so, then ignore them – better to stick your finger in the air and just hope”

We say: “Surely it is not beyond the wit of man to find a set of measures useful to those on any bridge which helps them avoid rocks ahead, take advantage of wind-shifts and compete with the rest of the fleet”


  • Officials should accept that, at the macro level, it’s impossible to measure productivity in any useful way
  • What ministers at national level and managers at organisation level need is first, an alarm bell system to warn of dangers and opportunities ahead – then a framework of measures enabling them to drill down to levels where productivity measures are meaningful and useful
  • Only then, would ‘officers on watch’ have a suite of productivity measures which put them in good control for navigating their ships safely


UK productivity gap half-explained?

According to Philip Aldrick, Economics Editor of The Times, Britain’s dismal productivity gap with much of the developed world is due not only to lack of investment, bad management and low interest rates as previously thought

Another significant causal factor has been found

The UK’s ONS – Office for National Statistics – asked the Paris-based OECD – Organisation for Economic Cooperation and Development – to look into the consistency of national data produced by 40 different countries and they found ‘the maths used leads to misleading results’

It turns out there are differences in the adjustment of official figures used to calculate hours worked and employment levels – different countries make different adjustments for their self-employed, overseas workers, prison workers and even drug traffickers and sex workers – and for workers’ tendency to underestimate holiday time taken

For example, France marks down employees’ reported hours by nearly 20%

Hence, comparisons of national labour productivity levels – national output (GDP) divided by national hours worked – end up being ‘apples with pears’ comparisons

If the UK made the same sorts of adjustments, it is estimated their labour productivity would increase by 10%

And actual labour productivity gaps between the UK and France, Germany and the USA would be much smaller than officially thought viz:

     . 16%, not 24%, less than USA

     . 14%, not 22%, less than Germany

     . 11%, not 19%, less than France

So, whilst the finding of these errors does not explain away the apparent productivity gaps between the UK and other developed nations, nor cover the errors that abound when assembling all national productivity data, it does suggest things may not be quite as bad as once thought, at least in the UK 

So let’s leave the last words to Richard Heys, deputy chief economist at the ONS: “This research reveals some striking differences in the way different countries estimate the amount of work taking place – however, they don’t explain why productivity growth has been so stubbornly low for so long”


Work hard or work well?

Many say the secret for a good life is ‘work hard and play hard’

Leila Hock, in an article for Career Contessa, disagrees – ‘work hard’ apparently “makes my eyes roll a little”

She believes we’ve become too preoccupied with “the grind” and it’s actually bringing us down – “It has a negative effect on productivity”

When people say they’re working hard they mean they’re putting a lot of time in – this mindset is because our economies once hinged on time:

  • Workers ran machines or performed rote tasks, and those machines and tasks would give a pretty static output per hour
  • Occasionally, someone would find a way to increase output per time unit but, usually, more time spent led to more productivity


Nowadays, developed economies have transformed into knowledge economies, and they require brainworkers/ thinkers to produce new/ better ideas, decisions and results

The problem is that appropriate performance measures to monitor their progress at work have not been developed – instead, the old familiar industrial-age measures and thinking continue to be used for the new economies

People still tie time to the value of work, not least because measuring time is easy – it’s a number and numbers can be easily compared

Hence, when most managers see someone arriving early at the office, leaving late and responding to emails at all hours of the night, they usually think said employee is committed to her work and trying hard – why would she spend all that time that way otherwise?

What most managers need to do is start measuring the value of employees’ work – and that means truly understanding why they were hired and what they were required to produce – and it’s not just the quantity but the quality of their output that now matters

Few managers do this at present, however, not least because it would take considerable time – and as hours input wins their attention more than productive work, such an exercise is deemed ‘a waste of valuable time’

Consider also the professions that still bill clients solely by their time inputs rather than ways which reflect quantifiable results achieved – and who value their employees by the hours/ days billed regardless of the value obtained by the clients – for example:

  • Lawyers
  • Management consultants
  • Accountants
  • Marketing and PR consultants


Leila ends up saying that, instead of such archaic thinking, what’s needed nowadays is a focus on ‘working smarter, not harder’ for the benefit of both customers and employees


  • Success is no longer determined by hard work and long hours
  • Success comes from using time productively and being effective
  • That requires a focus on what one is trying to accomplish each day and week
  • And, once completed satisfactorily at least, one should relax

All nations need a National Productivity Centre

An article by Lalin Fernandopulle in Sri Lanka’s Sunday Observer, headed ‘Productivity policy vital for economic growth’, promotes the worth of all nations having a National Productivity Organisation 

Sri Lanka is the only APO (Asian Productivity Organisation) member country which does not have an NPO (National Productivity Organisation).

Company director Sunil Wijesinghe says: “Setting up a fully-fledged stand-alone NPO is the way forward for industrial and overall economic growth in Sri Lanka”

He said their National Productivity Secretariat (NPS) is still only a unit under a Ministry while in Singapore and Malaysia they are powerful statutory bodies.

The USA was the most productive nation at the end of the World War 2 – Japan realised Asian countries lagged behind in economic growth and initiated the Asian Productivity Organisation (APO) in 1961 with Asian member countries – Sri Lanka too joined, albeit a few years later

Most other Asian countries had open economies at that time, and developed their productivity programmes fast

For example, the Japanese Government carried out a massive program to inculcate good productivity habits and promote productivity techniques and practices in the 1960s through radio and TV programmes but later it was the private sector that carried it forward through the Japanese Union of Scientists and Engineers (JUSE) and the Japan Productivity Centre for Socio Economic Development (JPC-SED).

At the start of the National Productivity decade in 1996 Sri Lanka started emulating Singapore but later the focus changed.

Singapore claims their productivity programmes have helped economic growth substantially – they had the highest patronage with former Prime Minister Lee Kuan Yew initiating the programme when the annual productivity theme was launched each year – the initial focus of the program was to make government institutions more productive.

A few Sri Lankan enterprises have adopted good productivity practices while others lag. We need a massive re-launch of productivity enhancing programmes in Sri Lanka.

Not only industrial growth but also overall economic growth can be influenced by productivity because productivity improvement techniques can be applied not only in factories but also in offices, plantations, schools, government offices and even homes

Sri Lanka lags behind in industrial growth since economic policies are not consistent – frequent policy changes wreak havoc on the strategies of private companies.

What is needed is for policy makers to prepare a comprehensive medium-term strategic economic plan, in a similar way to strategic corporate plans – Singapore prepared a Strategic Economic Plan in 1990 and stuck to it.

Thereafter we need to communicate it to the people using tried and tested change management programmes so that the population buys in to it.

The ideal would be economic policy stability even with changes of government.

During a productivity study tour to Singapore in the 1980s, and following a briefing at the then Singapore Productivity Board, one of our Sri Lankan colleagues visited the wash room and, having seen a notice there which said “20 dollar fine if you don’t flush”, came back and asked the Director conducting the briefing how they could identify who the culprit is. His response was: “How come only Sri Lankan visitors ask this question? The notice in the toilet is a mere deterrent,” he said.

He said having observed the happenings in Sri Lanka, Singaporeans believe that Sri Lankans are overly legalistic, and this hampers progress.

Today every newspaper, radio and TV channel gives pride of place to (anything other than) coverage of management, productivity, or economics

We should focus on building up our economy and improving the productivity of our enterprises

And setting up a properly resourced NPO would be a good start

N.B. The same void exists in the UK where there is no well resourced/ well-known UKPC – Why?

UK industrial strategy

The UK government’s ‘Industrial Strategy’ for making the UK more competitive and the economy better-balanced essentially involves increasing R&D investment and workers’ skills

It considers five areas for productivity improvement – Ideas, People, Infrastructure, Places and Business environment – and recognises four grand challenges:

  • Artificial intelligence and machine learning
  • Clean growth
  • Future mobility
  • Ageing society

In particular, UK Prime Minister Theresa May has since confirmed her commitment to raise R&D spending to 2.4 % of our national income – an increase of £22 bn over the next 12 years when it increased by just £6.6bn over the last 12 – her aim is to help the UK become “the ideas factory of the future”

So is the strategy working?
The biggest challenges for smaller (SME) manufacturers are poor cash flow, high energy costs, reduced margins, competition from Asia, lack of skills and an ageing workforce i.e. most not listed in the grand strategy
So what help is on offer to these vitally important SMEs?
  • The HVMC – High Value Manufacturing Catapult:
    • The HVMC is a network of seven centres who work with industries, large and small, to prove and de-risk technologies that can be adopted in their own factories to improve productivity and quality
    • It’s funded through the BEIS – Business, Energy and Industrial Strategy Department – and Innovate UK – the national innovation agency
    • It’s tasked with engaging SMEs and measuring the impact of the Catapult on improving SME competitiveness
  • The ‘Made Smarter’ programme, led by Siemens CEO  Jürgen Maier:
    • The programme facilitates the adoption of digital manufacturing technologies such as robotics and automation, augmented and virtual reality, artificial intelligence and machine learning
    • The aim is to unlock big improvements in productivity
    • The approach is to link with existing growth hubs
    • However, some say that while it may eliminate duplication, its success will depend on getting people in post who understand industrial digitalisation and the challenges of change-averse business cultures
  • And, only recently, an independent council has been set up to oversee the delivery of the ‘Industrial Strategy’, headed by Andy Haldane, chief economist at the Bank of England:
    • Haldane is not a manufacturer but qualifies, apparently, because he’s ‘familiar with monitoring government performance on key economic indicators’
    • The council will scrutinise R&D spend, seek to keep the UK economy on track and assess whether the strategy’s aims are being delivered
So the UK might be said to have a productivity plan – expert advisers are in place, universities are being encouraged to join in, professional monitors are watching key indicators, billions have been allocatedWhat could possibly go wrong?

Well quite a lot, actually

For a start, this UK plan addresses a mere 15% of its total economy i.e. the manufacturing sector alone

And David Parker, New Zealand’s Minister for Trade, might well take a different tack – he recently outlined their plan to boost productivity – ‘creating more from our resources while staying within environmental limits’ – and so lift the standard of living of all Kiwis – a suite of sector-led Industry Transformation Plans they copied from Singapore, each one unique to the sector and the actions required, including investment, innovation and skills development:

  • The key industries chosen are agritech, digital technologies, food and beverage, forestry and wood processing
  • They bring together two of NZ’s key competitive advantages – sectoral expertise and the educated workforce
  • The overriding need is to move from volume to value in these sectors
  • There has been too little investment in growing their productive enterprises for competitive advantage

Parker says:

  • “NZ is facing new challenges and opportunities due to technology – the nature of work is being profoundly affected by AI (Artificial Intelligence) and automation, but this is also creating opportunities for businesses who innovate and develop their businesses using such technology”
  • “The higher the productivity of a country, the higher the living standards that it can afford and the more options it has to choose from to improve well-being – well-being can be increased by things like quality healthcare and education, excellent roads and other infrastructure, safer communities, stronger support for people who need it and improved environmental standards”

All splendid food for thought

Financial cardinals needed

Of the many financial measures available, only three qualify as financial cardinals – the ones whose alarm bells must ring to prompt action in good time

They are total revenue, total cost and profitability

They’re ‘catch-all’ measures covering all outputs, outcomes and inputs:

  • Total revenue covers net outputs sold and outcomes the customers took into account before making their purchases
  • Total cost covers the mix of costly input resources used
  • Profitability covers how well those input resources were used – a ‘total productivity’ measure in effect


Trends in each one need to be regularly monitored

In the private sector, the three must be monitored together – otherwise managers might be tempted to make themselves look good by boosting one at the expense of another

For instance, senior managers have been known to buy other companies to boost their revenue and profits growth record – however, their capital employed will also have increased so profitability, not profits, may well have fallen

In the public sector, there’s only one financial cardinal – total cost

Overall, if something goes wrong, it may not show up in the financial cardinals – minor failures can be cancelled out by minor successes when results are aggregated

But, if something goes badly wrong ‘below decks’, it should be noticed not only by the manager responsible but also his peers – transparency and honesty between them all are key

And once understood, quick action will be vital – hence, financial cardinals must always be presented in good time, not months later as many are

To quote Dr Devi Shetty, chairman of the Narayana Cardiac Hospital in India: “If you get the profit and loss at the end of the month, it’s a post mortem, the patient is dead – if you get the profit and loss daily, it’s a diagnosis and you can treat”

Financial metrics are not enough

  • How do you know if an organisation has performed well?


  • If it’s a private company, financial results will reflect customers’ valuations of what they were offered and translate them into revenue and profits


  • If it’s a public sector unit, the tax-paying public will judge quality and service levels received – actual costs are not their concern unless their taxes become unacceptably high – until then, they leave it to service unit managers and government ministers to manage resources needed and so costs


  • Currently, there’s a glut of financial measures available and, confusingly, more than one definition for some of them – they include:
    • Free Cash Flow – FCF
    • Net Assets – NA
    • Capital employed – CE
    • Return on Sales – RoS
    • Return on capital employed – RoCE
    • Asset turn
    • Added value – AV
    • Economic value added – EVA
    • Gearing
    • Working capital
    • Liquidity


  • The problem, as Peter Drucker once pointed out, is: “Financial accounting is an X-ray of an enterprise’s skeleton but most of the diseases we commonly die from such as heart attacks, cancer or Parkinson’s disease do not show up in a skeletal X-ray – a loss of market standing or failure to innovate does not show up in an accountant’s figures unless the damage has gone beyond repair”


  • Hence, financial measures have their limitations


  • And they don’t drive results – they’re the results of actions already taken – they show where an organisation has been, rather than where it’s going


  • Warren Buffett famously described them as ‘rear mirror, not windscreen’ measures


  • Using them alone would be like steering a boat by watching its wake and hoping there are no dangers ahead


  • That said, there are some financial measures which are vitally important – the financial cardinals, detailed later

Most plans go unseen or unused

The following is an extract from ‘Productivity Knowhow’

  • A good corporate plan is a punchy summary of where an organisation aims to be in five years’ time and, broadly, how it is to get there


  • Essentially, the plan should define the organisation’s ‘business model’ – how it will be better than its rivals and harder to copy – how it will make money


  • Glen Moreno, Chairman of Pearson and a director of Man Group and Fidelity, said: “A corporate plan is the reallocation of scarce capital resources towards the best opportunities for growth in earnings and returns”


  • Author, Stephen Covey, said: “Plans are the knowledge about what to do and why – others then have to provide the how to do and employee motivation for want to do


  • According to Peter Drucker, the corporate plan should provide answers to: “If we were not in this business, would we be going into it now?”


