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This is the personal website of Mark Cliffe, Chief Economist of the ING Group.

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The Circular Economy’s Six ‘C’ Challenge

Why market forces alone won’t drive it

In a recent presentation to the Financial Services Forum I addressed the financing of the Circular Economy. A growing number of companies are beginning to acknowledge its potential to deliver sustainable development. But adopting its zero waste philosophy of reduce, reuse and recycle – what I called the ‘veganism of sustainability’ – is easier said than done. Why aren’t circular business models taking off faster? Market forces alone plainly aren’t enough. Here are the six ‘C’ challenges:

circular economy market forces

 

  1. Consumer demand. A vocal minority of consumers are advocates of sustainable living, but few are willing to pay up for it. Surveys show that few consumers are prepared to pay more for green products, let alone ones that embrace fully circular principles. And what they tell pollsters may overstate their real appetite to do so. Lack of accepted definitions and labelling of circular products doesn’t help, but in any case businesses face the challenge of making them price competitive.
  2. Counter trends. Although sustainability is now a cultural trend, there are still cultural trends running in the opposite direction. Some business models are not merely waste-intensively linear, but actively accelerating resource use. Fast fashion has spurred rapid increases in clothing purchases and disposal, with so far limited push back. E-commerce is fuelling a want-it-now culture of over-ordering, fast delivery and return. Moreover, the burgeoning middle class in the emerging markets are embracing the consumerist habits of the developed world.
  3. CSR investor pressure. Businesses are also facing pressure to ‘go circular’ from investors adopting more ethical corporate and social responsibility (CSR) principles. But while this trend is growing, it is hampered by controversies over whether these principles lead to higher investment returns. Some studies show that they do, but the question of whether the profitability of sustainable principles are the cause or the effect of business success is unresolved.
  4. Costs. For many businesses it is cheaper to use virgin raw materials or brand new parts rather than to embrace recycling, reuse or reassembly. Prices of non-renewable resources, while volatile, have been cycling around a flat trend for decades, providing little incentive to limit their consumption.
  5. Culture. Apart from cost, it is simply easier for businesses to continue with traditional resource-using production and distribution methods. Although enlightened large corporations and idealistic start-ups are embracing circular principles, there is still a long way to go in shifting corporate cultures towards circular economy principles. In part this is because, unlike the traditional linear business model, the circular business model involves new forms of collaboration and transaction with companies along, and even outside, existing supply chains.
  6. Creativity. Having accepted the culture of circularity, companies need to innovate and invest in it. Creativity is required not just around eco-design, reuse, repair, and recycling, but also business processes, supply chains and market places for recycling and second-hand products, parts and materials.

Of these challenges, the final challenge of creativity is perhaps the most important. Innovative breakthroughs would go a long way in helping to address the other challenges. The precedent of the rapid progress in reducing the cost of renewable energy, and the development of new platform businesses, give hope that business can accelerate progress towards the pervasive adoption of the circular economy in the long term. But in the meantime, policy intervention is needed to incentivise action through taxes and subsidies, positive advocacy and proscriptive rules and regulations.

 

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The Great Disruption

PS cover 2019

The Great Disruption is the apposite title of Project Syndicate’s magazine for 2019. It explores the implications of the political fallout out from the financial crisis, globalisation, technological change and rising inequality. I was privileged to step into the panel debate at the recent launch event in London, chaired by Larry Hathaway and featuring Ngaire Woods and Lucrezia Reichlin. You can now listen to this on the Project Syndicate website here.

Here’s my contribution to the magazine, in which I explore how companies might deal with the Great Disruption:

Doing Business in the Great Disruption

Asset prices leave financial markets vulnerable to destabilising setbacks

A decade ago, the global financial crisis cast a spotlight directly on financial institutions; but that scrutiny has since morphed into a more general scepticism about corporate behaviour. While the tech giants that are driving the digital disruption have become the centre of attention, no company should assume that the Great Disruption will be a mere passing storm. A prudent outlook would accept that today’s polarization could get worse before it gets better. After all, populists have been on the march in the midst of a sustained economic upswing and falling unemployment. Just think what will happen when the next recession arrives. Though forecasters are not ringing alarm bells about a recession in 2019, high asset prices leave financial markets vulnerable to destabilizing setbacks. And while the current crop of populists might not fare well in the next recession, they could well be replaced by others with even more radical ideas.

