Technological Advances

Special Report

Highlights The price of Bitcoin has surged this year as the digital currency has gained increasing acceptance. Just as was the case with gold, a global financial system built around Bitcoin would be precariously unstable. Bitcoin transactions are expensive to make and slow to execute, making the currency unsuitable as a medium of exchange. Bitcoin miners consume more energy than many countries. ESG funds are likely to shun companies that associate themselves with the currency. Governments, which stand to lose billions of dollars in seigniorage revenue, will put up more obstacles to Bitcoin. As a result, Bitcoin will lose most of its value over time. Bitcoin And Bullion: Back To The Future? Modern banks grew out of the activity of goldsmith guilds during the Middle Ages. Not only did goldsmiths craft beautiful items from precious metals, but because they had to maintain adequate security, they also tended to offer safekeeping services. Chart 1An Inelastic Money Supply Historically Led To More Banking Crises A wealthy merchant who deposited some gold coins with a goldsmith would receive a receipt validating his claim on the coins. Rather than rushing back to the goldsmith to withdraw some coins in order to make a purchase, it became common practice to offer the receipt instead. To facilitate commerce, goldsmiths began to offer receipts for specific values, marking the creation of the first proto-banknotes. On a typical day, only a small fraction of the gold held on deposit would be withdrawn. As long as goldsmiths always had enough gold on hand to meet demand, they could issue notes in excess of the amount of gold that they held in their vaults. Sometimes the goldsmiths would use those additional notes to purchase goods for themselves. Other times, they would lend out the notes, with interest charged to the borrower. The fractional reserve banking system was born. As the fledgling banking system evolved, it became more sophisticated. Nevertheless, it continued to suffer from a fundamental flaw: It was highly vulnerable to self-fulfilling crises. If people began to fear that a bank would run out of gold reserves, they would rush to the bank to be the first to withdraw their funds. Chart 1 shows that bank runs were very common during the 19th century. What Is Bitcoin Good For? Not Much When Bitcoin enthusiasts talk about a world in which global finance is centred on cryptocurrencies, they see the future. Personally, I see the past. John Maynard Keynes famously called the gold standard a barbarous relic. He had a point. A world based on the “Bitcoin standard” would be just as chaotic as the one that was built on the gold standard. Bitcoin’s defenders would argue that the digital currency has advantages that gold, and more importantly, fiat money do not have. But what exactly are those advantages? It certainly is not ease of use. Whereas the Visa network processes nearly 25,000 transactions per second, the Bitcoin mempool, the pool of unconfirmed transactions, has trouble handling five (Chart 2). Bitcoin transactions take 10 minutes to an hour to complete compared to just a few seconds for most debit or credit cards. The average fee for a Bitcoin transaction is around $30 – a number that has been rising over the past year (Chart 3). Chart 2Bitcoin: The Speed Of Transactions, Or Lack Of It Chart 3Bitcoin: The Cost Per Transaction Is Rising Crypto-optimists insist that these impediments will recede over time. However, this is far from certain. Efforts to expedite Bitcoin transactions have run into “fundamental issues.” Markus Brunnermeier and Joseph Abadi have argued that no cryptocurrency can fully satisfy the three desirable properties of decentralization, correctness, and cost-efficiency. Unlike centralized institutions such as banks, blockchain technology works by generating a sort-of consensus among its participants about what constitutes a legitimate transaction. By its nature, the process tends to be very resource-intensive. Bitcoin’s Big Environmental Footprint Chart 4Bitcoin Is Not Your Eco-Currency (I) This raises another problem with Bitcoin: Its environmental impact. A single Bitcoin transaction consumes more than four times as much energy as 100,000 Visa transactions (Chart 4). Bitcoin’s annual electricity consumption now exceeds that of Pakistan and its 217 million inhabitants (Chart 5). The Bitcoin algorithm requires that “miners” solve computationally intensive problems to earn new coins. It should be stressed that the solutions to these problems have no social value. Miners are not solving protein-folding algorithms that are useful for the discovery of new drugs. They are basically wasting CPU cycles by competing with one another to guess extremely large numbers in the hopes of acquiring a shrinking volume of new coins (the total number of Bitcoins that can ever be produced is limited to 21 million). Chart 5Bitcoin Is Not Your Eco-Currency (II) To make matters worse, more than two-thirds of Bitcoin mining takes place in China, where electricity is primarily generated using coal. Companies that claim to be environmentally conscious have no business trafficking in Bitcoin. What Explains The Bitcoin Bubble? Given the seemingly intractable existential problems that Bitcoin faces, why has its price gone through the roof? To some extent, the euphoria over Bitcoin is part of a broader speculative mania that has swept over everything from shares of electric vehicle companies to dubious SPACs and highly shorted “meme stocks.” No commentary about Bitcoin on the internet is complete with an obligatory prediction that it is “going to da moon.” Chart 6Lower Spending And Higher Income Led To Mounting Excess Savings Occasionally funny late-night talk show host John Oliver has joked that Bitcoin is “everything you don’t understand about money combined with everything you don’t understand about computers.” When people don’t have a good basis for determining what something is worth, they can let their imaginations run wild, causing prices to become unhinged from reality. Bitcoin and other cryptocurrencies are especially susceptible to feedback loops because they rely on network effects: The more people that use Bitcoin, the more appealing it is for others to use it. PayPal’s decision to let its customers trade Bitcoin on its platform, as well as Tesla’s announcement that it will accept it as payment, have stoked hopes that the digital currency is about to go mainstream. A surfeit of savings has also helped propel Bitcoin. US households accumulated $1.5 trillion in excess savings in 2020, two-thirds of which came from spending less than they normally would (Chart 6). The counterpart to the savings glut is a dearth of high-yielding assets. Bitcoin does not generate any cash flow, but with real rates still in negative territory, the prospect of capital appreciation has been more than