  • Jack Welch, when CEO of GE, said an organisation’s strategy should define the ultimate aim of: “How it intends to win in business:
    • It’s actually very straightforward
    • It’s an approximate course of action that you frequently revisit and redefine according to shifting market conditions
    • It’s about funding the big ‘aha’, setting a broad direction, putting the right people behind it and then executing with an unyielding emphasis on continual improvement
    • It’s resource allocation, given you cannot be everything to everybody, whatever your size”


  • Good plans are thus not long-winded glossies but short statements of broad aims and how they are to be achieved – not prescriptive in every detail – and they deliberately leave tactics to others


  • They’re the written equivalent of the inspirational briefings that General George Patton or Vice Admiral Horatio Nelson gave to their commanders before battle – their commanders didn’t need, or want, any more


  • However, many managers think their corporate plans are a waste of time and effort – an annual ritual conducted by a few senior managers whilst those who have to implement them never know much about them – and, once written, even their authors tend to ignore them


  • The current situation was best summarised when Roger Smith, Chairman of General Motors, said: “We got these great plans together – then we put them on the shelf and marched off to do what we would be doing anyway – it took us a little while to realise that wasn’t getting us anywhere”

The evolution and future of productivity

The Universe is some 15 billion years old, apparently – ‘Big Bang’ followed, some 10 billion years later, spawning Planet Earth – then, over the last 4.5 billion years, life appeared on Earth and a wide variety of species, both flora and fauna, eventually emerged

At first, resources needed for their survival – food or sunlight, say – were plentiful – hence numbers of species grew

But those same resources were limited so, as demand for them grew, competitive battles began

Eventual winners proved to be ones which:

  • Either had an edge over others for the resources available at the time – fauna had to be bigger, stronger, have sharper teeth/ beaks/ claws or be faster – flora had to grow taller, quicker or need less water
  • Or, lacking such an edge – extra speed or stamina for catching prey, say – had the wit to organise themselves into over-powering teams e.g. lions, wolves or hyenas hunting in packs 

The result was the strong became stronger, and more fecund, whilst the weak became weaker, with many species dying out

Eventually, a ‘balance of nature’ would be reached when the great majority of winner species had ‘enough‘ to survive – they didn’t need or want more – they preferred to spend their spare time either asleep or watching others

And this happy state would only be interrupted by the occasional asteroid hitting the planet or Krakatoa-like volcanoes erupting, either one sending so much dust into the atmosphere that it blotted out sunlight on which most of life depended

Evolutionary battles would then start up again

And so it was until ‘homo sapiens’ appeared only some 200,000 years ago – a mere blink in the annals of total Earth time (< 0.01%):

  • It took ‘man’ most of those 200,000 years to invent stone tools and the use of fire to feed, warm and protect himself better
  • Then, in only the last 10,000 years, his brainier colleagues invented farm tools, gunpowder, the abacus and paper for books to make his life easier – they also enabled him to win all his battles with other species and become ‘king of the planet’
  • From then on, he could and would take whatever resources he needed, and more, leaving all the other species to fight for what was left – his only serious battles ever since have been with his own species
  • Tribes formed villages, then towns, then cities – groups of them became nations – some nations went further and built empires
  • Battles between tribes for resources became wars between nations for power and glory as well – the result was, over just the last 2,000 years, man has slaughtered hundreds of millions of his fellow-men
  • However, over the same period, man’s inventiveness has also enabled his total population to grow exponentially and far exceed this slaughter rate
  • But this net expansion of numbers did not improve the standard of living for most – life for all but a very select few was a constant struggle – most people were serfs, poor and miserable – their average lifespan was short, health poor and creature comforts rare compared to today
  • Rumblings for big changes thus started to be heard

Then, only some 300 years ago, the productivity revolution started in the UK – followed by Belgium and Germany:

  • Newcomen and Watt’s steam engines pumped water out of coal mines to increase supply
  • The steam engine then powered Hargreaves, Arkwright and Cartwright’s  spinning jennies, frames and looms for the northern cotton and wool mills
  • Using local coal and iron ore, Bessemer’s furnaces also started the UK’s iron and steel industry which enabled ship and bridge building
  • Stephenson’s ‘rocket’ locomotives and Brunel’s railways and tunnels soon followed

And, ever since, man has continued to find ways to invent more and better stuff to make his life longer and healthier, easier and more enjoyable

The result is the human population on Earth has now grown to some seven billion souls – some experts say it will soon reach 10 million and keep on rising – others claim vital physical resources are at their limit so, if the population does keep on rising, our future will comprise nothing but wars for them or starvation from being without

Such gloomy forecasts are not new, however

Back in the 18th century, Thomas Malthus, an English cleric, announced that growth of human food production (at the time) was linear whereas population growth had become exponential so, if nothing changed, mass starvation was inevitable – in the 1970’s the learned ‘Club of Rome‘ reached much the same conclusion

Happily, Malthus had not allowed for the capacity of man to improve productivity – to get more and better out of existing, albeit limited, resources

Indeed, over the last 300 years:

  • Man has contrived to produce more and more food from existing finite land – and whilst needing less and less human effort to do so
  • He was also able to produce more and more volume of stuff to meet his basic personal needs, the first rungs on Maslow’s hierarchical ladder – many things which once were considered a luxury for a few became an affordable necessity for the many e.g. motor cars, colour TVs
  • And, with his basic needs sated, more stuff came to be offered at affordable prices which made human lives not only easier but also more enjoyable e.g. dishwashers, package holidays 
  • Now, under pressure from customers and competition, suppliers not only offer more volume of affordable stuff but better quality stuff too – e.g. cars and TV programmes  

Thus, in a mere 300 years, most human lives, at least in so-called developed nations, have been transformed from what they were over man’s first 199,700 years – it’s been truly astonishing progress

But can it continue?

Modern-day pessimists, the equivalent of Malthus, say ‘no’

The most famous of them is the respected Professor Robert Gordon of North Western University, USA – he claims that invention of GPTs (General Purpose Technologies like the steam engine, electricity or computers) has been exhausted and there will be no more

But maybe this is is a selfish and blinkered view:

  • Selfish because it ignores the plight of most of the current human population on Earth who have yet to enjoy most of the benefits of the productivity revolution
  • Blinkered because it ignores the many unknowns we have yet to know about, not to mention all the unknown unknowns

And maybe a global paradigm shift is also in the offing – one where we humans now find ourselves at a watershed, moving:

  • From an ‘old world’ focussed on producing more and better tangible stuff from limited physical resources at lower unit costs – one which seeks to improve our SoL (Standard of Living)
  • To a ‘new world’ focussed on developing more and better intangible stuff from unlimited knowledge resources, much of it offered for free – one which seeks to improve our QoL (Quality of Living)

At this juncture, productivity improvement efforts become even more important to both our SoL and QoL:

  • SoL because some five billion of the seven billion people on Earth still live relatively wretched lives and need help to catch up with the rest of us – and we better-off two billion will not be content to just stand and wait for them, forever wanting to improve our SoL further
  • QoL because the ‘new world‘ opening up before us requires a radical review of what our likely needs will be in future

So what might life be like some 20 years from now when the ‘new world’ could well have taken over?

A. SoL factors?

  • Wages will be unnecessary – a UBI (Universal Basic Income) experiment will be short-lived and found pointless:
    • All private sector goods and services will be produced by AI (Artificial Intelligence) and robots, and be free – cash will not be needed to buy or exchange them – everyone will have what they want – expensive luxuries to reward success will be passé – IP (Intellectual Property) protection and patenting for commercial gain will no longer be needed as latest and best ideas will be immediately shared with all
    • Public services will also be free – taxation to fund them will be unnecessary
  • ‘Work’ will thus cease – we’ll all do only what we want to do, for fun, not what we had to do, for money
  • Wealth, and inequality, will no longer be measured by physical possessions but mental capacity beyond that available from AI
  • ‘Old world’ poverty will disappear as everyone will have all the physical stuff they need
  • ‘New world’ poverty will never arise as everyone will be able to learn basic skills instantly, for free e.g. downloads to their brain to play the piano or speak Swahili

B. QoL factors?

  • Every single person will consider themselves, and be considered by others, to be of equal importance – life will no longer be ‘unfair’ for most
  • Class systems, royalty, aristocracy and unmerited power will have disappeared
  • Status, fame and Olympic gold medals will be confined to those with exceptional minds, not those topping promotion ladders, looking good or winning track races 
  • We’ll all have the choice to live for as long as we like, disease free, either in our physical form thanks to major medical breakthroughs or via digitally uploading ourselves to ‘clouds of the day’ 
  • We’ll fill our time:
    • Either being proactive – learning new skills, socialising with others, being altruistic or helping push out boundaries in the arts and sciences
    • Or reactive – watching our favourite sports teams or being entertained by others 

In other words, life on Earth, and any other place we’ve populated, will probably be very different to now – one where the volume and quality of outputs needed most will be information on ideas, systems and controls

And the most important input resource will be knowledge – K – either held ‘in heads’ or ‘in files’ – for example, K stored in the form of data, flow charts, formulae, reports/ articles, customer details and contacts or experience gained from successes and failures etc.

However, there is one big difference between ‘old world’ physical inputs and ‘new world’ mental inputs:

  • Labour, materials and capital are all limited, often unrenewable and all costly
  • On the other hand, K is unlimited, can grow rapidly and is mostly free for, if you and I each have a £1 coin and exchange them, we each still have £1 but if you and I each have a good idea and exchange them, we each have two good ideas

Hence, unlike physical resources, mental K resources have enormous potential to improve the SoL and QoLof every man and woman on the planet

As ever, however, man already wastes most of this potential

Consider the following three performance measures usually applied to physical resources but now to the K available within your team, organisation or nation:

  • KA% = K Availability = Amount available in-house/ Total needed = 60%
    • The actual volume ‘in heads’ is usually more than adequate for any team to complete its work well
    • However, the volume ‘in files’ is usually poor – people are reluctant to record their experiences – systems are not in place for others to find it
    • And whilst the volume available from outside and in the public domain is vast, and said to be doubling every year, it’s currently biassed by search engine algorithms and optimisers which determine what one sees on first pages – hence much valuable information can be hidden on later pages, or completely ignored
  • KU% = K Utilisation = Amount used/ Amount available in-house = 30%
    • Only a small % of the K available in-house is made use of by others because:
      • They don’t know it exists, or how to find it
      • They find it difficult to access
      • It’s kept secret by owners because ‘knowledge is power’
      • It’s of poor quality and often out-of-date 
    • Hence many people in many organisations either keep re-inventing the wheel or make no advances
  • KE% = K Efficiency = Improvements made/ expected = 20%?
    • Key outputs sought from good use of K are more and better ways to do things which improve the quality of lives – also known as Kleverage, the ability to obtain significant benefits from the K available
    • KE% is a measure of the value of actual gains made versus those expected
    • The higher this %, the more it feels like having a Thomas Edison, Albert Einstein or Alexander Fleming on board – people who turn K into gold

Overall, the above product measures the efficiency of your team’s use of the knowledge available to it viz:

KAUE% = 60% x 30% x 20% = 3.6% = Very Poor

Clearly, ways to make big improvements to KA%, KU% and KE% are needed here – and given the above numbers are optimistic for the current position in most teams, the sooner the better

Fortunately, major advances are already under way, including:

  • Taxonomy and Knowledge Management, two disciplines still in their infancy, which seek to improve the availability and utilisation of K
  • Humans have limited capacity to absorb and analyse Big Data – masses of data and information – however, AI is able to dig far deeper, wider and quicker into all K that exists, seeking patterns, correlations and solutions, many beyond human comprehension – AI already offers the potential to discover whole new and better ways of doing things, from winning games like chess and ‘Go’ through to curing health problems, creating music or saving the planet 
  • Apps, expert systems and computer models have already mushroomed on a wide spectrum of fronts, many offering better, even optimum, solutions for specific personal or business problems faced 

So, whilst we humans might well be approaching peak performance levels in our physical ‘old world’, there’s a long and steep climb ahead as a mental ‘new world’ opens up before us

At present, man is still in the foothills of the K mountain, taking his first tentative steps, yet K productivity has already become the biggest issue he faces

Unlike the ‘old world’ however, if we ever near the top of this mountain, we must expect another higher mountain to appear soon after – K2?

Our future is thus mental, infinite and very exciting





Excess regulations and legacy systems solve productivity puzzle?

Brian Caplen, editor of The Banker, says the challenges banks face with regulation and legacy IT systems hold lessons for the wider economy

He points out that ‘great minds have been pondering the productivity puzzle – so why, in a time of rapid technological change, is productivity stagnant in many advanced economies?’

The UK has particular problems related in part to the tendency of firms to hire cheaply from an EU migrant pool (one which may disappear post-Brexit) rather than invest in new equipment.

But there are two other reasons — less often cited and especially pertinent to banks — which might explain this so-called mystery.

  1. The first is regulation and compliance:
    • Banks can be forgiven for thinking that theirs is the only industry suffering from regulation overload given the slew of regulation since the financial crisis
    • But all industries are engaged in a massive regulatory push across areas ranging from data protection to employment law to health and safety
    • These require not only additional resources to implement but also take out management and employee time filling in spreadsheets in order to comply
    • Many of these new regulations have noble objectives but are a direct hit to productivity
  2. Then there are control systems run on legacy IT that also eat into productivity:
    • These typically involve purchasing and invoice systems that require numerous steps to complete and ask for multiple lines of information
    • All very nice for the data collectors but they use up lots of staff time
    • Banks again are in the frontline as they are often running these off legacy and cumbersome IT systems


The US administration is currently under fire for revisiting regulation and loosening it where appropriate.

In fact, a thorough cost-benefit analysis of regulation and compliance across sectors could produce better outcomes, consume fewer resources and help solve the productivity puzzle.


Deaf ears encore une fois

Barnes Wallis, the English scientist of ‘bouncing bomb’ fame, once said: “There is a natural opposition among men to anything they have not thought of themselves”

He might better have said ‘western men’ – ‘eastern men’ can be ‘all ears’

Once upon a time, just after WW2, three eminent American statisticians tried to convince US businesses of their radical new ways to improve productivity by reducing waste and improving output volumes and quality – ways which employed basic statistics at their heart
But those same US businesses chose to ignore them, preferring more obvious stuff like Work Study and O&M, then Mathematical modelling via Operations Research, then TQM for culture changes and employee engagement – and nowadays ICT systems rule the roost
Fed up with those deaf ears, the three statisticians – Doctors Edwards Deming, Philip Crosby and Joseph Juran – crossed the Pacific to Japan where they were listened to intently – the result was the Japanese economic miracle – a transformation over a decade from a reputation for widespread shoddy goods to one quite the opposite – and with exponential increases in profit margins and overall profitability
In the 80s and even 90s, the US, and West in general, could no longer ignore this huge change in their competition – they flew thousands of managers to Japan to discover their secrets – they also came back no wiser, thinking it must be something to do with culture differences and changes
Hence TQM (Total Quality Management) was born, and it took over a decade before most in the West realised it was not the answer – worse, it was an expensive failure given it produced few quantifiable and significant results yet cost a lot in time and effort
Meanwhile, productivity deaf ears continue in the West
Readily available common sense about productivity improvement is again being ignored whilst organisations believe it’s ICT systems plus digitisation of processes that will transform their financial accounts and improve their service levels
The first problem is productivity has been so downgraded in the minds of most managers that it no longer features on any boardroom agenda – some soul-searching is thus required straight-away
Then consider what’s on offer to organisations in the West if and when any of them do seek to improve their productivity – if only as a by-product of some other worthier aim:
  • Management organisations like the CBI and IoD offer no help via their websites and largely ignore the topic
  • UK business schools, to their everlasting shame, offer no courses on productivity improvement
  • And UK management consultancies that peddle good practical sense for big productivity improvements are as rare as hen’s teeth – but there are hundreds, including all the top ten, who do not – they prefer to address leading-edge thinking in more strategic or technical areas, which also command higher fees

And none dare offer their services to clients at a cost which includes a ‘payment by results’ element – as per investment advisers with their win/ win 2/ 20 charging formula i.e. 2% to cover their basic costs plus 20% of any resultant profits (and no recompense if any losses)

It’s another example of an ‘elite’  bubble, all thinking and speaking the same way and blotting out pesky outsiders with their differing views


What kills change?