Moreover, while politicians come and go, other key elements of the Great Disruption will endure. The new-technology genie is out of the bottle. The rapid deployment of digitalization and artificial intelligence (AI) will be hard to stop, owing not only to the pervasive benefits these technologies bring but also to the competition they have spurred between countries – led by the United States and China – to be the winner that takes all. Similarly, the environmental challenges of climate change and resource usage are not about to go away. If anything, they will intensify as a result of populist climate denial, delay, and prevarication. Accordingly, companies should think of themselves as polar explorers, whose top priority is always to avoid freezing to death.

To survive the Great Disruption, companies first need to be careful what they say. Policy advocacy risks triggering a backlash and boycotts, and one critical presidential tweet can send share prices tumbling. In an era of social media and fake news, active but sensitive reputation management is more challenging than ever.

Foreign companies must be attuned to local cultural diversity

Second, recognizing that trust in big business is fragile, corporate leaders need to understand not just populist politicians but also the motivations and desires of the people who support them. Foreign companies, in particular, must be attuned to local cultural diversity. And ensuring the privacy and security of client data is another critical ingredient in building and maintaining trust.

Third, companies need to be better prepared to weather shocks, by de-risking their operations and balance sheets. Scenario and contingency planning, along with stress testing, are crucial for building the resilience and flexibility needed for survival. In particular, complex international supply chains and lean inventory-management techniques can be caught out by capricious political decisions and other shocks. Once companies have built up resilience, they can start to look for opportunities that the Great Disruption may offer.

To that end, multinationals should start behaving more like ‘multi-locals’ With countries so internally divided, companies will need to pay more attention to the nuances of local interests when serving their customers. Looking beyond urban elites, there are profitable opportunities in catering to less advantaged segments of the population. These cohorts’ concerns are what governments – populist or not – are under increasing pressure to address.

A major focus in the months and years ahead will be the tension between the US and China

Moreover, digital technologies and AI are creating new possibilities to serve disadvantaged groups with segmented and personalized products. Already, policymakers in Europe and elsewhere have begun to look for ways to address the dominance of US and Chinese tech companies. If that increased attention leads to tax, data, and privacy policies which level the competitive playing field, there could be new business opportunities for others.

Companies also should consider adopting a “barbell” investment strategy: having made their core businesses resilient to polarization, they can reserve a small proportion of their investment budgets for bets that promise high pay-offs. This calls for agility because companies will need to respond quickly to changing circumstances. But so long as they keep bigger buffers and reserves, they will be able to pounce on bargains after negative shocks. Today’s stretched asset valuations suggest that such shocks are becoming more likely. But even if they don’t materialize in 2019, companies can start thinking through their options. A major focus in the months and years ahead will be the tension between the US and China, which may depress asset prices, presenting attractive entry points for the booming Chinese and intra-Asian regional markets.

Companies should play the long game on environmental sustainability

Finally, companies should play the long game on environmental sustainability. Populism and nationalism may be weakening cooperation on these global challenges, particularly now that

President Donald Trump has withdrawn the US from the Paris climate agreement. But this means that there will be an even greater need for action in the long run. Alternatively, the populists themselves may come to see the attraction of shifting taxation from workers – especially their core voters – toward fossil fuels.

This may not be imminent in the US, where Trump has committed to propping up the coal industry. But the falling cost of renewable energy presents a longer-term opportunity to make the shift away from fossil fuels. Not only will less expensive renewables depress fossil-fuel prices, but, absent policy action, they will also stimulate a counterproductive rise in energy consumption. To prevent this, policymakers could raise taxes on energy generally and use the revenue to fund cuts in other taxes.