enough to compensate investors for that deficiency. Bitcoin: Risks Tilted To The Downside Of course, if the price of Bitcoin were to start trending lower, speculators could flee the currency en masse. And therein lies the problem: If people decide that Bitcoin is not worth much, then it will not be worth much. Chart 7The Uses Of Gold: A Breakdown One could argue that the same risk plagues gold. There is some truth to this argument, but it should be noted that gold does have alternative uses, most notably jewelry. According to the World Gold Council, jewelry comprised 46% of the above-ground stock of gold at the end of 2020. Private investors held 22% of the gold stock, while central banks held 17% (Chart 7). Bitcoin has absolutely no alternative use to fall back on. Whereas central banks have been willing to hold gold as part of their external reserves, the same courtesy is unlikely to be extended to Bitcoin. The existence of fiat currencies gives central banks the power to set interest rates and provide liquidity backstops to the financial sector. Bitcoin would deprive them of that power. Governments derive significant benefits from the ability of their central banks to create money out of thin air and use it to purchase goods and services. In the US, this “seigniorage revenue” amounts to over $100 billion per year. Bitcoin threatens this stream of revenue. Speaking to The New York Times DealBook conference on Monday, Treasury Secretary Janet Yellen panned Bitcoin: “To the extent it is used I fear it’s often for illicit finance” she said, adding “It’s an extremely inefficient way of conducting transactions, and the amount of energy that’s consumed in processing those transactions is staggering.” Many companies have cozied up to Bitcoin in order to associate themselves with the digital currency’s technological mystique. As ESG funds start to flee Bitcoin, its price will begin a downward spiral. Stay away.   Peter Berezin Chief Global Strategist pberezin@bcaresearch.com    

Highlights The (earnings) yield premium on tech stocks versus the 10-year bond yield is at its 2.5 percent lower threshold that has signalled four previous market fragilities. Additionally, the 65-day fractal structure of stocks versus bonds has collapsed, signalling a high probability of an exhaustion or correction over the next 65 days. Likewise, the 130-day fractal structure of bitcoin has also collapsed, signalling a high probability of an exhaustion or correction over the next 130 days. Bond yields are unlikely to go much higher; they are likely to go lower. Prefer utilities within the value segment, and prefer healthcare within the growth segment. Offices and bricks-and-mortar retail will never fully reopen. This will devastate the jobs market once the protection from government-funded furlough schemes winds down in 2021. Feature The pandemic will ease in 2021, and with it many of the restrictions on our lives. Yet when it comes to the economy and investment, the great reopening narrative for 2021 is misleading because the world economy has already largely reopened. We quickly learned that, with some adaptations, like working from home, and doing our shopping online, almost all economic activity can resume during a raging global pandemic. As a result, global profits have already rebounded very strongly (Chart of the Week). Chart of the WeekGlobal Profits Have Already Rebounded Very Strongly Manufacturing is fully open. Construction is fully open. Industrial production is fully open. Finance and most services are fully open. Looking at the world’s two largest economies, China is already beyond its pre-pandemic levels of output (Chart I-2), while the US is a mere 0.9 percent below (based on the Atlanta Fed Nowcast of 2.6 percent growth in the fourth quarter)1 (Chart I-3). Chart I-2The Chinese Economy Has Already Rebounded Chart I-3The US Economy Has Already ##br##Rebounded   Offices And Bricks-And-Mortar Retail Will Never Fully Reopen In the great reopening narrative, the end of the pandemic will allow the full reopening of offices, shops, restaurants, bars, travel and leisure. But will former office workers flock back to their offices full-time, or even majority-time? Will consumers flock back to bricks-and-mortar retailers? Will firms flock back to the same extent of business travel? Our high conviction answers are no, no, and no. The reason we will not go back to the pre-pandemic way of doing things is because we have found a better way of doing things. Obviously, we will relish our re-found ability to go on holiday and to meet our fellow humans in the flesh. But do we really need to meet our co-workers every day, or even most days? Do we really need to do our shopping in person every time, or even most times? Do we really need to visit the overseas office every quarter? In 2021 and beyond, we will continue to work, shop, and interact more remotely, not because a pandemic forces us to, but because it improves the quality of our personal and working lives. It improves our standard of living. In 2021 and beyond, we will continue to work, shop, and interact more remotely. Unfortunately, there will be collateral damage. As working from home becomes mainstream, the ecosystem of city centre bars, restaurants, and shops that rely on office workers will wither. This ecosystem’s large footprint can be illustrated by a remarkable fact: the pre-pandemic populations of both Manhattan and central London were 2 million people greater during the weekday daytime than during the night-time. Likewise, as online shopping becomes the default, bricks-and-mortar retailing will go into terminal decline. This is significant because retail employs 10 percent of all workers in the US and the UK, the majority in bricks-and-mortar retail outlets. In the same way, more online meetings and fewer business trips means less employment in the travel and accommodation sectors.  