Ken Blanchard, of ‘One Minute Manager’ fame, recently focused on why implementing change stumbles so much

He listed 13 pitfalls that stop major change in its tracks without attaching relative weightings to each one so you are left to decide your own:

  • Culture = The predominant attitudes, beliefs and behaviour patterns of the organisation:
    • The current culture is not fully understood at first
    • Any disconnect between actual values and those posted on the wall means the latter are ignored
    • Employees become cynical about leaders who say one thing and do another
    • One must determine how to leverage the current culture to support, enable and sustain the change
    • To change the culture requires leadership, measures and incentives
  • Commitment = Employees’ motivation to engage in the new behaviours required by the change:
    • Employees are far more likely to buy in to a change they’ve influenced than one imposed on them by others – their involvement may lengthen implementation but greatly increase the likelihood of success
    • Uncovering and addressing employees’ concerns about any change increases both trust and buy-in
    • Those ignored can derail all
    • “There’s no commitment without involvement”
    • So provide forums for people affected to express their views, listen to bosses, become involved, have doubts removed, not least by others converted
    • Do you want compliance or commitment?
  • Sponsorship = The senior person pushing for the change and with authority over resources needed:
    • He must live and breathe the change in behaviours needed to show he is serious
    • Actions speak louder than words
    • He must assemble a well-qualified change team
    • He must not announce visions such as “To be the leading XYZ company” when all employees know it’s unrealistic given the company is nowhere near that position and steadily falling further behind
  • Change team = They have responsibility for deciding and making the changes – leading people through them and delivering the outcomes wanted:
    • They must speak with one voice ALWAYS and resolve employees’ concerns
    • Members should include advocates for the change – people who have been part of successful projects, have the time, have respect of peers, are highly skilled, will speak the truth, can communicate – people from different areas, and represent diverse points of view
    • They must involve, not ignore, employees affected
  • Communication = Essential dialogue between changers and those affected on why the change is needed:
    • Mixed messages from sponsors, other managers and the change team give employees excuses not to change
    • Don’t focus on getting words out – also listen i.e. take employees’ words in
    • Use all types of media, often
  • Urgency = How quickly employees must change:
    • Employees must be convinced that the status quo is not a viable option – what is wrong with now
    • Present them with the facts, show the gaps between what is and what could be, and then ask them why the need to change
    • Spend lots of time with apparent losers
  • Vision = A clear and compelling picture of the future after the change:
    • Go beyond a slogan and present a clear picture of what the future could look like
    • Ensure (most) employees can see themselves benefiting in future
    • Don’t invent a vision off-site at some exec retreat
    • Involve the maximum number of employees in the visioning process to maximise the number who will want to be part of it
  • Plan = A detailed programme of actions to fully integrate changes into the organisation:
    • Don’t focus on the big picture and ignore the detail, the main stumbling blocks for change projects
    • Always try to include ‘early wins’ – build a momentum of enthusiasm or naysayers will prevail
    • Always include employees affected in the planning process, especially the resisters who will identify what could go wrong
    • “Those who plan the battle rarely battle the plan”
  • Budget = The allocation of limited resources:
    • Ensure the project has enough resources for the change to succeed
    • Don’t let he who holds the purse strings run the whole show
  • Training = All employees affected have all the new skills needed:
    • Pilot the changes first – to learn who needs what training
    • Employ trainers whom employees respect and will learn from
  • Incentives = Rewards for desired behaviours and results:
    • They must be meaningful/ relevant – and not necessarily monetary
    • They must be on offer to all, not just a few
    • Employees must not forget their other roles
  • Performance management = Goals and expectations:
    • Track outcomes expected of people – provide feedback and coaching
    • Ensure they have the time and capacity for the extras needed for the changes
    • Some people want change but are not willing to pay for it
    • HR should be the most important division – (not a dumping ground for failures)
  • Accountability = Delegation, follow-up and consequences:
    • Leaders must ‘walk the talk’
    • Avoid lots of action then no follow-up
    • Need clear measures of success for all, not just the leaders – which are  regularly reviewed

With so many important factors to consider, it makes one wonder about the chances of success for any big improvement initiatives, whether at organisation or national level

Productivity improvement must involve all employees

The following are extracts from an article in the Huffingtom Post by Mike Clancy, General secretary of Prospect – one must involve all employees, all the time, for effective productivity improvement

The appointment of the Bank of England’s Andy Haldane to lead the government’s work on productivity may herald the advent of some badly needed fresh thinking. In a forensic speech this summer Haldane explained what he sees as the root causes of our current malaise. There was a lot in the speech, but two themes stand out:

  • Lack of innovation
  • Lack of institutional economic infrastructure.

It is time to call time on this top-heavy economic model and its defenders. The belief that all wisdom in a company is contained within the boardroom is central to our productivity and wages crisis.

If we are serious about ending it, we need to shake up the power imbalance in companies, reverse the decline of collective bargaining and involve everyone, government, employers and trade unions in a national mission to raise productivity.


The perfect working environment?

According to an article by Michael Odell in The Times, Basecamp is a US software/ tech company that supposedly runs without the scourge of 80 hour weeks, unrealistic deadlines, weekend emails and meetings

Two American guys, Jason Fried and David Heinemeier Hansson, run Basecamp – they’re also authors of a new book called It Doesn’t have to be Crazy at Work covering their creation of a ‘calm office’ where everyone is happy and well paid, and stress doesn’t exist

The two brim over with iconoclastic views about work, including:

  • Meetings should be a last resort – pull your eight most talented people into a one-hour meeting and that’s eight hours of quality work lost
  • Sustained exhaustion is not a badge of honour, it’s a mark of stupidity
  • No-no’s re staff attendance:
    •  Are they working? – Dunno
    • Are they taking a break? – Dunno
    • Are they at lunch? – Dunno
    • Are they picking up the kids from school? – Dunno – Don’t care
  • Adopt traditional workplace titles reluctantly – there’s often a lot of bullshit around them
  • 40 hours a week is enough for anybody – workaholics who slave all hours out of loyalty to the mission are advised to “f*** the mission”
  • Staff benefits should include:
    • Pay the best rates in the US tech industry
    • Take proper holidays, not ‘fakecations’
    • While on holiday – “log out, delete the company app, go dark” and “here’s $5,000 towards your trip”
    • Only work four days a week in the summer
    • Have a paid sabbatical every three years
    • A free monthly massage at a spa
    • A free monthly fruit and veg delivery, to their homes
  • Our goal? – We have no goals:
    • No customer count goals
    • No sales goals
    • No retention goals
    • No revenue goals
    • No profitability goals (other than to be profitable)
  • People who say ‘doing nothing is not an option’ are dumb – nothing should always be on the table
  • If you’re the multi-billionaire gorilla in the room, why not pay good rates to your staff?
  • We make good money so why try to avoid taxes – why not set an example instead – it really rubs us up the wrong way when people don’t pay enough tax


Many of these views were prompted by a survey they conducted of 600 people, asking “who managed three to four hours effective work in a day?” – only 30 put their hands up

Such a result will come as no surprise to regular readers of our posts

And when, in 2016, Basecamp showed signs of booming sales and growth, they took action to slow things down, stopped hiring and tripled selling prices – it worked – they continue to exist but stopped growing

They say they don’t want to be the next Jeff Bezos and Amazon:

  • “I don’t want to meet the Canadian Prime Minister for lunch”
  • “Colonising space is not on my to-do list”


So what do they want?

“We don’t want a bigger company – and if that means leaving some money on the table, so be it”

“We love work, but we want a life too”


Robots at Work

The Financial Times reported on a study “Robots at Work,” written by Georg Graetz, a researcher at the Department of Economics, Uppsala University, and Guy Michaels, London School of Economics, which examines the impact of industrial robots on jobs, productivity and growth.

Industrial robots are programmable and are widely used for assembly, packaging, inspection and agricultural harvesting. In recent years, use of industrial robots has increased sharply, while the price of the robots has declined by about 80 per cent, taking into account increased quality.

A brief summary of their findings and conclusions follow – readers may disagree

Job opportunities and wages

“We can see that industrial robots increase employee wages and increase productivity and that the  of jobs for low-skilled employees, and also to some extent for the medium-skilled, decreases, while job opportunities for the highly skilled increase,” says Georg Graetz.

“Most likely the profits realised through the introduction of robots are divided among the company and its employees.” (an optimistic view)

The composition of the labour market is changing towards a higher proportion of highly educated employees while at the same time the study suggests that the total number of jobs is not affected by industrial robots.

Increased productivity

Industrial robots increased the annual growth in GNP in the countries surveyed by 0.37%, and labour productivity increased by 0.36% (unbelievable accuracy)

“This means that without industrial robots, growth in labour productivity would have been about 5% lower during the 14 years we have studied.”

The contribution of robots to the economy is comparable to the economic importance of the railways in the 19th century or the more recent contribution from ICT (Information and Communication Technology).

“In this context, it is interesting to note that industrial robots account for only 2% of capital, which is much less than technological driving forces for growth in the past.”

Of the surveyed countries, the number of robots increased most in Germany, Denmark and Italy.

Countries that had a more rapid increase in the number of robots also had a greater increase in labour productivity.

Continued increases in productivity likely

The study suggests that:

  • An increasing number of robots produces a reduced increase in productivity – that is, there is a limited potential for utilising robots in production. ( we disagree – now is take-off, not slow-down, time – and what of the impact on all other sectors?)
  • Robots will continue to contribute to an increase in growth and productivity.
  • Industrial robots are evolving and will be able to do more.
  • At the same time, new types of robots are coming, such as medical robots that can perform surgery or different types of robots for transport.
  • This development will contribute to continued growth and production increases.


By refreshing contrast, consider the views expressed in an article by Kweilin Ellingrud who claims to cover ‘transforming large-scale companies and workplace diversity’ viz:

  • To date, the results of integrating automation and new technology in manufacturing operations have been promising
  • Their bottom lines have been improving via higher efficiency and greater employee productivity
  • There will be more automatic real-time data feeds and data monitoring
  • For employees, the mix of their work is changing to be less repetitive and more judgement-intensive
  • In addition, new and more exciting jobs are being created, rather than merely eliminating positions
  • Workers are or will be doing less predictable physical work, data processing and information collection – and, at the other end of the spectrum, making better decisions based on data collected, more managing of others and reacting better to what customers want
  • The result is manufacturing jobs are growing at the fastest pace for two decades
  • And, over all sectors, there are now far more people than ever before employed
  • In future, there will be a lot of job transitions and retraining needed


Kweilin then quotes MGI (McKinsey Global Institute) projecting that:

  • About 15% of the global workforce, or 400 million people, will be displaced by 2030
  • Another 8-9% of employees will work in categories that do not yet exist today (unknown unknowns?)
  • So there will have to be significant reskilling of workers



Kweilin and the MGI must surely trump the dismal views of Georg and Guy

Immigration pluses and minuses

  • 40% of Fortune 500 companies were founded by first or second generation immigrants, and more than half of the nation’s billion-dollar startups have an immigrant co-founder
  • According to the National Science Foundation, only 17% of US bachelor degrees are STEM (science, technology, engineering, and maths) degrees – the percentage in China topped 40%
  • The US leads the world in awarding STEM doctoral degrees, but more than a third of those degrees are awarded to foreign students
  • Twenty years ago the US share of global venture investment was 90% – that number dropped to 81% in 2006 and to 53% in 2017
  • In 2016, China was home to six of the ten largest venture capital investments in the world


And other countries like the UK, Singapore, France, and Canada dedicate visa regulations explicitly to attract young immigrant entrepreneurs (n.b. Claude claims)

Not to mention China which, in addition to graduating far more STEM students than the US, is also devoting vast resources to its Made in China 2025 program to surpass the US in the production of key high-tech industries.

There are claims of fiscal benefits too

Consider the following extracts from an article in Moneyweek by James Lewisohn

September’s Migration Advisory Committee report on immigration to the UK from Europe claimed that European migration into the UK has caused only small impacts to our economy compared to other events such as the post-Brexit referendum devaluation of sterling, for example.

Its main conclusion appeared to be that the many immigrants to the UK from the EU (who arrived in waves, first after we opened our borders to Eastern European states in 2004, and again during the economic crisis from 2008 onwards) have made a much more fiscally positive contribution to the UK than immigrants from elsewhere. This fitted nicely with the views of most politicians and journalists and so generated a good few headlines along the lines of “Immigration myths that fuelled Brexit blown apart” (the Independent).

But is the conclusion really so simple?

It seems not.

The Committee’s chair, LSE’s Professor Alan Manning, gave evidence to the Home Affairs Select Committee

The SNP’s Stuart McDonald asked him: “Given the research, should the conclusion just be that the best thing for the UK to do is just carry on with free movement of people from the EU?”

The answer was a surprise – “No” replied Professor Manning

He and his team had, regrettably, failed to synthesise their argument:  “It’s all in there – but not brought together in one place”

What they actually meant is “lower-skilled migrants have been fiscally-negative – they make the UK a slightly lower wage, lower productivity kind of economy – any effects that they have on innovation are not positive – and, basically, if you ask what have been the benefits of this lower-skilled migration, there isn’t very much on the positive side of the ledger”

“That doesn’t come through very much in your report”, said Mr McDonald.  And indeed, it doesn’t. But it is very very important.

If Professor Manning’s conclusion is correct, and a lot of work has gone into it, then he may just have put the UK’s 14-year debate over the EU’s bedrock principle – free movement of labour – to bed

By the time of the EU referendum, the UK had seen more EU immigration, and vastly more low-skilled immigration, than any other country – it has, for example, been the primary reason why the UK’s foreign-born population, rose from 8.6% in 2003 to 12.3% even at a time when major economies such as Germany and Italy actually saw their foreign-born populations slightly contract

If, as many have argued, that has come with huge fiscal benefits for the UK, it isn’t necessarily a bad thing. But if it has not, as seems to be the case, a more restrictive policy post Brexit might be a very good idea

It could, for example, force Britain’s employers of low-wage labour to invest in greater automation, leading to greater productivity (n.b. already happening, even in advance of Brexit) and to real wage growth

That, surely, would be a result welcomed by both sides of the Brexit debate

National distribution of wealth

An interesting, sometimes complex (at least to me), article by Laurie Macfarlane for follows – it amply demonstrates that totting up any figure for national wealth is not straightforward

According to a new OECD working paper, Britain is one of the wealthiest countries in the world.

Net wealth is estimated to stand at around $500,000 per household – more than double the equivalent figure in Germany, and triple that in the Netherlands. Only Luxembourg and the USA are wealthier among OECD countries.

On one level, this isn’t too surprising – Britain has long been a wealthy country.

But in recent decades Britain’s economic performance has been poor. Decades of economic mismanagement have left the UK lagging far behind other advanced economies. British workers are now (said to be) 29% less productive than workers in France, and 35% less than in Germany.

How can this discrepancy between high levels of wealth and low levels of productivity be explained?

Wealth creation and division:

If you pick up an economics textbook today, you’ll probably encounter a narrative similar to the following:

  • Wealth is created when entrepreneurs combine the factors of production – land, labour and capital – to create something more valuable than the raw inputs.
  • Some of this surplus may be saved, increasing the stock of wealth, while the rest is reinvested in the production process to create more wealth.


How the fruits of wealth creation should be divided between capital, land and labour has also been the subject of much debate. In 1817, the economist David Ricardo described this as “the principal problem in political economy”.

Nowadays, however, this debate attracts much less attention. That’s because modern economic theory has developed an answer to this problem, called ‘marginal productivity theory’.

This theory, developed at the end of the 19th century by the American economist John Bates Clark (author of ‘The Distribution of Wealth’), states that each factor of production is rewarded in line with its contribution to production. Marginal productivity theory describes a world where, so long as there is sufficient competition and free markets, all will receive their just rewards in relation to their true contribution to society.