This article appears in ‘The Year Ahead 2019: The Great Disruption’ published by Project Syndicate, and also on ING’s THINK website, here.

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Forecasting is Fallible, But Necessary

In my first piece for Project Syndicate I build on my earlier post on the lessons for economic forecasters.  The subtitle of their post prompted some questions about the usefulness of big data. There’s no doubt that economic forecasters can benefit from machine learning, which provides powerful ways of spotting patterns in past (big) data. Nevertheless, this focus on past data means that is of limited use for forecasting in the midst of uncertainty and unprecedented structural change…

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Future of Banking – digital disruption, digital diversity

In this video for Oracle, I discuss the future of banking. Digitalisation means that banks will have to figure out how to best use data and combine it creatively with external data sources to provide better and more holistic services to their customers. But platform companies are raising strategic questions for the banks to address. Can banks retain the customer relationship, or are they going to turn themselves into utilities, processing transactions in the background? The core of the banks’ profitability remains their savings and loan activities. In considering whether to pursue this balance sheet-led business model in the future, banks are likely to pursue a variety of strategies.

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Machine, Platform, Crowd – Book Review

Andrew McAfee and Erik Brynjolfsson have done it again. ‘Machine, Platform, Crowd’ is a lucid and timely exploration of three powerful trends unleashed by the digital revolution. They describe the ‘three rebalancings’: first, as machine learning either complements or supplants human minds, secondly , as platforms drive the selection, production and distribution of products and services, and thirdly, as on-line crowds increasingly augment or surpass the core functions of companies.

Although the authors don’t put it this way, there are essentially three economic drivers of the ‘triple revolution’. First, the “free, perfect and instant economics of digital information goods in a time of pervasive networks” which means that “the marginal cost of an additional digital copy is (almost) zero”. Second, the positive network effects from the growth in user interaction. As they put it, “Networked goods can become more valuable as more people use them. The result is ‘demand side economies of scale’, giving an advantage to bigger networks”.

The third driver is the distinctive ability of multi-sided platforms to subsidise one or more set of users in order to incentivise the participation of other sets of users. Digital technology allows many platforms to offer free or discounted services which draw in consumers. This encourages ecosystem growth by attracting more producers or advertisers who pay for the privilege, in turn making it more attractive for other consumers and users.

The book is clearly structured into three sections discussing the title themes, with handy summaries at the end of each chapter. Although the three themes are given equal billing, in the end what dominates is the potential for platforms to capitalise on machine learning and crowd mobilisation. Chapter 7 observes that platforms compete on drawing in users’ contributions and curating them effectively, “but it becomes much more difficult to build a vibrant platform if at least two are already in place”. Network effects and the reluctance of consumers to switch or use more than one platform (or “multi-home”) allows successful platforms to dominate their markets. McAfee and Brynjolfsson are curiously reluctant to use the word, but tendency of platforms towards monopoly is clear. The potentially negative effects of this on competition and innovation are left unexplored, although they would probably need another book to do them justice.

The book also glosses over another distinctive feature of the growth of platform companies: many of today’s giants spent years losing money. Investors have tolerated this, placing faith in the platforms’ ability to turn rapid growth in their user base into longer term profits. They perceive market dominance arising from investing in network effects has long term value. The problem for incumbent companies facing digital disruption is how to secure similar investor indulgence in seeking to transform themselves. The authors argue that there is room for incumbent companies to co-exist with platforms, but they leave little room to doubt that the momentum is with the latter.

The authors end on an optimistic note, saying that “the next few decades should be better than any other […] so far”, although they are careful to describe this as “possibility and a goal”. Indeed, they are suitably humble throughout about making predictions of the disruptive trend that they describe. Amusingly, while the publishers describe the book as a “toolkit” MacAfee and Brynjolfsson say at the outset that they are not offering a “playbook” and that “we suspect that people who offer such a playbook are kidding either themselves or their readers. There is simply too much change and […] uncertainty”. Nevertheless, they have done us all a great service in explaining some of the powerful trends that will shape our future.