The common thread connecting retail and accommodation and food services is that they produce relatively little output, but account for a lot of jobs – in fact, just 8 percent of output but 20 percent of all jobs (Table I-1). Table I-1Retail Plus Accommodation And Food Services Account For 8 Percent Of Output But 20 Percent Of Jobs Hence, as these sectors wither, the good news is that the impact on economic output will be modest. The bad news is that the ultimate impact on the jobs market will be devastating. Crucially, this ultimate impact on the jobs market will only be felt once the protection from government-funded furlough schemes winds down in 2021. In time, a dynamic economy will redeploy the army of shop assistants, city centre bar and restaurant staff, and cabin crew into fast growing sectors such as healthcare and education. But a process that requires retraining and reskilling will take years not months. During this long adjustment, there is likely to be huge slack in developed economy labour markets. Given that central banks are now explicitly targeting labour market slack, these central banks will be forced to keep nominal bond yields at ultra-low levels for a very long time. The Near-Term Constraint On Bond Yields In the near term, there is an even greater force holding bond yields in check, and that force is something that central banks also explicitly target – financial stability. Higher bond yields would imperil financial stability. The global stock market is at an all-time high because valuations stand 25 percent higher than a year ago (Chart I-4). Valuations have surged because bond yields have collapsed (Chart I-5), but even relative to these ultra-low bond yields, technology sector valuations are now stretched. Chart I-4The Global Stock Market Is At An All-Time High Because Valuations Are 25 Percent Higher Chart I-5Valuations Are 25 Percent Higher Because Bond Yields Have Collapsed The (earnings) yield premium on tech stocks versus the 10-year bond yield is at its 2.5 percent lower threshold that has signalled four previous market fragilities. These previous market fragilities resulted in an exhaustion, or worse, a correction in the stock market in February 2018, October 2018, April 2019, and January 2020. Just as important, these points of fragility signalled that bond yields were approaching a major or minor peak (Chart I-6). Chart I-6Tech Stock Valuations Are Fragile Hence, in the early part of 2021 at least, steer towards investments that will benefit from a backing down of bond yields. This means avoiding value stocks as an aggregate, because value cannot outperform growth unless bond yields are rising (Chart I-7). However, it also means avoiding growth stocks in aggregate as the fragility lies in tech stock valuations. Chart I-7Value Cannot Outperform Growth Unless Bond Yields Are Rising A good strategy is to prefer utilities within the value segment, given that utilities benefit from lower bond yields (Chart I-8). And prefer healthcare within the growth segment, given the sector’s more reasonable valuation. Chart I-8Banks Cannot Outperform Utilities Unless Bond Yields Are Rising Stocks Are Vulnerable… And So Is Bitcoin Manias occur in markets when marginal buyers keep flooding in at a higher and higher price. (Likewise, panics occur when marginal sellers keep flooding in at a lower and lower price.) The supply of marginal buyers fuelling the strong uptrend tends to come from longer-term investors who are uncharacteristically behaving like short-term momentum traders for fear of missing out on the rally. For example, an investor with a 130-day investment horizon shouldn’t buy because of a one-day price increase. If he does, then his investment horizon has shrunk to 1-day. In this example, the strong uptrend will run out of fuel when the 130-day investors who are fuelling it are all in. This is defined by the 130-day fractal structure of the investment collapsing, meaning that its 130-day fractal dimension has reached its lower bound. If someone now puts on a sell order, there are no more 130-day horizon investors available to be the marginal buyer at the current price. Having sucked in all the 130-day investors, an investor with an even longer horizon, say 260 days, must step in as the marginal buyer. The likely outcome is a price correction because the longer-term investor is likely to buy only when a lower price satisfies his value compass. The other possibility is that the 260-day investor joins the uptrend, becoming a marginal buyer at the current price, adding more fuel to the mania. This is the less likely outcome because the longer that an investor’s horizon is, the more faithful he is likely to be to his valuation compass. Nevertheless, sometimes the valuation compass goes awry because of structural shifts or massive intervention by policymakers, allowing the trend to continue. The above describes the basis of our proprietary fractal trading system. In a nutshell, when the fractal structure of an investment collapses, the probability of a trend reversal increases sharply, and the probability of a trend continuation decreases sharply. Right now, the 65-day fractal structure of stocks versus bonds has collapsed, signalling a high probability of an exhaustion or correction over the next 65 days (see final section). Likewise, the 130-day fractal structure of bitcoin has also collapsed, signalling a high probability of an exhaustion or correction over the next 130 days (Chart I-9). Chart I-9The 130-Day Fractal Structure Of Bitcoin Has Collapsed To be clear, these rallies can continue uninterrupted if longer-term investors join the bandwagon. But this would require them to discard their valuation compasses. Hence, on balance, we think that this is the lower probability outcome. Also, to be clear, the long-term direction of both stocks versus bonds and bitcoin is up. The vulnerability we refer to is of a tactical pullback within a structural uptrend. An Excellent Year For The Fractal Trading System Among our most recent trades, overweight Portugal versus Italy achieved its 7 percent profit target, and underweight Australian construction materials (James Hardie, Lendlease, and Boral) achieved its 6 percent profit target. This takes the 2020 win ratio to a very pleasing 63 percent, comprising 18.4 winning trades versus 11 losing trades. Using a position size that delivers 2 percent for a win (and -2 percent for a loss), this equates to a 2020 return of 15 percent with a worst drawdown of -6 percent. By comparison, the MSCI All Country World index delivered a similar return of 17 percent but with a much more severe worst drawdown of -34 percent. 63 percent is a great win ratio. 63 percent is a great win ratio, but our aim is to reach 70 percent. To this end we are preparing several enhancements to the system which we will unveil in the coming weeks. Stay tuned. Fractal Trading System* As already discussed, we are targeting a tactical pullback in the MSCI All Country World Index versus the 30-year T-bond. The profit-target and symmetrical stop-loss are set at 5.8 percent. Chart I-10 The rolling 12-month win ratio now stands at 63 percent. When the fractal dimension approaches the lower limit after an investment has been in an established trend it is a potential trigger for a liquidity-triggered trend reversal. Therefore, open a countertrend position. The profit target is a one-third reversal of the preceding 13-week move. Apply a symmetrical stop-loss. Close the position at the profit target or stop-loss. Otherwise close the position after 13 weeks. * For more details please see the European Investment Strategy Special Report “Fractals, Liquidity & A Trading Model,” dated   December 11, 2014, available at eis.bcaresearch.com.   Dhaval Joshi Chief European Investment Strategist dhaval@bcaresearch.com Footnotes 1 The GDP rebound creates a dissonance. If GDP is indicating a largely recovered economy, but our lives feel far from normal, is GDP really a good measure or objective for our wellbeing? We will leave a deeper discussion of this to a later date. Fractal Trading System   Cyclical Recommendations Structural Recommendations Closed Fractal Trades Trades Closed Trades Asset Performance Currency & Bond Equity Sector Country Equity Indicators Bond Yields Chart II-1Indicators To Watch - Bond Yields Chart II-2Indicators To Watch - Bond Yields Chart II-3Indicators To Watch - Bond Yields Chart II-4Indicators To Watch - Bond Yields   Interest Rate Chart II-5Indicators To Watch - Interest Rate Expectations Chart II-6Indicators To Watch - Interest Rate Expectations Chart II-7Indicators To Watch - Interest Rate Expectations Chart II-8Indicators To Watch - Interest Rate Expectations  