There is, in Milton Friedman’s famous terms, “no such thing as a free lunch”.

Seen in this light, wealth accumulation is a positive sum game – higher levels of wealth reflect superior productive capacity, and people generally get what they deserve.

There is some truth to this, but it is only a very small part of the picture. When it comes to how wealth is created and distributed, many other forces are at work.

Wealth, property and plunder:

The measure of wealth used by the OECD is ‘mean net wealth per household’. This is the value of all of the assets in a country, minus all debts. Assets can be physical, such as buildings and machinery, financial, such as shares and bonds, or intangible, such as intellectual property rights.

But something can only become an asset once it has become property – something that can be alienated, priced, bought and sold. What is considered as property has varied across different jurisdictions and time periods, and is intimately bound up with the evolution of power and class relations.

For example, in 1770 wealth in the southern United States amounted to 600% of national income – more than double the equivalent figure in the northern United States.

This stark difference in wealth can be summed up by one word: slavery:

  • For white slave owners in the South, black slaves were physical property – commodities to be owned and traded.
  • And just like any other type of asset, slaves had a market price.
  • As the below chart shows, the appalling scale of slavery meant that enslaved people were the largest source of private wealth in the southern United States in 1770.

When the United States finally abolished slavery in 1865, people who had formerly been slaves ceased to be counted as private property. As a result, slaveowners lost what had previously been their prized possessions, and overnight over half of the wealth in the southern US essentially vanished. All of a sudden, the southern states were no longer “wealthier” than their northern neighbours.

But did the southern states really become any less wealthy in any meaningful sense?

Obviously not – the amount of labour, capital and natural resources remained the same. What changed was the rights of certain individuals to exercise an exclusive claim over these resources.

But the wealth that had been generated by slave labour did not disappear, and it wasn’t only the USA that benefited from this:

  • Many of Britain’s major cities and ports were built with money that originated in the slave trade.
  • Several major banks, including Barclays and HSBC, can trace their origins to the financing of the slave trade, or the plundering of other countries’ resources.
  • Many of Britain’s great properties, which today make up a significant proportion of household wealth, were built on the back of slave wealth.
  • Even today, many millionaires (including many politicians) can trace some of their wealth to the slave trade.


The lesson here is that aggregate wealth is not simply a reflection of the process of accumulation, as theory tends to imply. It is also a reflection of the boundaries of what can and cannot be alienated, priced, bought and sold, and the power dynamics that underpin them. This is not just a historical matter.

Today some goods and services are provided by private firms on a commodified basis, whereas others are provided socially as a collective good.

This can often vary significantly between countries.

Where a service is provided by private firms (for example, healthcare in the USA), shareholder claims over profits are reflected in the firm’s value – and these claims can be bought and sold, for example on the stock market. These claims are also recorded as financial wealth in the national accounts.

However, where a service is provided socially as a collective good (such as the NHS in the UK), there are no claims over profits to be owned and traded among investors. Instead, the claims over these sectors are socialised. Profits are foregone in favour of free, universal access. Because these benefits are non-monetary and accrue to everyone, they are not reflected in any asset prices and are not recorded as “wealth” in the national accounts.

A similar effect is observed with pension provision: while private pensions (funded through capital markets) are included as a component of financial wealth in the OECD’s figures, public pensions (funded from general taxation) are excluded. As a result, a country that provides generous universal public pensions will look less wealthy than a country that rely solely on private pensions, all else being equal. The way that we measure national wealth is therefore skewed towards commodification and privatisation, and against socialisation and universal provision.

Capital gains, labour losses:

The amount of wealth does not just depend on the number of assets that are accumulated – it also depends on the value of these assets. The value of assets can go up and down over time, otherwise known as capital gains and losses. The price of an asset such as a share in a company or a physical property reflects the discounted value of the expected future returns. If the expected future return on an asset is high, then it will trade at a higher price today. If the expected future return on an asset falls for whatever reason, then its price will also fall.

Marginal productivity theory states that each factor of production will be rewarded in line with its true contribution to production. But although presented as an objective theory of distribution, marginal productivity theory has a strong normative element. It says nothing about the rules and laws that govern the ownership and use of the factors of production, which are essentially political variables:

  • For example, rules that favour capitalists and landlords over workers and tenants, such as repressive trade union legislation and weak tenants’ rights, increase returns on capital and land. All else being equal, this will translate into higher stock and property prices, which will increase measured wealth.
  • In contrast, rules that favour workers and tenants, such as minimum wage laws and rent controls, reduce returns on capital and land. This in turn will translate into lower stock and property prices, and lower paper wealth.


Importantly, in both scenarios the productive capacity of the economy is unchanged.

The fact that wealth would be higher in the former case, and lower in the latter case, is a result of an asymmetry between how the claims of capitalists and landlords are recorded, and how the claims of workers and tenants are recorded. While future returns to capital and land get capitalised into stock and property prices, future returns to labour – wages – do not get capitalised into asset prices. This is because, unlike physical and financial assets, people do not have an “asset price”. They cannot become property. As a result, it is possible for measured wealth to increase simply because the balance of power shifts in favour of capitalists and landowners, allowing them to claim a larger slice of the pie at the expense of workers and tenants.

To the early classical economists, this kind of wealth – attained by simply extracting value created by others ­­– was deemed to be unearned, and referred to it as ‘economic rent’.

For the most part, economists have tended to focus on the acts of saving and investment which drive the real production process. But on closer inspection, it is clear that economic rent is far from peripheral. Indeed, in many countries it has been the main story of changing wealth patterns.

To see why, let’s return to the OECD wealth statistics. Recall that net wealth per household in Britain is more than double what it is in Germany, even though Germany is (apparently) far more productive than the UK. This can partly be explained by comparing the power dynamics associated with each factor of production.

Let’s start with land:

Germany has among the strongest tenant protection laws in Europe, and many German cities also impose rent controls. This, along with a banking sector that favours real economy lending over property lending, means that Germany has not experienced the rampant house price inflation that the UK has. Remarkably, the house price-to-income ratio is lower in Germany today than it was in 1995, while in the UK it has nearly tripled over the same time period. The fact that houses are not lucrative financial assets, and renting is more secure and affordable, means that the majority of people choose to rent rather than own a home in Germany – and therefore do not own any property wealth.

In Britain, the story couldn’t be more different. Over the past five decades Britain has become a property owners’ paradise, as successive governments have sought to encourage people onto the property ladder. Taxes on land and property have been removed, and subsidies for homeownership introduced. The deregulation of the mortgage credit market in the 1980s meant that banks quickly became hooked on mortgage lending – unleashing a flood of new credit into the housing market. Rent controls were abolished, and the private rental market was deregulated. Today tenant protection is weaker than almost anywhere else in Europe. Meanwhile, the London property market has served as a laundromat for the world’s dirty money. As Donald Toon, head of the National Crime Agency, has described: “Prices are being artificially driven up by overseas criminals who want to sequester their assets here in the UK”.

The result has been an unprecedented house price boom. Since 1995, skyrocketing house prices have increased value of Britain’s housing stock by over £5 trillion – accounting for three quarters of all household wealth accumulated over the same period. While this has been great news for property owners, it has been disastrous for tenants. The driving force behind rising house prices has been rapidly escalating land prices, and we have known since the days of Adam Smith and David Ricardo that land is not a source of wealth, but of economic rent. The trillions of pounds of wealth amassed through the British housing market has mostly been gained at the expense of current and future generations who don’t own property, who will see more of their incomes eaten up by higher rents and larger mortgage payments.

So while German property owners have not benefited from skyrocketing house prices in the way that they have in Britain, the flipside is that German renters only spend 25% of their incomes on rent on average, while British renters spend 40%. The former is captured in the OECD’s measure of wealth, while the discounted value of the latter is not.

Now let’s look at capital:

In the UK and the US, the goal of the firm has traditionally been to maximise shareholder value. In Germany, however, firms are generally expected to have regard for a wider range of stakeholders, including workers. This has led to a different culture of corporate governance, and different power dynamics between capital and labour.

Large companies in Germany must have worker representatives on boards (referred to as ‘codetermination’), and they are also required to allow ‘works councils’ to represent workers in day-to-day disputes over pay and conditions. The evidence indicates that this system has led to higher wages, less short-termism, greater productivity, even higher levels of income equality.

The quid pro quo is that it also tends to result in lower capital returns for shareholders, as workers are able to claim more of the surplus. This in turn means that German firms tend to be valued less than their British counterparts on the stock market, which contributes to lower levels of financial wealth.

None of this means that Germany is poorer than Britain.

Instead, it just reflects the fact that German capitalists and landowners have less bargaining power than they do in the UK, while workers and tenants have more power.

While lower shareholder returns and house prices are reflected in the OECD’s measure of wealth, better pay and conditions and lower rents are not.

A Comment also published:

We all agree that slavery and theft are a bad idea, yet this logic is not extended to natural resources. As land is supplied for free by nature/God, when it becomes valuable, those excluded from its use suffer a loss of opportunity equal to its rental value. As we are all equally excluded, we should therefore be entitled to an equal share of the total rental value of all land.

As this does not currently happen, there is a net transfer of incomes from those that own little/no land by value, relative to the taxes they currently pay, to those whom the opposite is true.

Therefore the selling price of land is but a measure of economic injustice. If there was no net transfer, it’s selling price would be zero.

So not only does a typical working household have to pay much more to buy a house, they need to do so from a reduced disposable income.

Furthermore, as the incomes of some in society are higher than they should be, this leads to over consumption and misallocation of housing.

The housing crisis is just one symptom of economic injustice. It, along with many other issues, can in principle be easily solved by the application of a 100% tax on the rental value of land.

It just needs enough people to stand up and say so.



UK manufacturing to become ‘smarter’

The UK magazine Drives & Controls has just reported that a group of UK manufacturing business leaders and academics have joined forces with the government to create the Made Smarter Commission (MSC) which aims to make UK manufacturing “smarter”.
The inaugural meeting of the commission was chaired by Siemens CEO Professor Juergen Maier and Business Secretary Greg Clark and follows the publication of the Made Smarter Review almost a year ago.
The commission aims to drive forward digital developments to boost productivity in British manufacturing, to create more highly-skilled jobs, and to enable more efficient, cleaner production systems. It forms part of the government’s Industrial Strategy.

The commission consists of nine men and eight women from business, trade bodies and academic institutions, and includes top-level representatives from EEF, GE Digital, Renishaw, the CBI, ABB, Nestle, Rolls Royce, the TUC, and Jaguar Land Rover.

Priorities for its first meeting included discussing the pilot for adopting digital technology by manufacturers in North West England, and the Industrial Strategy Challenge Fund for digital manufacturing which aims to bring together UK researchers with business to tackle industrial and societal challenges.

The commission also discussed how the manufacturing industry can be transformed by new technologies such as 3D printing, as well as the need for stronger and more ambitious leadership.

According to Maier, the commission “promises to deliver [the Made Smarter Review’s] core recommendation of driving digitalisation across UK and invigorating industrial strategy. We need now, more than ever, to unite business, employees and government behind a strategy that boosts industrial productivity and improves living standards.

“We will build on our North West Pilot, and look at how we can scale our efforts up across the country,” he adds. “If we get this right, I believe we can kick-start a new industrial revolution, that puts digital tech at the centre of economic policy-making.”

EEF CEO Stephen Phipson describes the formation of the commission as “a bold step in harnessing the expertise right across our sector. We look forward to helping it play a key role in unleashing the potential of manufacturing as part of the fourth industrial revolution and a modern industrial strategy.”

The UK is one of the world’s ten largest manufacturing economies and the fourth-largest in the EU. In 2017, manufacturing GVA (gross value added) totalled £186bn and supported 2.7 million jobs (with estimates of 5 million across the whole of the manufacturing value chain). The sector still accounts for 48% of UK’s exports of goods and services.

Business secretary Clark predicts that the increased adoption of digital technologies “will bring enormous benefits, potentially generating £455bn over the next ten years ­– boosting productivity, creating thousands of new highly skilled jobs and enabling more efficient, cleaner production systems”.


  • This is the first I’ve heard of this new MSC initiative – one wonders how it will interact with others such as the PLG and PIN
  • The MSC should be a sector specific part of an overall UK Productivity Centre (UKPC)
  • Given manufacturing now comprises only some 15% of the total UK economy, where are the comparable initiatives for the other 85%?

A short history of productivity improvement

Lydia Dishman wrote an article for Fast Company outlining steps taken over time to improve productivity – it’s not comprehensive but interesting nevertheless

According to her, there’s no definitive source for the start of productivity improvement efforts but there are historical mentions of it in Wealth of Nations by Adam Smith (1776).

Smith contended that there were two kinds of labour – productive and unproductive viz:

‘There is one sort of labour which adds to the value of the subject upon which it is bestowed; there is another which has no such effect. The former, as it produces a value, may be called productive; the latter, unproductive labour. Thus the labour of a manufacturer adds, generally, to the value of the materials which he works upon, that of his own maintenance, and of his master’s profit. The labour of a menial servant, on the contrary, adds to the value of nothing . . . A man grows rich by employing a multitude of manufacturers; he grows poor by maintaining a multitude of menial servants. The labour of the latter, however, has its value, and deserves its reward as well’.

Benjamin Franklin, a contemporary of the Scottish economist, had a simple way of assessing productivity – ‘start the day asking what good shall be done, and at the end of the day evaluate based on what was accomplished’. Lofty, to be sure, but an interesting measure nevertheless.


A milestone advancing  productivity occurred in the USA during the same era when Eli Whitney invented the cotton gin in 1793. This impacted the U.S. economy, particularly in Southern states where cotton was grown and picked by slaves. Of course, slave labor was free, and abuse of slaves was rampant, yet the landowners got an additional boost to their bottom lines by implementing a machine that increased their production 25-fold.

The cotton gin wasn’t the only technological advancement to grow out of the early days of the Industrial Revolution. Other machines– from steamboats to sewing machines, light bulbs to telephones – that moved production from handmade in the home to factories sprung up across the country during the late 18th and early 19th century and the frenzy with producing more goods more quickly became something of a national pastime.

Slavery was thankfully abolished after the Civil War, but low-wage factory workers (many of whom were children) continued to toil in unsafe conditions for decades, all in the name of increasing productivity. It took years, but eventually, the organisation of labour unions put measures in place to protect workers from the excesses of the push for productivity.


Although the 20th century was rocked by two World Wars and the Great Depression, productivity was a focal point for manufacturing goods needed to support military efforts and later, to satisfy the demands of the growing middle class.

So it was ripe for the rise of the earliest efficiency expert, an industrial engineer from Philadelphia named Frederick Winslow Taylor. Nicknamed Speedy Taylor, he would get himself a consulting gig with a company, observe its workers, and calculate how they could do their jobs faster – and then charge a hefty sum for the report.

Peers Frank and Lillian Gilbreth were mining a similar productivity vein by dividing human action into 17 motions and then determining which was the most efficient and effective way to do any task.

From these somewhat ignominious beginnings (Taylor was believed to be a liar who fudged his numbers, and Frank was famous for saying postpartum bedrest was a waste of time–prompting Lillian to keep working after the birth of each of her 15 children) grew a sizeable industry of management consultants who aimed to tackle the productivity problem from every possible angle.


Among the more recognisable players is Tom Peters, whose book In Search of Excellence chronicles the productivity practices of “America’s best-run companies.”

Michael Porter wrote Competitive Advantages, also exalting the leadership of productive management practices.