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How our mobiles could make us rich

Our mobiles could help us lead richer lives – this was the bold claim that I made at the recent Ignite conference in London. Based on my earlier post entitled Beyond Mobile Banking, I noted that people already say that their mobiles have improved their money management: according to ING’s International Survey last year, 71% of Europeans said so. Given the difficulty that most people have with financial decisions, mobiles offer enormous potential for further improvement. You can see the presentation here:  How our mobiles could make us rich – Ignite Event 06 03 2017

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Confessions of an Economic Forecaster

forecastingconfessions

Economists’ forecasts are notoriously inaccurate. This fallibility is hardly a great surprise given that we live in an irretrievably uncertain world. So why do we keep doing it? What can we learn? Here are my eighteen secrets and myths about the economic forecasters’ art.

1. Worse than fallible

It’s not just that economic forecasts are prone to be wrong. It’s that they tend to let you down when you most need them. Paul Samuelson once said the stock market has “predicted nine out of the last five recessions”. By comparison, economic forecasters rarely predict any! They are typically caught out by shocks.

2. But decisions need forecasts

Economists are well aware of the fallibility of forecasts. But they have no option but to keep making them. People can’t live without them – all decisions are based on a view of the future. People also keep asking for them because they can provide a (spuriously) precise way of justifying decisions made for other reasons. And if things go wrong, they can blame the ‘expert’!

3. A deceptive air of certainty

In an uncertain world, forecasts, and the story behind them, provide us with comfort. But if we place too much faith in them they may lead us astray. Such overconfidence gives us an illusion of control. We can see this in investment projects that are often late or over-budget. Similarly, official forecasts, for example from governments or central banks, are routinely over-optimistic. They assume policy success, sometimes because they are designed to change people’s expectations and behaviour.

Forecasters often ignore the sage advice that “it’s better to be vaguely right rather than exactly wrong”. Having constructed models to predict the future, forecasters tend to forget that they are only approximations.

4. Charlatans rule

Confident dogmatic forecasters are provocative and often grab the headlines. Unfortunately, as Philip Tetlock has ably demonstrated, their strong but simple narratives are often way off the mark. The devil is often in the detail, so we should be wary of forecasts based on a single factor. It’s also a good discipline to try to attach a probability to your forecasts. Admitting uncertainty and being open about your degree of confidence in your prediction is not a sign of weakness, but helpful realism.

5. Try to be objective

We’re all biased, and our forecasts reflect our pre-conceptions. Forecasters need to acknowledge this, and aim for objective assessments. It is easy to pay too much attention to the most recent and most shocking news. Yet, the latest news may be misleading, and forecasters often gloss over the fact that newly published statistics can be revised dramatically. They have a natural human tendency to indulge in elegant, after-the-fact rationalisations. Hindsight is indeed a wonderful thing. But it’s all too tempting to succumb to selective memory, cherishing your successes while forgetting your mistakes.

6. The more the merrier

By making more forecasts, you have more chances to ‘win’ by getting something right! But more seriously, it also gives you more chances to learn. Rather than sticking with a forecast that is off track, it is important to revise your views in the light of new information. As John Maynard Keynes put it “when the facts change, I change my mind. What do you do, sir?”.

7. Unlike traders, forecasters do get marks for style

Financial market traders and investors are judged by the profits that they make. As a result, they are happy to get it right for the wrong reasons. But relying on luck is dangerous. It has a nasty habit of running out on you in the end.

By contrast, and contrary to popular opinion, forecasting is not just, or maybe even mainly, about accuracy. Aside from providing provocative or comforting stories, forecasts are about survival rather than getting it right. Repeatedly making small errors may not kill you, particularly if you learn from them or if they help you to avoid a catastrophic error. And accuracy is often relative, just as when faced with a charging bull, your survival depends not on outrunning the bull, but your neighbour.

8. It’s more about ‘why’ than ‘what’

Meaningful forecasts come with useful explanations. They should provide the users with a framework of thought and wisdom, rather than just knowledge and information. A common error is to assume that because two things are moving together that the two are related, let alone that one is causing the other: as scientists say, correlation is not causation. Wisdom comes from a deeper understanding of why things happen.