Special Report

Dear Client, This Special Report is the full transcript and slides of a presentation I recently gave at the London School of Economics symposium: 'Will I Work For AI, Or Will AI Work For Me?' The presentation pulls together several years of research analyzing the impact of current technological advances on work, the economy and society. I hope you find the presentation insightful and provocative, especially the narrative surrounding Slide 12. Dhaval Joshi Slide 2 Feature Good afternoon Thank you very much for the invitation to speak here at the London School of Economics. The specific question you asked me was: will we be able to work in the future? (Slide 1). To which my answer is yes, an emphatic yes. I'm very optimistic that we will be able to work in the future. And one reason I'm saying this is, imagine that we had this symposium 100 years ago. I suspect we might have had exactly the same fears that we have right now (Slide 2). Slide 1 Slide 2 Specifically, at the start of the 20th century, about 35% of all jobs were on farms and another 6% were domestic servants. At the time, you could probably also have said, "Well, these jobs aren't going to exist." More or less half of the jobs that existed at that time were going to disappear - and disappear they did. So we'd have thought there would be mass unemployment. Of course, there wasn't mass unemployment, because just as jobs were destroyed, we had an equivalent job creation (Slide 3). For example, at the start of the 20th century, less than 5% of people worked in professional and technical jobs. But by the end of the century, these jobs employed a quarter of the workforce. I guess what I'm saying is that we're very conscious of job destruction because we can see existing jobs being destroyed. But we're not very conscious of job creation, because in real time, it's difficult to visualize or imagine where these new jobs will be. In essence, what we saw in the 20th century was one major segment of employment basically collapsed from very significant to insignificant. While another segment surged from insignificant to very significant (Slide 4). Slide 3 Slide 4 As you all know, there is an economic thesis that underlies this. It's called Say's Law, derived by French economist Jean-Baptiste Say in 1803. In simple terms, it says that new supply creates new demand. Think about it like this: why would you replace a human with a machine? You would only do that if it increases your productivity, right? Otherwise, it does not make sense to replace a human with any sort of machine, including AI. But because you have increased productivity, you then have extra income to spend on new goods and services. Now if those goods and services are being supplied by a machine, then you can redeploy humans to satiate new desires, desires that do not even exist at the time. In economic terms, the producer of X - as long as his products are demanded - is able to buy Y (Slide 5). The question is, what is Y? Y is the new product or service. Let me give you some examples (Slide 6). In the 19th century, we had the advent of railways. And then someone thought. "Hang on a minute. We have this way of moving things around much faster, and we've got all these people who live hundreds of miles from the coast who might want to eat fresh fish." So this was the birth of the frozen food industry. But you could not have the frozen food industry without railways. What I'm saying is that entrepreneurs will seize the new technology to satiate a desire. Or even create a new desire because maybe the people in the middle of the country never thought they could eat fresh sea fish. Until someone came along and said, "you can eat fresh fish now." Slide 5 Slide 6 Another example is, as technology improved the health and longevity of your teeth someone thought. "Well, hang on a minute. Maybe there's a desire to make teeth look beautiful." And we created this whole new industry called the dental cosmetics industry. We know this because prior to the 1960s, there was no job called dental technician or dental hygienist. A third example is, let's say that we have more advanced healthcare and pharmaceuticals, so humans are living longer and healthier lives. Well, then you can sort of ask. "Hang on a minute. Don't you want your dog to live the same long and healthy life that you're living?" And this is behind the explosion of the pet care industry that we're seeing at the moment. So while one segment of the economy will employ less, a new segment will come along to replace it. In the 20th century we saw farm work disappearing but professional work rising. Today, we are seeing manufacturing and driving jobs disappearing but healthcare work rising (Slide 7). Which does raise a pretty obvious question (Slide 8). Is there anything really different this time around? Slide 7 Slide 8 Well, the answer is yes, there is a subtle but crucial difference this time around. To see the difference, we have to look more closely at where jobs are being destroyed, and where they are being created. As you can see, the mega-sectors losing a lot of jobs are manufacturing, the auto industry, and finance (Slide 9). While on the other side of the ledger, we have job creation in health, social work and education. But now, let's look in a little more detail. Where, specifically, are the jobs being created? For this we have to look at the United States data which is much more granular than in Europe. Here are the top five subsectors of job creation this decade (Slide 10). At the top of the list is food services and drinking places, which is just a euphemistic way of describing bartenders, waitresses, and pizza delivery boys. We also have a lot of new administrative jobs and care workers. What is the common link in this job creation? Answer: these are predominantly low-income jobs. Slide 9 Slide 10 So it is true that we have an enormous amount of job creation in the last decade or so, and the policymakers keep boasting about it, they say, "Well look, the unemployment rate in the U.S. is at a record low, the unemployment rate in the UK is