And Bill Smith, an engineer at Motorola, introduced Six Sigma in 1986 as “a disciplined, data-driven approach and methodology for eliminating defects in any process – from manufacturing to transactional, and from product to service.”

According to Six Sigma, “Productivity is much more important than revenues and profits of the organisation because profits only reflect the end result, whereas productivity reflects the increased efficiency as well as effectiveness of business policies and processes. Moreover, it enables a business to find out its strengths and weaknesses. It also lets the business easily identify threats as well as opportunities that prevail in the market as a result of competition and changes in business environment.”


The thing is that in the frenzy to be more productive, we as a nation have become a little less so.

Economist Robert Gordon of Northwestern University chalks this up to the fact that we are using methods and procedures that are over a decade old. He told the Atlantic, “We had a great revolution in the 1980s and ’90s as businesses transitioned from paper, typewriters, filing cabinets to personal computers with spreadsheets, word-processing software. And then that revolution was accompanied in the 1990s by the internet, by free information through search engines, through e-commerce, and doing away with paper.” Until we start incorporating more robots and AI to take over our rote tasks, this downward trend will continue.


The other obsession with productivity is entwined with a false belief that we need to be working all the time to be our most productive selves. And that’s simply not true.

As Leila Hock, a career coach, points out: “It’s not hard work – work is work, and yes, some work requires more brain power, but most of us smart people like that and want more of it, so let’s stop calling it hard. Let’s call it productive. Effective. Valuable. Anything that speaks to nature over quantity, because that’s what we need more of.”

So maybe Ben Franklin’s to-do list had it right all along.

Work and assess what good was accomplished that day – then the most productive day will have the most good attached to it.

A famous fire that changed workers’ rights

The following are extracts from a publication by the AFL-CIO, America’s Unions

On Saturday, March 25, 1911, a fire broke out on the top floors of the Triangle Shirtwaist factory in New York

Firefighters arrived at the scene, but their ladders weren’t tall enough to reach the upper floors of the 10-story building. Trapped inside because the owners had locked the fire escape exit doors, workers jumped to their deaths. In a half an hour, the fire was over, and 146 of the 500 workers—mostly young women—were dead.

The fire alone wasn’t what made the shirtwaist makers such a focal point for worker safety. In fact, workplace deaths weren’t uncommon then. It is estimated that more than 100 workers died every day on the job around 1911.

What it did do was bring attention to the events leading up to the fire which, after the fire, inspired hundreds of activists across the state and the nation to push for fundamental reforms.

The Life of a Shirtwaist Maker:

The shirtwaist makers, as young as age 15, worked seven days a week, from 7 a.m. to 8 p.m. with a half-hour lunch break. During the busy season, the work was nearly non-stop. They were paid about $6 per week. In some cases, they were required to use their own needles, thread, irons and occasionally their own sewing machines. The factories also were unsanitary, or as a young striker explained, “unsanitary—that’s the word that is generally used, but there ought to be a worse one used.” At the Triangle factory, women had to leave the building to use the bathroom, so management began locking the steel exit doors to prevent the “interruption of work” and only the foreman had the key.

The “shirtwaist”—a woman’s blouse—was one of the country’s first fashion statements that crossed class lines. The booming ready-made clothing industry made the stylish shirtwaist affordable even for working women. Worn with an ankle-length skirt, the shirtwaist was appropriate for any occasion—from work to play—and was more comfortable and practical than fashion that preceded it, like corsets and hoops.

Clara Lemlich:

Years before the Triangle fire, garment workers actively sought to improve their working conditions that led to the deaths at Triangle.

In 1909, as factory owners pressed shirtwaist makers to work longer hours for less money, several hundred workers went on strike.

On Nov. 22, a section (Local 25) of the International Ladies’ Garment Workers’ Union (ILGWU) convened a meeting to discuss a general strike. Thousands of workers packed the hall.

Nineteen-year-old Clara Lemlich was sitting in the crowd listening to the speakers—mostly men—caution against striking. Clara was one of the founders of Local 25, whose membership numbered only a few hundred, mostly female, shirtwaist and dressmakers. A few months earlier, hired thugs had beaten her savagely for her union involvement, breaking ribs.

When the meeting’s star attraction, the American Federation of Labor President Samuel Gompers, spoke, the crowd went wild. After he finished, Clara expected a strike vote. Instead, yet another speaker went to the podium. Tired of hearing speakers for more than two hours, Clara made her way to the stage, shouting, “I want to say a few words!” – once she got to the podium, she continued, “I have no further patience for talk as I am one of those who feels and suffers from the things pictured. I move that we go on a general strike…now!”

The audience rose to their feet and cheered, then voted for a strike.

The Uprising of 20,000:

The next morning, throughout New York’s garment district, more than 15,000 shirtwaist makers walked out.

They demanded a 20% pay raise, a 52-hour workweek and extra pay for overtime.

The local union, along with the Women’s Trade Union League, held meetings at dozens of halls to discuss plans for picketing. When picketing began the following day, more than 20,000 workers from 500 factories had walked out. More than 70 of the smaller factories agreed to the union’s demands within the first 48 hours.

Meanwhile, the fiercely anti-union owners of the Triangle factory met with owners of the 20 largest factories to form a manufacturing association. Many of the strike leaders worked there, and the Triangle owners wanted to make sure other factory owners were committed to doing whatever it took—from using physical force (by hiring thugs to beat up strikers) to political pressure (which got the police on their side)—to not back down.

Soon after, police officers began arresting strikers, and judges fined them and sentenced some to labor camps. One judge, while sentencing a picketer for “incitement,” explained, “You are striking against God and Nature, whose law is that man shall earn his bread by the sweat of his brow. You are on strike against God!”

The struggle and spirit of the women strikers caught the attention of suffragists. Wealthy progressive women like Anne Morgan (daughter of J.P. Morgan) and Alva Belmont (whose first husband was William Vanderbilt) believed that all women—rich and poor—would be treated better if women had the right to vote. Alva saw the labor uprising as an opportunity to move the women strikers’ concerns into a broader feminist struggle. She arranged huge rallies, fund-raising events and even spent nights in court paying the fines for arrested strikers.

The coalition of the wealthy suffragists and shirtwaist strikers quickly gained momentum and favorable publicity. Fifteen thousand shirtwaist makers in Philadelphia went on strike, and even replacement workers at the Triangle factory joined the strike—shutting it down.

A month into the strike, most of the small and mid-sized factories settled with the strikers, who then returned to work. The large factories, which were the holdouts, knew they had lost the war of public opinion and were finally ready to negotiate. They agreed to higher pay and shorter hours but refused even to discuss a closed shop (where factories would hire only union members and treat union and nonunion workers equally in hiring and pay decisions).

At a series of mass meetings, thousands of strikers voted unanimously to reject the factory owners’ proposal. They insisted on a closed shop provision in which all employees at a worksite were members of a union. For these young women workers, the strike had become more than taking a stand for a pay raise and reduced work hours. They wanted to create a union with real power and solidarity.

While a closed shop became standard practice in later decades, at the time, their insistence seemed radical. The issue unraveled the alliance between the union and the wealthy progressive women. But by then, only a few thousand workers were still on strike, from the largest, most unyielding companies—including Triangle.

  • In February 1910, the strike finally was settledThe few remaining factories rehired the strikers, agreed to higher wages and shorter hours and recognized the union in name only, resisting a closed shop.
  • Local 25, which prior to the strike represented only a few hundred members, now had more than 20,000.
  • However, workers at Triangle went back to work without a union agreement. Management never addressed their demands, including unlocked doors in the factory and fire escapes that functioned.


The Legacy of the Shirtwaist Makers:

A week after the fire, Anne Morgan and Alva Belmont hosted a meeting at the Metropolitan Opera House to demand action on fire safety, and people of all backgrounds packed the hall.

A few days later, more than 350,000 people participated in a funeral march for the Triangle dead.

Three months later, after pressure from activists, New York’s governor signed a law creating the Factory Investigating Commission, which had unprecedented powers – they first enacted laws covering fire safety, factory inspections and sanitation and employment rules for women and children – then entirely rewrote New York State’s labor laws and helped create the nation’s most sweeping worker protections.

Target setting

Targets are needed to bring meaning to any performance measure

They enable one to quantify scope for improvement, performance gaps to be closed and urgency for change

Told your ‘bad’ cholesterol level is 8.6 and most would ask ‘so what?’ – told that good health requires the level to be below 5 and a course of statins is needed pronto

At work, all teams need to fully understand how they are being measured – and be provided with rapid feedback on actual performance levels whilst their efforts are still fresh in their memories

However, targets vary over time viz:

  • Budget targets = Short term – one year ahead:
    • Usually set by taking last year’s results and adding a small % to them so they’re not too difficult to meet
    • However, if and when they are met, most managers either don’t try to do any better (i.e. they put a brake on progress) or they don’t let it show in their results in order to get a flying start for the next year
    • In the public sector, it’s often worse – a culture of ‘use it or lose it’ is widespread – any manager who spends less than his budget in one year would expect to have it reduced in the next, so he makes sure he spends the lot
    • In addition, budgets, whatever the sector, can lock in considerable waste – if last year’s budget funded a 30% waste of resources, as many do, then adding say 5% to the total not only perpetuates current waste but adds to it
    • So always ask of any budget, ‘how wasteful?’ and ‘how stretching?’
  • Best practice (BP) targets = Medium term – two to three years ahead:
    • Internal BPs – they provide worthy targets, at least to start with – first identify best performances recently achieved by your own team, then other in-house teams working on similar work – then ask why your team does not achieve these BPs all the time – it’s much like an athlete constantly comparing his latest performances with his PB (Personal Best)
    • External BPs – by non-competitors who are leading lights in specific fields – some are happy to publicise their better ways of doing things, and the lessons learned from mistakes made
    • External BPs – by competitors – if there’s a significant performance gap between you and the best in your sector, your very existence is under threat so action is needed to close the gap – however, beware copying what others do for it may not suit your circumstances and abilities – it’s much like watching Rory McIlroy play golf but never being able to play like him – instead, it’s often better to study your own working methods and improve them in your own way
  • Goals = Long term – five years ahead:
    • Goals are aspirational targets i.e. dreams that just might become reality
    • Most global winners possessed ambitions out of all proportion to their resources and capabilities when they started out

Given these options, beware the following:

  • Many targets are set arbitrarily by back-room bureaucrats who lack a good understanding of what managers need to focus on in order to keep their customers happy and their units performing well:
    • The latest example is the decade-long campaign for normal (gold standard) rather than caesarean (systemic failure) births despite the danger and psychological damage done to mothers and their babies
    • Hospitals were assessed accordingly, even ‘congratulated for having done only natural deliveries over eight months’ according to The Times
  • Other targets are always raised every year:
    • A ‘puissance problem’ is the result
    • Managers keep ‘raising the bar’ regardless of how high it was last time so, eventually, it can become too difficult to meet
    • Teams then either give up trying and / or become demotivated, and so fail miserably, especially if there are no extra carrots on offer

Overall, target setting is thus not a quick, back-of-envelope exercise but one which must be carefully considered

For positive results, targets MUST be seen by those who have to reach them as realistic, stretching but fair – otherwise, only negative results will follow

Waste leaves productivity dead in the water

 A post by author Charles Hugh Smith hits the nail on the head about the ‘productivity puzzle’ – rising waste in all sectors, hardly mentioned by the experts, is mostly to blame

Productivity in the U.S. has been declining since the early 2000s. This trend mystifies economists, as the tremendous investments in software, robotics, networks and mobile computing would be expected to boost productivity, as these tools enable every individual who knows how to use them to produce more value.

One theory holds that the workforce has not yet learned how to use these tools, an idea that arose in the 1980s to explain the decline in productivity even as personal computers, desktop publishing, etc. entered the mainstream.

A related explanation holds that institutions and corporations are not deploying the new technologies very effectively for a variety of reasons: the cost of integrating legacy systems, insufficient training of their workforce, and hasty, ill-planned investments in mobile platforms that don’t actually yield higher productivity.

Productivity matters because producing more value with every unit of energy, every tool and every hour of labor is the foundation of higher wages, profits, taxes and general prosperity.

I have four theories about the secular decline in productivity, and all are difficult to model and back up with data, as they are inherently ambiguous and hard to quantify:

1. Mobile telephony and social media distract workers so significantly and ubiquitously that the work being produced has declined per worker/per hour of paid labour

2. Public and private institutions have become grossly inefficient and ineffective, soaking up any gains in productivity via their wasteful processes and institutionalized incompetence.

3. Our institutions have substituted signaling and compliance for productivity

4. The financial elites at the top of our neo-feudal economy have optimised protecting their skims and scams above all else; their focus is rigging the system in their favor and so productivity is of no concern to them.

Other commentators have noted the drain on productivity as workers constantly check their mobile phones and social media accounts–up to 400 times a day is average for many people.

“Addicted” is a loaded word, so let’s simply note the enormous “able-to-focus-without-interruptions” gap between those who only answer phone calls and limit social media to a few minutes per day in the evening during off-work time, and those who are distracted hundreds of times throughout the day.

Some tasks can be interrupted without much loss of productivity, but most knowledge-worker type tasks are decimated by this sort of constant distraction – even though the distracted worker will naturally claim that their productivity is unharmed.

The list of public institutions that now demand absurd wait times for minimal or even defective service keeps growing:

  •  The California Dept. of Motor Vehicles (DMV) now soaks up to eight hours of waiting to complete mundane tasks. Employees have been caught napping for hours, and customers waiting for service note the lines finally start moving in the last half-hour of the day when the employees are motivated to process the people in line so they can go home.
  • Other public-sector systems are equally Kafkaesque – building permits that once took hours to process now take months
  • In the private sector, it’s becoming increasingly difficult to fix problems created by the corporations themselves – multiple phone calls, long wait times, etc.

The core dynamic is that public institutions and corporate cartels lack any mechanisms to enforce transparency and accountability

There is no competitive pressure on the DMV or courts, and essentially zero competitive pressure on monopolies such as Facebook and Google and cartels such as the big healthcare insurers.

The only possible output of this system is extortion as a way of life:

  • We make you wait
  • We make you pay more for a poor quality service
  • We make you comply with useless regulations
  • We make you use buggy, bloated software, and so on.

Quasi-monopolies like Microsoft and Apple force tens of millions of users to re-learn new versions of software, detracting from productivity rather than enhancing it, despite their claims.

Other types of planned obsolescence are equally destructive.

With no mechanisms in place to enforce accountability and efficiency, there is no accountability or efficiency – so these monopolies and cartels can be as wasteful, inefficient and unaccountable as they want.

Compliance is a productivity killer – doctors and nurses no longer have enough time to serve patients because compliance now soaks up so much of their time.

Signaling, like compliance, is a productivity killer – the entire trillion-dollar system of higher education doesn’t measure or reward learning or the acquisition of knowledge; the diploma / credential signals that the student dutifully navigated the bureaucracy and is ready to be a corporate/government drone in another bureaucracy. That they learned next to nothing is of no concern to the system. If learning was the goal, we’d accredit the student, not the institution.

If we look at the economy as a whole, we find it is dominated by monopolies and cartels, public and private.

No wonder overall productivity is declining: there are no feedback loops or mechanisms to enforce transparency, accountability or pressures to improve efficiency and productivity gains on these neofeudal, extortionist structures.