9. Past isn’t always prologue

The models used by forecasters are based on past causal relationships. People’s spending, for example, will typically closely reflect what is happening to their incomes. Many such relationships are reliable, with variables showing steady trends or cyclical patterns. But sadly, history doesn’t always repeat itself, or even rhyme. Things don’t always revert to their previous trends, and so what is considered ‘normal’ can be a moving target. Models live in the past, and die in the future.

Former British Prime Minister Harold MacMillan once said that his biggest fear was “Events dear boy, events”. So it is for forecasts. Unprecedented shocks have a habit of wrecking them. Economists make a crucial distinction between risk and uncertainty. Risk is where you know the odds, like playing roulette. Uncertainty is where you don’t. Worse still, you may have no idea that the game is about to change entirely; Former US Defense Secretary Donald Rumsfeld referred to this as the ‘unknown unknowns’. This is why forecasting is more of an art than a science.

10. Some things are more certain than others

The further out you look, the more uncertain things are, making forecasts even less reliable. This is why economic pundits on TV prefer to look smart by focusing on the short term. In fact, some forecasts of monthly indicators, which are released with a month or so’s delay, are more like ‘backcasts’, because anecdotal information helps to ‘predict’ last month’s outcome.

Apart from the time dimension, some variables are more volatile, or prone to extreme outcomes than others. For example, commodity prices and share prices are far more volatile than consumer prices. The importance of this is reinforced by the fact that the starting point for the forecast is often critical to the outcome. Economies, like the weather or biological systems, may be acutely sensitive to initial conditions. The phenomenon of one thing leading to another, or ‘path dependency’, makes forecasting especially difficult. In the metaphor of chaos theory, a butterfly flapping its wings may ultimately lead to a tornado far away.

11. Some things matter more than others

In wrestling with the complexity of the economy, the forecaster’s job is helped by the fact that some things matter more than others, and therefore deserve more attention. Sometimes this is obvious. Thus businesses focus on variables critical to the thresholds key to their decisions to invest, lend, hire, buy or sell. For example, the prospective price of oil may a critical determinant of the profitability of an oil rig.

However, sometimes we are prone to miss crucial risks to a forecast. We may fail to recognise our implicit assumptions, often because we mistakenly take things for granted. The global financial crisis that broke in 2008 at least partly stemmed from a widespread complacency that US house prices don’t fall.

12. Stories can help, but also hinder

One way of dealing with the myriad of possible futures is to construct scenarios. Rather than ‘betting the ranch’ on a single forecast, scenarios are stories constructed around the key drivers of the outlook in terms of their probability and impact. The stories give the users benchmarks for answering the question ”what kind of a world are we in?”. This can be a helpful way of thinking through potentially disruptive events beforehand, because people are liable to make bad decisions under stress. Contingency planning can allow ‘grace under fire’.

But in thinking about alternative scenarios, it’s important not to be blinded about other possibilities – the unknown unknowns. Multiple stories can confuse, and you have to make a choice in the end. The goal is to pick a ‘no regrets’ strategy that’s robust in variety of scenarios – but that’s easier said than done.

13. Don’t just forecast what you can measure

Scenarios are one way tackling important issues that don’t lend themselves to measurement. Economists often tend to ignore non-economic factors that are hard to put into their models. In so doing they often miss the ‘elephants in the room’. In particular, politics and institutions matter. In the long run they can be critical in determining whether countries succeed or fail. And even in the short term, the outcome of a divisive election, as we have been observing recently in the US, may be far more important to the outlook than a forensic analysis of the latest economic data.

14. More data isn’t always better

Data is vital to test theories in the real world. But that doesn’t mean that more data is always better. Data doesn’t speak for itself. Economic forecasters, embarrassed again by the global financial crisis, are in danger of being overshadowed by the new breed of ‘data scientists’. The emergence of Big Data and machine learning is opening up great new forecasting opportunities. In effect, massive computational capacity is being used to spot patterns through automated trial and error.