at a record low, the unemployment rate in Germany is at a record low. We're creating loads and loads of jobs." The trouble is that these are predominantly low-income jobs. Meanwhile the job destruction is in middle-income jobs in manufacturing and finance. This means what we're seeing in the labour market is called a 'negative composition effect' - a hollowing out of middle incomes. So while we're getting loads and loads of job creation, it is not translating into wage inflation at an aggregate level. I think one of the reasons is a concept called Moravec's paradox. Professor Hans Moravec is an expert in robotics and Artificial Intelligence, and he noticed this paradox (Slide 11). He said, "Look. For AI, the things that we think are difficult are actually easy." By easy, he means they're doable. Let me give you some specific examples. Say someone could speak five languages fluently and translate between them at ease. We would think that person is a genius, a real rare specimen, and the economy would value this person extremely highly, probably pay that person hundreds of thousands of pounds at a minimum. But actually, AI can translate across five languages quite easily, and even something like Google Translate, which we all use, does a reasonably good first stab at translating from one language to another. Slide 11 Or consider something like insurance underwriting. Pricing an insurance premium from lots of data on a risk. AI can do that extremely well, much better than a human can. Or medical diagnosis. Figuring out what's wrong with a patient from very detailed medical data. Again, AI beats humans hands down on that. What I'm saying is, these skills that we thought were difficult transpired not to be that difficult for AI, because they just amount to narrow-frame pattern recognition and repetition of algorithms. Whereas, the second part of Moravec's paradox is that AI finds the easy things very hard. Things that we think are really innate, we don't even give them a second thought like walking up some stairs, cleaning a table, moving objects around, and cleaning around them. Actually, AI finds these things incredibly difficult, almost impossible. We have a false sense of what is difficult and what is easy. The main reason is that the things that we find innate took millions and millions of years of human brain evolution for us to find them innate. And as AI is in essence trying to replicate the human brain, only now are we recognizing that things that we find innate are actually incredibly complex. If it took millions and millions of years to evolve the sensorimotor skills that allow us to walk up some stairs, recognize subtle emotional signals, and respond appropriately, then obviously AI is going to find it very, very difficult to replicate those innate human skills. Conversely, the brain's ability to do calculus, construct a grammatical structure for a language, or play chess only evolved relatively recently. So AI can do them very easily. Which brings me to quite a profound thought. If there's one thing that I want you to remember from this presentation it is this (Slide 12). Might we have completely misvalued the human brain? Might we have grossly overvalued things that are actually quite easy? And might we have undervalued things which are actually very, very difficult? And what AI is now doing is correcting this huge error. In which case, the next decade could be extremely disruptive as AI corrects this economic misvaluation of our skills. Slide 12 This might also explain the mystery as to why there is no wage inflation when the Phillips curve says there should be. The Phillips curve makes a simple relationship between the unemployment rate and wage pressures. And the folks at the Federal Reserve and Bank of England, they're sort of getting really perplexed. They're saying, "Look, unemployment is so low. Where is this wage inflation? It's going to kick in any time now." In fact, there's a bit of a paradox going on. For the people who are continuously employed in the same job, there has been pretty good wage inflation - at sort of three, four percent (Slide 13). But when you take the negative composition effect into account, then suddenly there's this big gap because what's happening is that the well-paid jobs are disappearing to be replaced by lower-paid jobs. So even if you give the bartender making thirty thousand a big pay rise to thirty-five thousand. Even if you hire two of them, but you're losing a finance job paying over a hundred thousand, then at the aggregate level, you won't see much wage inflation. And this problem, I think, continues for the next few years, minimum. It means that you will not get the wage pressures that a lot of economists think you're going to get from the low unemployment rate. Because you have to look at the quality of the jobs as well as the quantity. I think there is another disturbing impact from a societal perspective. Look again at where the jobs are being lost and where they're being created, and look at the percentage of male employees (Slide 14). Job destruction is occurring in sectors that are male-dominated, whereas job creation is occurring in sectors that are female-dominated. Slide 13 Slide 14 AI is good at narrow-frame pattern recognition and repetition of algorithms and functions - jobs like driving, which are typically male-dominated. Whereas jobs that require emotional input, emotional understanding, and empathy in the 'caring sectors' are typically female-dominated. So if you're a male, you're in trouble. You're in a lot of trouble. Obviously, there'll be re-training, so all the guys who were driving trucks will have to retrain as nurses, or as essential carers. But if you're a female, things are looking okay. You can see that in the data (Slide 15). Female labour force participation is in a very clear uptrend. Male participation is flat to down. This varies by country by country, and in the U.S., it's catastrophic for males, especially young males. Young male participation in the U.S. is really falling off a cliff at the moment. I think the other thing to say from a societal perspective is that the so-called 'Superstar Economy' is booming - both superstar individuals and superstar firms. One way of seeing this is in this index called 'the cost of living extremely well' calculated every year by Forbes (Slide 16). Whereas the ordinary CPI includes the cost of bread and milk, the CPI index for the extremely rich includes the cost of Petrossian caviar and Dom Perignon champagne. And a Learjet 70, a Sikorsky S-76D helicopter. I think there's a pedigree racehorse in there too. Anyway, we're seeing the CPI for the extremely rich rising at a dramatically faster pace than the CPI for society as a whole. So it would seem that superstar individuals and superstar firms are really thriving. Slide 15 Slide 16 Let's explain this dynamic in terms of a superstar we all