For more, see ‘The Nearly Free University and the Emerging Economy’ ($2.99 Kindle, $15 print)

Misleading research metrics

In an article entitled ‘Capitalism is ruining science’, Meagan Day (for points out that universities existed before capitalism and pursued not profit but truth and knowledge

But no longer

The modern university has become increasingly subservient to the imperatives of capitalism i.e. competition, profit maximisation and increasing labour productivity

In academia, this manifests itself as ‘publish or perish, funding or famine’

Without public investment, universities are compelled to play by private sector rules i.e. to operate like businesses, focus on their bottom line and constantly evaluate their inputs and outputs

Hence, according to researchers Marc Edwards and Siddhartha Roy, they have introduced new performance metrics which govern almost everything researchers do, including:

  • Publication counts
  • Citations
  • Journal impact factors
  • Total research dollars
  • Total patents

These metrics now dominate decision-making in faculty hiring, promotion and tenure, and awards – so academic scientists are increasingly driven to get their research published and cited – scientific output as measured by cited work has doubled every nine years since WW2, they say

But quantity does not equate to quality:

  • Rewards for publication volumes have resulted in scientific papers becoming shorter and less comprehensive, ‘boasting poor methods and an increase in false discovery rates’
  • The growing emphasis on work citations has resulted in reference lists becoming bloated to meet career needs due to peer reviewers requesting their own work be cited as a condition of publication

Meanwhile, because increased grant funding also includes more professional opportunities, scientists spend an outsize amount of time writing grant proposals and overselling the positive results of research to catch the attention of funders – and lose opportunities for careful contemplation and deep exploration, which are vital if they are to uncover complex truths

Sadly, the combination of perverse incentives and decreased funding increases pressures which can lead to unethical behaviour – and if a critical mass of scientists become untrustworthy, a tipping point may be reached where scientists are thought to be corrupt, and public trust is lost

Peter Higgs, the British theoretical physicist who, in 1964, predicted the existence of the Higgs boson particle, said he would never have been able to make his breakthrough in the current academic environment:

  • “It’s difficult to imagine how I would ever have enough peace and quiet in the present climate to do what I did in 1964
  • Today, I wouldn’t get an academic job – it’s as simple as that – I don’t think I would be regarded as productive enough
  • I became an embarrassment to the physics department at Edinburgh University when they did research assessment exercises – they would send around a message saying ‘please give us a list of your recent publications’
  • I would send back a statement – ‘None’
  • I was kept around, despite this, solely in the hope that I would win the Nobel Prize which would be a boon to the university

The noble purpose of any science academy is to provide the resources and encouragement for people to carry out rigorous experiments that will enhance collective knowledge about the world we live in

At present, those aspirations suffer when (US) austerity-minded administrations stem the tide of federal funding and institutions change their business models to suit

US views on employee performance measures

A sample of US managers’ views was recently published on performance measures they use

In essence, they said:

  • ‘App overload’ constantly disrupts work flows – they’re meant to streamline productivity and communications but do the opposite – most employees want a single platform for phone calls, chats, email and team messaging – so get rid of legacy solutions
  • Measure the average quantity of work on a given day or week – 5 hi-quality projects are usually better than 20 hastily written ones – so emphasise the value of quality over quantity to get a better idea of pace – then help them improve their efficiency to produce more without sacrificing quality
  • Identify where workflow bottlenecks are, plus track down causes of those slowdowns – collect KPIs like the number of client issues resolved or the amount of time employees spend training to use a particular piece of software
  • Have teams set meaningful goals and then plan meaningful actions each week that will take them closer towards those goals – productivity can then be measured by comparing weekly accomplishments against planned actions for the week e.g. quantity of phone calls, press releases, blog posts, bugs fixed, products delivered, candidates interviewed
  • Establish a baseline for the employee – then set clear and concise goals with the date and exact expected result
  • Have employees log time for tasks they’ve completed, mark them as billable or not, and assign them to certain projects – then you see where time is being spent and whether it is being dedicated to customers or internal work (if they’re honest!)
  • Develop KPIs e.g. number of five star ratings or new client files opened for a given week, month or quarter
  • Set an expected goal requiring moderate effort, a reach goal (high performance) and a stretch goal (extremely high performance) – they’re a great way to motivate team members to go beyond minimum expectations
  • Measure the team by:
    • Time spent on various tasks and completion rate
    • Quality of tasks performed
    • Attendance on training programmes
    • Time spent helping others achieve their goals
  • Break projects down into concise granular tasks – assign a deadline and accountable individual to each


Quick comments:

  • The measures are all supply-side, focussed on internal team members, resources and processes when they should be outward looking, focussed on their customers’ needs and wants e.g. their satisfaction levels, end-to-end waiting times from their viewpoint, time spent by employees dealing with demand that should never have occurred in the first place and which brings in no extra revenue, only extra costs
  • There’s no reference to measures of waste of resources or time are mentioned 
  • It’s always labour productivity – and never material, capital or knowledge productivity

Forget productivity growth in future?

The following are notes jotted down whilst reading a lecture (40 x A4 pages long) given by Adair Turner, Chair of INET (Institute for New Economic Thinking) in Washington DC in 2018



  • The lecture covers the possible long term impact of rapid technological progress – i.e. work automation and AI – on the nature of and need for work
  • What if all useful, versus zero-sum, human work could be automated, say 50 to 100 years on?
  • What are the implications for the distribution of income and wealth?
    • Incomes for useful work will fall to zero, but for zero-sum work will rise
    • We cannot rely on the market to determine acceptable income levels
    • Wealth will come more and more from land, brands and beauty
  • As technological advances accelerate, the national productivity growth rate will fall faster – so productivity should stop being a national priority
  • The current combination of rapid technological innovation and low measured productivity growth is exactly what we should expect


Section 1 – When, not if, almost all economic activity is automated:

  • ICT progress means, in 50 years, we’ll be able to deploy unimaginably massive quantities of computing power, and automate almost all activities we call work and for which people receive income
  • Many jobs are repetitive/ predictable, others more complex/ thoughtful/ creative, and others a mix of the two
  • Automation reduces the time needed for the first and last categories, and so employment overall (if we continue to work 35 hour weeks)
  • Accommodation and food services are far more susceptible to automation than health and social services and education
  • ICT hardware costs keep on reducing – software originals cost a lot but marginal copies cost next to nothing
  • At some stage. combinations of hard and software will equal, and then far exceed, human intelligence
  • N.B.
    • The above merely extrapolates current known technological capabilities without any possibility given to whole new ideas/ sectors unexpectedly emerging for mopping up surplus labour and offering both new productivity improvement potential but also higher wages and jobs they want versus have to do – aka unknown unknowns – e.g. the internet, search engines and social media back in the 80s
    • Re humans being overtaken by machines, what if man implants IAs (Intelligent Assistants) to upgrade his grey cells – and keeps on upgrading by up/ downloading his brain contents for updates and extra capacity – and thus keeps well ahead of any machines alone so he’s not threatened by them?


Section 2 – Explaining the Solow paradox i.e. “Computers are everywhere but in the productivity statistics”:

  • Why is measured productivity growth slowing down?
  • Because an acceleration in technological progress, which enables dramatic productivity improvement in some sectors, can be accompanied by displaced labour having no choice but to move to low-wage sectors, resulting in a decline in total measured productivity
  • How come?
    • Proliferation of low-productivity jobs – for those displaced by increasingly automated sectors:
      • In the past, labour displaced were able to move to new sectors which also had potential for productivity improvement e.g. agriculture to manufacturing, then on to some services
      • But once those with money have all they ‘must have’ plus ‘like to have’, they don’t provide the demand for more – they have ‘enough’ of what is currently on offer 
      • However, they might like a domestic servant or two, on very low wages – plus maybe the occasional painter or singer to entertain them
      • So aggregating all incomes from all (currently known) sectors arithmetically reduces GDP and national productivity because of the rapidly growing low-wage, high employment sectors
      • Thus, rapid productivity growth in one sector combined with low productivity in others results in lower overall productivity growth
      • Total productivity growth is as much driven by the productivity growth potential of the sectors into which workers move as those where they are automated away from – it’s simple arithmetic
      • The logic is that, eventually, all jobs will be automated, so all humans will be displaced to lower and lower productivity jobs – until there’s no productivity growth at all
      • Then we’ll need to ‘find something for them all to do’, albeit at lower rates of pay
    • Rise of zero-sum activities as nations get richer:
      • Zero-sum activities are those where more and more human talent can be applied (and higher and higher incomes paid) but not produce more GDP or value to humans
      • Examples include:
        • Criminals v Police – they balance each other out – they don’t add to the total sum of goods or services for increasing human welfare
        • Cyber criminals v cyber experts defending people and firms against them
        • Legal services – if divorce lawyers improve their quality and so results, the other side does the same, so soon one is back to square one
        • Corporate and IP lawyers – they secure new ventures or protect valuable IP rights, so benefit others
        • Tax accountants for minimising tax versus HMG tax officers for maximising tax-take
        • Marketing and advertising executives, and communications consultants – who seek to convince us that product A is better than B
        • Financial traders and asset managers – most add no value versus index investing
        • Financial regulators and compliance officers
        • Corporate financiers who organise M&As which rarely enhance shareholder value
        • Political campaigners and lobbyists who seek to influence votes one way or the other
      • But more education is good for all
      • And fashion design is a creative artistic process which adds to the variety and enjoyment of life
      • However, productivity improvement leads to the creation of more and more zero-sum jobs, many of which are not counted by GDP and cancel each other out anyway – another reason national productivity is seen to fall
      • If you apply AI to zero-sum jobs, you simply increase the intensity parties on both sides work at – it’s not their efficiency that matters but their effectiveness – did the lawyer win the case, or not?
    • Growth of low-cost/ free goods and services which enhance lives:
      • After automating so much and dispelling the need to work, why do we get a proliferation of low-paid jobs and zero-sum activities but no better human welfare – or do we underestimate the benefits to human welfare?
      • We only need a few very clever people plus AI to do wondrous things – e.g. to invent super drugs so we all live to 100, or forever evenand disease free – so the rest are mostly surplus to overall requirements
      • GDP accounting conventions – the methods, estimates and assumptions used – are flawed for the future viz:
        • GDP clocks salaries of the above clever inventors plus their sales – but when their patents expire, their sales revenue drops to very little yet their products and benefits continue
        • ‘GDP deflators’ used to cover price changes are suspect – all sorts of shenanigans are possible here
      • The knowledge of how to produce something can cost a lot – but the marginal cost of actually producing it can be peanuts
      • Professor Martin Feldstein -“Government statisticians are almost bound to underestimate the scale of productivity improvement – as low growth estimates fail to reflect the innovations in everything from healthcare to internet services to video entertainment that have made life better during these years”
      • Turner questions whether all innovations make life better
      • As unit prices collapse with technical advances, so do sales revenue and apparent benefits clocked by GDP – thus, GDP undercounts technological progress
      • And, as computers get more powerful, do our kids get happier/ less stressed – so is human welfare also not measured well?
      • Thus “real productivity growth fails to account for some of the most dramatic increases in productivity”
      • Note “the apparent paradox of expanding opportunities for automation combined with mediocre and declining productivity growth”
      • “With limitless potential to automate jobs, it is almost inevitable that we will observe a slowdown in measured productivity growth”
  • N.B.
    • What if we reduce average weekly work input hours to 15 (a la J M Keynes) for no loss of GDP? – productivity would gallop ahead
    • Can we really expect no more new hi-wage/ hi-employment/ hi-productivity sectors, as Turner seems to assume – just ones which have no automatable potential?
    • Where’s the huge benefit clocked of less work hours and more leisure hours – of more doing what we ‘want to do’ , not what we ‘have to do’ to earn money to pay for stuff we ‘must have’
    • Surely, the ultimate human aim is NOT to have to work at all, and instead be able to choose whether to potter in our gardens, mow our own lawns, launder our own clothes, sail boats or play golf with chums – this would not only fill available time enjoyably but allow us to develop what talents we may have enthusiastically
    • Over time, all sectors tend to peak as all potential to improve is addressed, first in quantum leaps then increasingly via marginal gains – but new sectors are new, and offer huge potential for clever people to address productivity issues therein


Section 3 – Meaningless measures:

  • GDP measures have always been imperfect but, as we get richer, they become even more so – especially with new ICT collapsing hardware costs and enjoying zero cost software replication
  • GDP fails to reflect the pace of technical progress which enables us to deliver more with less
  • There’s a limit to how many cars or washing machines we want to buy, and as we reach those limits, labour must inevitably shift to activities which cannot be automated
  • Past measures were perhaps ‘good enough’ for policy-making to reflect technological advances and GDP/ productivity growth – and each generation feeling better off than the last one
  • But no longer, when GDP counts many activities which cannot possibly improve human welfare (e.g. social networks and always-on devices) and does not count many others that do (e.g. healthcare) – and where productivity growth is rapid in some areas but more than offset by low wage/ low productivity jobs elsewhere
  • What’s the difference between consumption (tangible?) and welfare (intangible?) for different goods and services? – GDP per capita is already suspect as a measure of human welfare
  • Maximising real GDP growth can no longer be the prime objective of economic policy if it’s losing its meaning
  • Standard GDP measures are already less meaningful and less useful – they will be worse in future



  • All work will be automated – so if people still need an adequate income, employment will be dominated by low-wage jobs, many still existing because the rich like being served by people, not robots, even though those jobs could be automated
  • GDP will be dominated by property values and various forms of rent (of property and IP i.e. stuff people compete for) as all other goods and services are produced at ever falling prices so most income people have will pay for what remains either limited or is distinctly different e.g. high fashion, pop heroes, top footballers, highly talented people – or zero-sum activities attracting the very best to outdo others re winning elections, court cases, cyber defence efforts
  • According to Thomas Piketty, almost all developed economy wealth over the last 50 years has been explained by rising property values, and almost all that explained by rising land values, which is not limitless
  • Employment will be dominated by low-wage face-to-face services
  • Inequality between the rich few and the poor many will widen further
  • Measured productivity growth will be very slow


Section 4 – Average is over – Income and wealth inequality is inevitable:

  • “Where will the new jobs come from, especially the new incomes?”
  • It’s no good just blindly saying ‘give everyone better skills’
  • In the past, new ideas led to new sectors offering stuff more and more people soon found they wanted – this led to many more new jobs, often better paid
  • Productivity improvements across the board also led to more pay and so more disposable income for more people to buy other goods and services – thus did economies grow
  • If people have to work to gain income, and if there’s no minimum wage rates, then jobs will always be created to induce demand for some new service provision – an employment equilibrium will result albeit, in future, accompanied by ever-rising inequality:
    • A relative few digital companies and their clever top guys will be the big winners
    • Losers will be the rest of us, including those who previously were doing quite well but, now outplaced, are forced to accept low-wage jobs to scratch a living
  • But even if average incomes fall, that would fail to reflect everything from healthcare to internet services to video entertainment that have made most lives much better over the last few decades
  • But Turner asks: “Have always-on mobile phones, computer games and social networks made lives better?”
  • Most people will not be able to afford to live in the best areas/ cities so will migrate to where there is plentiful and cheap land with few planning restrictions or properties left unwanted and so cheap by the rich – this will enable them to live worthwhile/ acceptable lives – so social turmoil, as in Ned Ludd’s day, is unlikely
  • Meantime, over the long term, attempts to increase the productivity growth rate of developed countries are likely to be both unnecessary and ineffective:
    • We only need a few highly talented ICT experts to keep advances going in those sectors which can be automated
    • And, as some sectors get better, others less productive attract more of the outplaced labour which more than counteracts any productivity gains made so, on balance, productivity will continue to dip
  • The most important choices facing advanced rich societies in the future will be how we spend the fruits of increasing productivity and how to distribute it:
    • Forget ‘better skills’ – education is good for all, but not essential for productivity improvement – we only need a few very good IT bods for AI and super intelligence
    • Pay everyone a UBI (Universal Basic Income) to ensure they receive at least a basic minimum for a reasonable standard of living (varying only by location, property and land prices) plus enjoy high-quality public services e.g. health, education and public transport plus shared public spaces like countryside and beaches – however, UBI ignores the psychological benefit that work delivers a sense of status and self-worth
    • More people are likely to find satisfaction in becoming skilled gardeners, artists, cooks, brewers, organic farmers and beekeepers rather than software developers
    • Nations facing an ageing population problem need worry no longer – too few workers to support too many oldies will no longer be a threat
  • RCS:
    • Turner repeats many of his pearls, and not necessarily for emphasis
    • Why not push for more and more leisure and far fewer hours at work – all big benefits to ‘human welfare’?
    • With more education for all, people will be better able to decide what they want to do with their lives
    • Technical advances already mean many more low-wage people get to enjoy acceptable (to them) living standards, including cheap fun and education 