However, powerful new weapons are dangerous in the wrong hands. ‘Predictive analytics’ is in reality a tool for micro-economic forecasting. It’s not a ‘silver bullet’. More data and more variables gives more chances of correlations. The problem is that the proportion that are spurious and dangerously misleading rises even faster. As Nassim Taleb puts it, ‘Big Data’ brings ‘cherry-picking to an industrial level’. It is crucial to put the data into context, check its quality, understand the questions we are trying to answer, and to separate the signal from the noise. Identifying ‘why’ things are happening is vital for developing better forecasts.

But here the distinction between risk and uncertainty is crucial: more data in an uncertain world may create the impression that we are dealing with calculable risks. In such situations, simple rules of thumb, or ‘heuristics’, may outperform complex forecasting techniques. And they are clearly quicker and cheaper!

15. Magnitudes aren’t always necessary

For some purposes, we don’t need precise, quantified forecasts. Sometimes the direction is enough. That said, it also helps to get the timing right too! This is particularly true of questions that have binary outcomes. Wars and elections are won or lost. We are far more concerned with who wins an election than their margin of victory.

Such events are not easy to plug into economic models, and require different methods, with an emphasis on probabilities. Scenarios based on these contingent forecasts can then be produced. In some cases, some step changes, or what modellers call ‘structural breaks’, can be anticipated, such as preannounced policy shifts or regulatory changes.

16. Not all relationships are linear

Non-linear relationships create major problems for forecasters. They are difficult to model, and harder still to forecast. This is particularly troubling now because many of the emerging new digital technologies exhibit, or have the potential for, exponential, explosive growth. Minor initial errors in your forecasting model quickly lead to huge ones.

In this environment, Nassim Taleb, in his book ‘Antifragile’, advocates that we give up on forecasting. Instead, we should focus on non-predictive forms of decision-making, looking for choices that are not merely robust in the face of unexpected extreme changes, but actively benefit from the them. Sadly, this is easier said than done, and many businesses appear to be paralysed by uncertainty rather than energised by techno-phoria. That said, technological progress is not entirely unpredictable. Moore’s Law, which suggests that computer processing power doubles every two years, has proven to be remarkably durable.

17. Beware of market madness

Surveys of forecasts typically show that only few mavericks stick their necks out. Most forecasters prefer to stick close to the consensus of their peers. They seek safety in numbers, figuring that it is better to get it wrong in a crowd. This is just as well, because when it comes to the financial markets, they are typically wrong, often by a large margin.

Financial markets are not like other markets, because their prices are themselves forecasts. Financial asset prices reflect expected future returns and risk. According to the ‘efficient market hypothesis’, you cannot beat the wisdom of the crowd, because its collective intelligence already incorporates all available information. While it is certainly true that markets quickly incorporate incoming information, and it is hard, if not impossible to outperform them, this hypothesis does not stand up to scrutiny. And all too often, the wisdom of crowds is revealed to be madness, with extreme, unexpected outcomes. Investor beliefs about the future are loosely held, and prone to shifts from optimism to pessimism. This leads to booms and busts in market prices that leave consensus-minded forecasters trailing in their wake.

18. The ‘unusually uncertain’ cliché

Forecasters are prone to downplay the potential accuracy of their forecasts by saying ‘times are unusually uncertain’. It’s a cliché that is surprisingly rarely challenged. All eras have their own uncertainties. The current era is surely beset by potential volatility from digital technologies, political and social turmoil, unconventional monetary policies, financial fragilities, and regulatory change. But hindsight makes it easy to forget previous eras had their own uncertainties at the time – just think of the existential threats of the nuclear arms race during the Cold War. By deterring risk taking, the ‘unusual uncertainty’ cliché often equates to a pessimistic outlook. Yet we should not forget that the darkest hour comes before the dawn. Positive surprises do happen.

A version of this post appeared in the Q3 2016 edition of ING World

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