recognise - Roger Federer. Roger Federer was unknown initially, but as he went up the tennis rankings and became a superstar, his income grew exponentially. The other aspect is, how long can he stay a superstar? Because all superstars are eventually displaced by a new superstar. So there's two aspects to the dynamics of superstar incomes (Slide 17). First, how exponential is your income growth? And second, how long do you stay a superstar? What I'm saying is that the rise of AI, by hollowing out the middle jobs, actually allows a few superstars to have this exponential rise in their income. Let's think about it in terms of the legal profession. The top lawyer will be in huge demand. Technology really boosts him. Not just AI, but things like the internet, the fact that social media will reinforce his position, whereby everyone will know who he is. Even if he can't service you directly, he will have a team with his brand on it. And he can stay there for longer before he is displaced. So this is the mechanism by which technology can increase income inequality by hollowing out the middle. In the legal profession, the assistant lawyer who just checks a document for simple legal principle, well the machine can do that. But the guy who knows all the oddities, who knows all the loopholes that can win you the case, the machine won't be able to do that. Essentially what I'm saying is that the technological revolution - it's not just AI, it's technology in aggregate, including the internet and social media, and so on - it increases the rate of income growth for a few superstar individuals and firms. And it increases their longevity (Slide 18). And these are the two drivers for the Pareto distribution of incomes. You can actually go through the mathematics of this to show that it does increase the polarization of incomes. Slide 17 Slide 18 Let's sum up (Slide 19). First of all, yes, we will be able to work in the future. I don't think there's any doubt about that because there will be new jobs created, the nature of which we can only guess because we're going to get new industries to satiate our new desires. However, in the coming years, middle-income work will suffer high disruption because of Moravec's Paradox. Some things that we thought were difficult are actually quite easy for AI. But things like gardening, plumbing, nursing, and childcare are very difficult for machines to replicate. Which means that low-income work will suffer much less disruption and, of course, low-income work will get paid better over time - though the gap is so large at the moment that it's preventing overall wage inflation from kicking in. And that, I think, will persist for the next few years at a minimum. Slide 19 Men are going to suffer much more disruption than women because of the nature of the job destruction versus the job creation. And the final point is that superstars will thrive. All of this has a lot of implications for how we respond as a society, and maybe we will need some support mechanisms in this period of disruption. I think the most intense disruption will be in the next decade. After that we will reach a new equilibrium once we have actually corrected this misvaluation of the brain, this misvaluation of what it is that makes us truly human. Thank you very much. Dhaval Joshi, Senior Vice President Chief European Investment Strategist dhaval@bcaresearch.com

Special Report

Dear Client, In addition to this Special Report written by my colleagues Mark McClellan and Brian Piccioni, we are sending you an abbreviated weekly report. Best regards, Peter Berezin, Chief Global Strategist Global Investment Strategy Highlights Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. Technological advance in the past has not prevented improving living standards or led to ever rising joblessness over the decades, but pessimists argue that recent advances are different. The issue is important for financial markets. If structural factors such as automation are holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. We see no compelling evidence that the displacement effect of emerging technologies is any stronger than in the past. Robot usage has had a modest positive impact on overall productivity. Despite this contribution, overall productivity growth has been dismal over the past decade. If automation is increasing 'exponentially' and displacing workers on a broad scale as some claim, one would expect to see accelerating productivity growth, robust capital spending and more violent shifts in occupational shares. Exactly the opposite has occurred. Periods of strong growth in automation have historically been associated with robust, not lackluster, wage gains, contrary to the consensus view. The Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. This and other evidence suggest that it is difficult to make the case that robots will make it tougher for central banks to reach their inflation goals than did previous technological breakthroughs. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. Feature Recent breakthroughs in technology are awe-inspiring and unsettling. These advances are viewed with great trepidation by many because of the potential to replace humans in the production process. Hype over robots is particularly shrill. Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. In the first in our series of Special Reports focusing on the structural factors that might be preventing central banks from reaching their inflation targets, we demonstrated that the impact of Amazon is overstated in the press. We estimated that E-commerce is depressing inflation in the U.S. by a mere 0.1 to 0.2 percentage points. This Special Report tackles the impact of automation. We are optimistic that robot technology and artificial intelligence will significantly boost future productivity, and thus reduce costs. But, is there any evidence at the macro level that robot usage has been more deflationary than technological breakthroughs in the past and is, thus, a major driver of the low inflation rates we observe today across the major countries? The question matters, especially for the outlook for central bank policy and the bond market. If structural factors are indeed holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. However, if low inflation simply reflects long lags between wages and the tightening labor market, then inflation may suddenly lurch to life as it has at the end of past cycles. The bond market is not priced for that scenario. Are Robots Different? A Special Report from BCA's Technology Sector Strategy service suggested that the "robot revolution" could be as transformative as previous General Purpose Technologies (GPT), including the steam engine, electricity and the microchip.1 GPTs are technologies that radically alter the economy's production process and make a major contribution to living standards over time. The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks," and this encompasses a broad range of machines: From the Jacquard Loom, which was invented over 200 years ago, on to Numerically Controlled (NC) mills and lathes, pick and place machines used in the manufacture of electronics, Autonomous Vehicles (AVs), and even homicidal robots from the future such as the Terminator. Our Technology Sector report made the case that there is nothing particularly sinister about robots. They are just another chapter in a long history of automation. Nor is the displacement of workers unprecedented. The industrial revolution was about replacing human craft labor with capital (machines), which did high-volume work with better quality and productivity. This freed humans for work which had not yet been automated, along with designing, producing and maintaining the machinery. Agriculture offers a good example. This sector involved over 50% of the U.S. labor force until the late 1800s. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," contributed to a massive rise in output-per-man hour. The number of hours worked to produce a bushel of wheat fell by almost 98% from the mid-1800s to 1955. This put a lot of farm hands out of work, but these laborers were absorbed over time in other growing areas of the economy. It is the same story for all other historical technological breakthroughs. Change is stressful for those directly affected, but rising productivity ultimately lifts average living standards. Robots will be no different. As we discuss below, however, the increasing use of robots and AI may have a deeper and longer-lasting impact on inequality. Strong Tailwinds Chart 1Robots Are Getting Cheaper Factory robots have improved immensely due to cheaper and more capable control and vision systems. As these systems evolve, the abilities of robots to move around their environment while avoiding obstacles will improve, as will their ability to perform increasingly complex tasks. Most importantly, robots are already able to do more than just routine tasks, thus enabling them to replace or aid humans in higher-skilled processes. Robot prices are also falling fast, especially after quality-adjusting the data (Chart 1). Units are becoming easier to install, program and operate. These trends will help to reduce the barriers-to-entry for the large, untapped, market of small and medium sized enterprises. Robots also offer the ability to do low-volume "customized" production and still keep unit costs low. In the future, self-learning robots will be able to optimize their own performance by analyzing the production of other robots around the world. Robot usage is growing quickly according to data collected by the International Federation of Robotics (IFR) that covers 23 countries. Industrial robot sales worldwide increased to almost 300,000 units in 2016, up 16% from the year before (Chart 2). The stock of industrial robots globally has grown at an annual average pace of 10% since 2010, reaching slightly more than 1.8 million units in 2016.2 Robot usage is far from evenly distributed across industries. The automotive industry is the major consumer of industrial robots, holding 45% of the total stock in 2016 (Chart 3). The computer & electronics industry is a distant second at 17%. Metals, chemicals and electrical/electronic appliances comprise the bulk of the remaining stock. Chart 2Global Robot Usage Chart 3Global Robot Usage By Industry (2016) As far as countries go, Japan has traditionally been the largest market for robots in the world. However, sales have been in a long-term downtrend and the stock of robots has recently been surpassed by China, which has ramped up robot purchases in recent years (Chart 4). Robot density, which is the stock of robots per 10 thousand employed in manufacturing, makes it easier to compare robot usage across countries (Chart 5, panel 2). By this measure, China is not a heavy user of robots compared to other countries. South Korea stands at the top, well above the second-place finishers (Germany and Japan). Large automobile sectors in these three countries explain their high relative robot densities. Chart 4Stock Of Robots By Country (I) Chart 5Stock Of Robots By Country (II) (2016) While the growth rate of robot usage is impressive, it is from a very low base (outside of the automotive industry). The average number of robots per 10,000 employees is only 74 for the 23 countries in the IFR database. Robot use is tiny compared to total man hours worked. In the U.S., spending on robots is only about 5% of total business spending on equipment and software (Chart 6). To put this into perspective, U.S. spending on information, communication and technology (ICT) equipment represented 35-40% of total capital equipment spending during the tech boom in the 1990s and early 2000s.3 Chart 6U.S. Investment In Robots The bottom line is that there is a lot of hype in the press, but robots are not yet widely used across countries or industries. It will be many years before business spending on robots approaches the scale of the 1990s/2000s IT boom. A Deflationary Impact? As noted above, we view robotics as another chapter in a long history of technological advancements. Pessimists suggest that the latest advances are different because they are inherently more threatening to the overall job market and wage share of total income. If the pessimists are right, what are the theoretical channels though which this would have a greater disinflationary effect relative to previous GPT technologies? Faster Productivity Gains: Enhanced productivity drives down unit labor costs, which may be passed along to other industries (as cheaper inputs) and to the end consumer. More Human Displacement: The jobs created in other areas may be insufficient to replace the jobs displaced by robots, leading to lower aggregate income and spending. The loss of income for labor will simply go to the owners of capital, but the point is that the labor share of income might decline. Deflationary pressures could build as aggregate demand falls short of supply. Even in industries that are slow to automate, just the threat of being replaced by robots may curtail wage demands. Inequality: Some have argued that rising inequality is partly because the spoils of new technologies over the past 20 years have largely gone to the owners of capital. This shift may have undermined aggregate demand because upper income households tend to have a high saving rate, thereby depressing overall aggregate demand and inflationary pressures. The human displacement effect, described above, would exacerbate the inequality effect by transferring income from labor to the owners of capital. 