Section 5 – The old ladder destroyed – Rapid economic catch-up is no longer possible:

  • Radical automation potential combined with rapid population growth could create almost insurmountable barriers to economic catch-up:
    • Note the USA and W Europe gap with the RoW (Rest of World) over period 1800 to 1950
    • A few other nations have achieved catch-up since, partially via their manufacturer exporters mopping up surplus agricultural workers, and higher incomes from manufacturing increasing savings and investments in plant and machinery – e.g. Japan, Korea, Taiwan
    • But much of manufacturing is, or soon will be, automatable at attractive cost so, if wages keep on rising there, automation will soon take over
    • India, with a growth rate of 5% p.a., needs to create 10 to 12 million new jobs every year just to keep unemployment and underemployment stable – but they’re failing as companies start to apply state-of-the-art technology despite labour available at very low cost
    • Ditto China, with a growth rate of 7% p.a.
    • Africa has a far bigger problem – average growth rate 4.6% p.a. versus 2.7% population growth rate
  • Soon the rich world will not need cheap emerging economy labour to provide low-priced footwear, apparel and other goods – automation at home will do the jobs
  • So the key is to boost sectors unlikely to be prone to automation e.g. tourism and construction
  • And boost the quality of education for all to equalise opportunity


Section 6 – Implications for economic theory:

  • We now live in a world where:
    • Productivity improvement can be delivered with little capital investment
    • Most wealth resides in locationally desirable property/ land, IP rights and brands
    • Most wealth creation derives either from changes in the relative price of already existing assets, or from the creation of IP, brand and externality (?) effects
    • The problems of production will become unimportant
  • Hence, productivity improvement will no longer be about how to get more from less but how to resolve, in a fair and sustainable way, disputes about the distribution of those goods, services and assets, both created and natural, which automation does not make available at ever falling and close to zero prices
  • It will become a balancing act, between individual freedom versus fairness
  • According to Peter Orszag, in an article for Bloomberg Opinion commenting on Turner’s lecture:
    • The impact of new technology on total productivity growth depends crucially on who accrues the income from the new inventions – what additional consumption they choose to enjoy with that income – and the nature of productivity advances in the sectors that workers are shifted into as a result
    • And any new sectors are not expected to lend themselves to automation or significant productivity improvement
    • And, as the rich get richer, they may well choose services offered only by low-wage sectors e.g. personal care aides, cooks and servers, registered nurses and home health aides – i.e. person-to-person interactions that are, for now, difficult to automate
  • So no more big productivity gains on the horizon?
  • None foreseeable, for now




Effective change management

The following is a punchy article by journalist Simon Caulkin describing the best way, by far, to improve customer service whilst minimising costs – it’s counter-intuitive, and ignored by big consultancies – however, it works well, and puts their approaches to shame

Google ‘change management’ and you get half a billion hits. ‘Change management models’ gets 17m. Yet perhaps never in management has so much been sought by so many to such little effect. Almost all of the models referenced have one unwanted trait in common. They don’t work.

70% of all large-scale change initiatives fail, according to the Harvard Business Review. When they involve IT, the failure rate, in whole or in part, is 90%.


Well, not coincidentally, there’s something else conventional models have in common: a starting assumption that when you launch change, or more fashionably ‘transformation’, you know where you’re going. Of course you do: what leader would admit she didn’t? So change is a matter of planning how to get to the appointed destination, with a schedule of carefully orchestrated quick wins, deliverables, milestones and communication campaigns to keep programme and people on track.

But there’s a snag.

In any body composed of interdependent moving parts, change happens not mechanically but through a series of interactions and feedback loops between the parts, which ripple out and alter the whole. The behaviour of the ensemble can’t be predicted in advance from that of the components, and vice versa. In other words, change is emergent – a result, not a cause.

This changes everything.

The result is not just a different ‘change model’ – it’s a different way of thinking:

  • Conventional change models come straight out of the command-and-control (aka central planning) playbook, decreed from above and cascaded down through the organisation.
  • In a systems view, change is better seen as discovery, proceeding not by way of an abstract plan, plotted to an arbitrarily fixed destination, but by open-ended investigation and iterative experiment leading to deliver ever-improving outcomes.

In this latter version of the process, change starts by establishing not where you’re going but where you are now. Like it or not, you start from here, facing forward. And the only way to start the process of discovery is to go and see for yourself.

Professor John Seddon, founder of Vanguard Consultants, recounts how a brilliant and mercurial mentor noticed on one assignment how little front-line service agents could actually do for clients calling in with a problem – ‘what if we equipped them to deal with the calls that they are likely to get?’

It was a pivotal moment.

To work out how to do that:

  • The first step was to listen to customers’ calls live – a revelation in itself, since the most striking thing was how many were complaints about something not done, or not done properly, on the first contact (since known as failure demand).
  • Next, they had to turn that thought round and ask themselves what should have been done that would have made the follow-up call unnecessary – that is, what was the purpose of the service, from the customer’s point of view?
  • Finally, they needed to know what kind of customer needs were predictable and which only arose from time to time. Only then could they proceed to train operators in a way that would reliably improve performance.

‘Go and see for yourself’ turned out to be critical in other ways:

  • The root problem to be addressed, and hence the nature of the subsequent change, was never the one managers thought it was:
    • The functional measures they were using – number of calls per shift, speed of response of the different functions – told them nothing about the experience of the customers, who naturally took an end-to-end view. As a result they were always surprised, and often dismayed, to discover that service that was excellent according to their (or regulators’) measures got a vigorous thumbs down from recipients.
    • Conversely, the eventual benefits often went far beyond the incremental gains required by the plan e.g. huge increases in capacity by cutting unnecessary work and failure demand and steadily shrinking costs as customer service improved.
  • The truth about the operational reality was so unpalatable to managers brought up on conventional methods, and who had so much invested in them, that unless they saw it with their own eyes they refused to believe it.
  • It’s not that a systems view of work or organisation is harder to grasp than a conventional one; it’s that the two are so different that there’s no intellectual route map between them. They are parallel tracks with no connection. In other words, it’s impossible to convince a conventional manager to cross from one track to the other by rational explanation. They have to see it with their own eyes – the corollary being, once they have ‘got’ it, they have crossed a Rubicon: there is no going back.

There’s a rigorous discipline to ‘study’, but broadly speaking once customers have put them right about where they are, managers and front-line workers can jointly start to figure out what to do to meet the purpose of the service without recipients having to make follow-up calls to remind them. It’s only when the hypothesis has been tested in action and adjusted accordingly that it is possible to envisage what the redesigned process will actually look like.

This empirical approach to change brings two enormous benefits, one negative, the other positive:

  • It prevents managers wasting large amounts of money and effort on top-down change programmes that are doomed to fail.
  • It can eventually lead to the kind of gains that no one would have dared to put in a plan.

Both of these are well illustrated by the case of IT.

IT is usually presented as the ‘driver’ or ‘enabler’ of large-scale change, as in the ill-fated Universal Credit project in the public sector, and countless ‘digital transformations’ in the private sector. The assumption is that the IT system comes first and operations will automatically be more efficient if digitised. But this is diametrically the wrong way round. When managers start by learning how their system works, they usually find, again to their surprise, that a giant, all-singing, all-dancing IT system not only does nothing to solve the real problems – by locking in the old system, it is a constraint rather than an enabler.

This is not to denigrate or downplay the importance of technology – provided it is kept in its proper place, which is last, and always as an aid to rather than replacement for human intelligence.

As for any change project, the order is:

  • First, study the system – get knowledge
  • Second, improve the service to the customer – redesign
  • Third, ‘pull’ the IT that you need – so you use it all and don’t buy bells and whistles you don’t need.

This goes for heavily IT-dependent services such as banking and insurance just as much as for customer helplines or emergency services.

If that sounds unlikely, consider the stories put forward by senior financial executives at a recent event put on by Vanguard where bank CIOs said:

  • Changing rules of the game meant an urgent need to experiment with the customer journey without having a full plan, representing ‘a profoundly new world, mindset and model for banking,’
  • ‘If you think of the solution as a technology thing or opportunity, you’ll solve the wrong thing or make matters worse.’
  • ‘We forgot that banking is not about current accounts, it’s about accessing money and buying a home,’
  • ‘It was a cost-related, industrialised approach. We had a lot to unlearn.’
  • ‘Now, no one can touch anything unless they can show they understand how the system works and have experienced how the service is consumed.’
  • ‘Don’t digitise what you don’t need to. Our problems weren’t caused by technology, so how can it solve them?’

Another leader in banking confessed that having joined the bandwagon to ‘go digital’ and invested heavily in new digital services, managers discovered through studying that it led to increases in failure demand into its service centres. Calling a halt to the costly dysfunction, they set about doing what should have been the starting-place – studying customer demand, studying how well the bank serviced those demands (not very well), improving the way the demands were serviced and, finally, on the basis of thorough knowledge, ‘pulling’ IT into the designs.

‘Innovation isn’t about technology. It’s about solving customer problems, and using tech to do it where necessary,’ said a South African insurance CEO who, after much heart-searching, had cancelled a big IT systems investment because she could see it was simply a modernisation of the old architecture that would do nothing to attract new customers. The breakthrough moment was a ‘what if’ question that emerged from studying the system: ‘What if we thought of our business not as picking up the pieces when things get broken but stopping bad things happening in the first place?’ Out of that came a clever initiative to use advanced technology to monitor customers’ heating boilers, triggering instant alert and repair in case of failure. ‘Insurance at the touch of a button! But it’s critical that the IT architecture supports the right measures.’

Change of this kind, as all the participants emphasised, isn’t a one-off event but a never-ending journey

What emerges is a service design that absorbs the variety of customer demand using new and fundamentally different controls which facilitate a constant focus on perfection.

Effective change starts with ‘study’, not plan.

The consequence of gaining knowledge is that change is guaranteed to work, and deliver results far beyond what might have been considered possible in a plan.

NB We have no connection with Vanguard Consultants but have always applauded their approach

Invest more to raise productivity

An article by John Mills, Chairman of the eponymous JM Ltd, author of economics books and major Labour party donor, claims that UK productivity is ‘so low’ partly because we spend a far lower proportion of our national income (17%) on capital investment (aka capex) than the 26% world average – and woefully less than China’s 45%

Not only that, what we do invest is not on things that increase our output per hour, and so our GDP growth rate:

  • Most public capex is on roads, rail, schools, hospitals, public buildings and housing
  • Most private sector capex is on office blocks, shopping malls, new restaurants and housing

Investment that does produce productivity growth is in technology, mechanisation and power – and most of this is made by the private sector, much in light industry which in the UK is mostly unprofitable

Hence the UK suffers from a lack of highly productive investments – hence, the ‘productivity puzzle’ will continue to persist

However, a surprise is in store!

A separate article by Joseph Sternberg, a political economist, in the WSJ (Wall Street Journal) claims that Germany also invests much too little

Apparently, throughout the 1990s and much of the 2000s, Germany was known as the sick man of Europe, weighed down by the costs of reunification, suffocated by high taxes and labour relations, and battered by the competitive pressures of globalisation

Today, Germany’s economy appears to be in a rude state of health – GDP growing at 2.5% p.a., a trade surplus of 8% of GDP, unemployment at a low of 3.7%

But these happy numbers mask what is set to become a debilitating drag on their economy – Germany is in the grip of a productivity crisis – stagnation will return as entitlement burdens become crushing

Germany’s reputation for efficiency is even misleading – the most productive industries are exporting manufacturers, and the most productive companies are large ones – but the great majority of companies are smaller service firms, whose productivity increasingly lags

Hence, the OECD (Organisation for Economic and Cultural Development) has announced a widening gap between their most productive companies and the rest:

  • The best are not pulling others up by spreading their new technologies and methods
  • Companies are not investing at home, preferring to increase their savings instead – the IMF (International Monetary Fund) claims that capex plays a smaller role in Germany than in any other major economy
  • Most of Germany’s middling productivity gains come from companies figuring out how to do more with existing resources
  • If they boosted their capex to the level of say Belgium (no less!), they would lead the world in productivity growth

So how could they do this?

Suggestions offered include:

  • Encourage start-ups, since entrepreneurial firms typically take the lead in developing and diffusing innovations
  • Encourage companies, especially smaller ones, not to store up so much cash to fund future R&D by providing alternative, more adventurous sources of finance

Clearly, these are ‘interesting times’ for Germany and the EU:

  • The economies of EU Med members have all been described as basket-cases
  • Most of the new Eastern Europe members are piggy-backing the richer members
  • Brexit is about to happen which will significantly reduce EU central funds available – perhaps more for bail-outs than productive investments
  • France’s economy and politics are delicately poised
  • And now we find the strong man of them all, Germany, is not as strong as we thought


  • The UK should include productivity and payback criteria when making all its big capex decisions
  • Remember Jeremy Grantham’s wise words about the ‘Catch-22’ for any firm trying to expand:
    • The more it wants to grow, the more it will need to invest in both people and capex (plant and machinery) to meet expected rising demand
    • That money must come from cutting dividends or increasing its capital base, say by issuing more shares
    • Both are bad news for shareholders, the former reduces their returns, the latter dilutes their stake
  • The EU, in its present form, has not got long to live




The skills delusion

Adair Turner, Chairman of INET (Institute for New Economic Thinking) and one-time Chairman of the UK’s FSA (Financial Services Authority) wrote a weighty article a year or so ago on the need for more investment in our human stock

We cannot better his choice of words so, below, reproduce much of his article verbatim

It should be read whilst keeping in mind that Prime Minister Tony Blair once targeted 50% of school leavers becoming graduates (it didn’t matter what in) – and, more recently, some experts have been encouraging kids to study STEM subjects (Science, Technology, Engineering or Mathematics) to benefit both themselves and the national economy – however, others have confused the kids by saying they should choose humanities/ liberal arts instead ‘to hone their skills for expression, creativity and thinking’

Turner offers a different slant on such matters


Everybody agrees that better education and improved skills for as many people as possible is crucial to increasing productivity and living standards and to tackling rising inequality – but what if everybody is wrong?

Most economists are certain that human capital is as important to productivity growth as physical capital – and to some degree, that’s obviously true – modern economies would not be possible without widespread literacy and numeracy

But one striking feature of the modern economy is how few skilled people are needed to drive crucial areas of economic activity:

  • Facebook has a market value of $374 billion but only 14,500 employees
  • Microsoft, with a market value of $400 billion, employs just 114,000
  • GSK (GlaxoSmithKline), valued at over $100 billion, has a headcount of just 96,000

The workforces of these three companies are but a drop in the ocean of the global labour market and yet they deliver consumer services enjoyed by billions of people, create the software that supports economy-wide productivity improvements, or develop drugs that can deliver enormous health benefits to hundreds of millions of people

This disconnect between employment and value added reflects the role of ICT (Information and Communications Technology) – hardware power keeps on improving dramatically – and software, once created, can be copied at almost zero cost making low-cost automation of ever more economic activities possible and requiring high skills from only a tiny minority of the workforce

Despite this trend, more people than ever seek higher education levels, believing higher skills bring higher pay – but many higher-paid jobs may play no role in driving productivity improvement:

  • If more people become more highly skilled lawyers, legal cases may be fought more effectively and expensively on both sides, but with no net increase in human welfare – they’re zero-sum jobs
  • The same applies to much financial trading, or developing new fashions and brands

So more people receiving higher education does not necessarily mean their higher skills will drive productivity growth

Likewise, at the lower end of the income scale, it is not clear that better skills will offset rising inequality – new jobs can always be created as we automate away many existing jobs, but the jobs often pay less

Consider projections by the US BLS (Bureau of Labor Statistics) for job creation over the next 10 years:

  • Of the top ten occupational categories that account for 29% of all forecast job creation, only two – registered nurses and operational managers – pay more, on average, than US median earnings – while most of the other eight pay far less
  • Employment is growing fastest in face-to-face services such as personal care – these jobs are more difficult to automate than manufacturing or information services – but they require only limited formal skills or on-the-job training
  • Job categories that require specialist ICT skills do not even make the top ten
  • Overall, the BLS foresees 458,000 more personal-care aides and 348,000 home-health aides, but only 135,000 more software and application developers

But wouldn’t better skills enable people currently in rapidly growing but low-pay job categories to get higher paid jobs?

In many cases, the answer may be no

However many people are able to code, only a very small number will ever be employed for their coding skills – and even if someone currently in a low-skill job is equipped to perform a high-skilled one at least adequately, that job may still go to an employee with yet higher skills, and the pay differential may still be great – in many jobs, relative skill ranking may matter more than absolute capability

So “better education and more skills for all” may be less important to productivity growth and a less powerful tool to offset inequality than conventional wisdom supposes

However, Turner concludes that, in this new world, education and skills are more important  than ever – not because they raise everyone’s price in the labour market, but because they equip us to live more satisfying lives, enjoying the arts, science and each other more, for example


AI will automate tasks, not skills

Michael Hicks, Professor of Economics at Ball State University, USA, claims productivity growth, whether through automation, plant design or better-skilled workers, doesn’t kill jobs – it eliminates tasks:

  • First, hard, dirty and dangerous ones – think agriculture and steel-making where output continues to grow in volume but now uses a small fraction of the labour once employed
  • Then routine non-cognitive tasks e.g. assembly line work or truck driving, brick-laying, retrieval or processing of information, moving or tracking the movement of goods
  • Then routine cognitive, like medical diagnostics, detecting cancer


To date, not all tasks have been eliminated in existing sectors but the share of work done by manual labour has been slashed profoundly:

  • Few now carry water, split wood for the stove or hand-loom our shirts – or spend hours balancing the books at the end of each day
  • It is also claimed that US labour’s share of output overall has been falling over the last 50 years – from around 66% to 58% – and AI/ robotics will simply accelerate this trend


Technological progress is also relentless in all sectors:

  • It displaces workers in existing sectors by displacing many of the tasks they do – for example, over the last 30 years, the production of US steel has risen by about 10% while employment there has fallen by about 60%
  • It first invaded goods sectors, then basic service sectors, and is now encroaching other service sectors positioned at the professional or more personal end of the services’ spectrum


In the process, and against expectations, overall employment has not fallen

New and more interesting jobs have been created in existing sectors, jobs which involve working with the new technology – at the same time, new sectors have kept on appearing/ growing to meet human needs which could not be met beforehand

Over time, therefore, we humans have always found other, usually better and more interesting, jobs to do

We all possess skills that are hard to automate i.e. non-routine tasks, showing empathy, integrating and analysing quantitative and qualitative data at the same time, learning new, non-routine tasks

The problem is few training establishments – schools, colleges, universities and business schools – offer anything much to improve these skills, just as they (currently) ignore the small matter of ‘productivity improvement’


Run hospitals like Tesco stores

David Dalton is CEO of Salford Royal NHS foundation trust, the first to be rated as ‘outstanding’ by the CQC (Care Quality Commission) on two consecutive occasions, so his words carry considerable weight

He has just posted an article in the Thunderer column of The Times which is a cause for alarm to many


  • Because it drips with practical common sense ways to run the NHS more efficiently and more effectively – to end mediocrity and waste
  • Because it offers to do much more with tax-payers’ money – and to improve customer service for patients everywhere, regardless of post-code
  • Because his ideas are clearly not being listened to by those sat in NHS HQ – hence his decision to shout them loud using the Thunderer channel

Essentially, he says:

  • There are too many separate hospital trusts – England alone has 135 separate acute trusts, each with their own separate boards determining their own ways of doing things – so standards in care vary dramatically between them
  • A ‘new model’ is needed to lift and shift best practice from one trust to another – better run trusts should take over and run trusts in trouble
  • In 2016, Salford Royal moved this way and formed the Northern Care Alliance (NCA) NHS Group (aka an NHS chain) to run four other local trusts, caring for more than a million people:
    • The group has a standard operating model that ensures each hospital focuses on the right priorities using methods proven to work
    • The other four trusts were rated ‘inadequate’ when Salford took them over – now 70% of their services are already rated ‘good’ or ‘outstanding’
  • Too much variation in health services is tolerated currently
  • And huge sums are spent on management consultants and ‘turnaround directors’ in failing trusts
  • Instead, trusts in difficulties should be encouraged (why not told?) to join a chain so they can learn and implement best practice

Dalton compares such chains with Tesco and Sainsbury’s who employ a standard model per supermarket to drive up quality and increase efficiency at the same time as giving people the local shops they want

In Salford, community services, social care and even some GP practices have all become part of a single organisation – and they’ve demonstrated it works well for all interested parties

So how is it ministers and NHS leaders keep banging on about the need for integrated care, and the need for all in the NHS to be aware of and implement best practice, yet leaders on the front line feel the need to voice ways of doing this in the national media?

And given dissemination of public sector best practice information is not a commercial secret, as in the private sector, where are the national initiatives to spread them into all corners of the NHS – and what savings are being targetted?

At present, all we hear about is NHS HQ executives asking HMG for billions more of tax-payers’ money, otherwise they will never meet ever-increasing demand (ano Project Fear) – yet practical solutions, requiring no extra money, may be staring them in the face (if they read The Times)



Protectionism ensures slower growth

Excerpts from an article in Forbes by Jeffrey Dorfman, a professor of economics at the University of Georgia. USA , follow

Dorfman claims that tariffs help uncompetitive industries because:

  • They put a penalty on imports in the form of a tax
  • Domestic producers that would otherwise lose market share to imports are able to produce more and find domestic markets for those goods
  • This maintains jobs in the protected industry
  • And, by keeping factories open, it also means more capital stays in the industry benefiting from the tariffs


These outcomes are pretty much the point of the tariffs, but they impose both obvious and hidden costs on the economy

The obvious costs are twofold:

  • First, consumers of the products sold by the protected industry must pay a higher price thanks to the tariffs
  • Second, jobs and profits are lost in the rest of the economy because the higher prices induced by the tariffs leave less money to be spent on everything else


These costs are much discussed, particularly during any ramp up in trade disputes and tariff levying

What is mostly overlooked is the hidden cost of tariffs – ergo, slower economic growth

At any given time, the US economy has a certain amount of capital to invest in productive activities:

  • Tariffs protect uncompetitive businesses from shrinking or going bankrupt
  • And because these protected industries are larger with the tariffs than without them, more capital is trapped in those low-growth or shrinking sectors of the economy benefiting from tariffs
  • That lowers the average return on capital


Without tariffs, those industries would shrink, and capital be reallocated to faster-growing parts of the economy – this reallocation of capital would boost aggregate output thanks to the faster growth and mean there would be more future capital to invest in other productive uses – and economies with more capital have higher economic growth and higher wages thanks to the productivity derived from that capital

Thus, removal of tariffs would boost economic growth and create a feedback loop that would keep economic growth accelerating


Tariffs thus cost an economy jobs because the costs imposed on the rest of the economy outweigh the gains awarded to the protected industries


  • Protectionism doesn’t just mean current economic losses from both the taxes and the disruption of rearranging global logistic networks
  • It also means slower growth for as long as certain industries are protected
  • This effect compounds over time, growing for every year protectionism continues and protected industries remain larger than optimal
  • One thing markets are good at is allocating capital – sure, they make mistakes and those investors lose money – and markets do a better job than politicians when choosing which industries to support from foreign competition
  • Hence, overall, more tariffs mean an inferior capital allocation, a lower average return on capital, and thus slower economic growth

NZ shows way for public sector productivity

The New Zealand Productivity Commission was asked by its Government to provide guidance and recommendations on measuring and improving productivity in public services, especially education, health, justice and social welfare which play an important role in promoting individual and community well-being

The Commission interviewed multiple current and former senior state sector leaders, carried out case studies to demonstrate how to measure productivity in public services, and commissioned research to better understand how
innovation, the engine of productivity improvement, occurs and spreads in
public services

The Commission produced two final reports:

The following is their summary of their findings

Higher state sector productivity is critical to delivering more and better public services now and into the future, yet many government agencies lack the culture, capability and encouragement to make these gains

“Faced with demand for more services, the public sector often relies on hiring more people – that strategy is not sustainable” says Commission Chair Murray Sherwin

“Getting the best value out of existing resources requires good information, measurement of performance, openness to new ways of working, and an environment committed to making improvements”

“The Commission saw examples of good and innovative practices within the state sector. Examples include primary health care models which make better use of nursing staff skills to meet patient needs better and faster, and use of data to test which employment training programmes actually make a difference to people’s lives”

“But there are too many barriers to these sorts of practices emerging and spreading:

  • Few government agencies measure the productivity of their services
  • Some lack the capability or inclination to do so
  • Many are risk-averse and through prescriptive and inflexible commissioning arrangements, make it difficult for contracted service providers to innovate
  • The Government’s budget system tends to reinforce ‘business-as-usual’ activities instead of new and innovative approaches”

“Making progress on public sector productivity will require action by ministers, central agencies such as the Treasury, and departmental leaders”

In particular, Ministers need to start asking questions about productivity performance and setting clearer expectations for improvement – they should:

  • Establish and support a network to help build expertise in measuring productivity
  • Reshape the annual budget system to devote more money over time to high-quality initiatives that have a high probability of making a big difference to well-being, and less money to ‘business as usual’ proposals
  • Report annually on performance measures for key public services
  • Review existing public service funding models and where feasible, move to approaches that pay for results or outcomes, not inputs
  • Renew its processes for assessing the performance of state organisations, to test how well their cultures, values and practices support innovation

“This inquiry has demonstrated, through case studies and other investigations, that measuring and tracking productivity in public services is quite feasible”

Higher state productivity matters for better, sustainable public services – it allows a
community to have more or better services or lower taxes – it also contributes to
higher national productivity and, through that, to higher incomes and a larger tax base – and, as New Zealand’s people age, there will be more people
needing assistance and a smaller share of the population working and available to
provide services or pay taxes

However, there are seven major barriers to higher state sector productivity and available evidence, while limited, suggests recent state sector productivity growth has been poor:

Barrier 1 – Not enough demand for measures
It is difficult to understand and improve something that has not been measured yet
measurement of public service productivity is relatively uncommon – some agencies
do not ask the right questions or do not make good use of available information,
and politicians typically do not ask for productivity information

Barrier 2 – Hostility to measurement
Some who work in the state sector are hostile to the concept of
‘productivity’ or ‘efficiency’ in public services and resist its measurement – they argue that such measurement would be a distraction from their core business, or
have perverse impacts

Barrier 3 – Closed, risk-averse cultures in government agencies
Achieving productivity improvements in public services often means doing things
differently, such as using technology better – yet many government agencies are risk-averse, closed to ideas from outside and poor at managing change

Barrier 4 – Poor policy and commissioning practice
Effective commissioning ensures services are designed to best meet the needs of users but government agencies often take very conservative approaches to commissioning services, leading to ineffective delivery and waste

Barrier 5 – Restrictive rules and funding models
Innovation and productivity often depend on changing the mix of people,
technologies, and other resources used to deliver services – however, service units face rules or policies that limit their ability to make these changes

Barrier 6 – Few budgetary rewards for productivity
Annual budgets provide relatively little encouragement for productivity gains – the majority of existing spending is not regularly reviewed and a large share of new funding allocated goes towards ‘business as usual’ activities rather than new
and better approaches

Barrier 7 – Patchy monitoring, evaluation and data use
Finally, government agencies often make poor or little use of available data and
information which means they may not fully meet the needs of users or
officials may not know which services are ineffective, and need improvement

So the Commission recommend the following action to lift productivity performance
across the state sector

Action 1 – Set clearer expectations for productivity gains
Ministers can play an important role in lifting performance by setting clear
expectations for public services and demanding more information about productivity

Action 2 – Build the capability to measure, and measure more
Build up measurement capability within government agencies as this is currently weak – establish and support a network of capable officials to share experience and build expertise in state sector productivity measurement

Action 3 – Report on core public service efficiency
Much public sector information is not very useful for measuring changes in productivity – regular collection and publication of information on expenditure on key public services (e.g. annual per-client or unit costs for schooling, court trials etc.) would provide transparency needed and strengthen incentives on agencies and providers to seek ongoing improvements

Action 4 – Use performance measures wisely
There is a place for well-designed quantitative productivity measures in public sector performance frameworks, as they help provide a more balanced picture of performance

Action 5 – Raise the bar on new spending in the budget
The Commission recommends a set of reforms to increase the rewards
for productivity and service improvements:

  • Set aside a share of each year’s allocation of new funding for initiatives that have a high probability of making a significant impact on social well-being, and gradually increase this share over time. To qualify, these initiatives would need to have robust business cases, strong supporting evidence and clear evaluation plans
  • Tighten the link between past performance and future allocations from the budget. The annual budget round is supposed to test how well agencies have used their existing resources, but has lacked consequences for poor past performance. Agencies should only be able to access the ‘high impact initiative’ share of the budget allocation if they could credibly demonstrate they had made productivity gains from their baselines
  • Retain and strengthen a separate avenue for organisations outside the public service to make budget bids. Non-government organisations and the private sector are important sources of innovative ideas and processes, but can face hostile or unreceptive public agencies. Allowing these organisations to make proposals directly, without the approval of departments, removes roadblocks and exposes ministers to a wider range of ideas and proposals
  • Pay for results, not inputs – In many core public services, funding models encourage increases in inputs (e.g. staff) or volumes (e.g. student numbers). These models provide certainty for providers and can support access, but offer limited rewards for innovation, are often restrictive and can have perverse impacts. By comparison, results or outcome-based funding models provide more flexibility and more incentive for productivity gains

Conclusion – One can’t help thinking Professor C. Northcote Parkinson would agree with much of what they say

P.S.1 – One could substitute UK for NZ in all the above and the messages would still be the same

P.S.2 – Hence, there are many good lessons the UK, and others, could learn from it

P.S.3 – The UK still has no equivalent to the NZ Productivity Commission to come up with such weighty reports – our media should be forever banging a drum for ministers to create and support one, and encouraging tax-payers to ask “WHY NOT?”