1. Productivity It is difficult to see the benefits of robots on productivity at the economy-wide level. Productivity growth has been abysmal across the major developed countries since the Great Recession, but the productivity slowdown was evident long before Lehman collapsed (Chart 7). The productivity slowdown continued even as automation using robots accelerated after 2010. Chart 7Productivity Collapsed Despite Automation Some analysts argue that lackluster productivity is simply a statistical mirage because of the difficulties in measuring output in today's economy. We will not get into the details of the mismeasurement debate here. We encourage interested clients to read a Special Report by the BCA Global Investment Strategy service entitled "Weak Productivity Growth: Don't Blame The Statisticians." 4 Our colleague Peter Berezin makes the case that the unmeasured utility accruing from free internet services is large, but so was the unmeasured utility from antibiotics, radio, indoor plumbing and air conditioning. He argues that the real reason that productivity growth has slowed is that educational attainment has decelerated and businesses have plucked many of the low-hanging fruit made possible by the IT revolution. Cyclical factors stemming from the Great Recession and financial crisis are also to blame, as capital spending has been slow to recover in most of the advanced economies. Some other factors that help to explain the decline in aggregate productivity are provided in Appendix 1. Nonetheless, the poor aggregate productivity performance does not mean that there are no benefits to using robots. The benefits are evident at the industrial level, where measurement issues are presumably less vexing for statisticians (i.e., it is easier to measure the output of the auto industry, for example, than for the economy as a whole). Chart 8 plots the level of robot density in 2016 with average annual productivity growth since 2004 for 10 U.S. manufacturing industries (robot density is presented in deciles). A loose positive relationship is apparent. Chart 8U.S.: Productivity Vs. Robot Density Academic studies estimate that robots have contributed importantly to economy-wide productivity growth. The Centre for Economic and Business Research (CEBR) estimated that labor productivity growth rises by 0.07 to 0.08 percentage points for every 1% rise in the rate of robot density.5 This implies that robots accounted for roughly 10% of the productivity growth experienced since the early 1990s in the major economies. Another study of 14 industries across 17 countries by the Centre for Economic Performance (CEP) found that robots boosted annual productivity growth by 0.36 percentage points over the 1993-2007 period.6 This is impressive because, if this estimate holds true for the U.S., robots' contribution to the 2½% average annual U.S. total productivity growth over the period was 14%. To put the importance of robotics into historical context, its contribution to productivity so far is roughly on par with that of the steam engine (Chart 9). It falls well short of the 0.6 percentage point annual productivity contribution from the IT revolution. The implication is that, while the overall productivity performance has been dismal since 2007, it would have been even worse in the absence of robots. What does this mean for inflation? According to the "cost push" model of the inflation process, an increase in productivity of 0.36% that is not accompanied by associated wage gains would reduce unit labor costs (ULC) by the same amount. This should trim inflation if the cost savings are passed on to the end consumer, although by less than 0.36% because robots can only depress variable costs, not fixed costs. There indeed appears to be a slight negative relationship between robot density and unit labor costs at the industrial level in the U.S., although the relationship is loose at best (Chart 10). Chart 9GPT Contribution To Productivity Chart 10U.S.: Unit Labor Costs Vs. Robot Density In theory, divergences in productivity across industries should only generate shifts in relative prices, and "cost push" inflation dynamics should only operate in the short term. Most economists believe that inflation is a purely monetary phenomenon in the long run, which means that central banks should be able to offset positive productivity shocks by lowering interest rates enough that aggregate demand keeps up with supply. Indeed, the Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. Also, note that inflation is currently low across the major advanced economies, irrespective of the level of robot intensity (Chart 11). From this perspective, it is hard to see that robots should take much of the credit for today's low inflation backdrop. Chart 11Inflation Vs. Robot Density 2. Human Displacement A key question is whether robots and humans are perfect substitutes. If new technologies introduced in the past were perfect substitutes, then it would have led to massive underemployment and all of the income in the economy would eventually have migrated to the owners of capital. The fact that average real household incomes have risen over time, and that there has been no secular upward trend in unemployment rates over the centuries, means that new technologies were at least partly complementary with labor (i.e., the jobs lost as a direct result of productivity gains were more than replaced in other areas of the economy over time). Rather than replacing workers, in many cases tech made humans more productive in their jobs. Rising productivity lifted income and thereby led to the creation of new jobs in other areas. The capital that workers bring to the production process - the skills, know-how and special talents - became more valuable as interaction with technology increased. Like today, there were concerns in the 1950s and 1960s that computerization would displace many types of jobs and lead to widespread idleness and falling household income. With hindsight, there was little to worry about. Some argue that this time is different. Futurists frequently assert that the pace of innovation is not just accelerating, it is accelerating 'exponentially'. Robots can now, or will soon be able to, replace humans in tasks that require cognitive skills. This means that they will be far less complementary to humans than in the past. The displacement effect could thus be much larger, especially given the impressive advances in artificial intelligence. However, Box 1 discusses why the threat to workers posed by AI is also heavily overblown in the media. The CEP multi-country study cited above did not find a large displacement effect; robot usage did not affect the overall number of hours worked in the 23 countries studied (although it found distributional effects - see below). In other words, rather than suppressing overall labor input, robot usage has led to more output, higher productivity, more jobs and stronger wage and income growth. A report by the Economic Policy Institute (EPI)7 takes a broader look at automation, using productivity growth and capital spending as proxies. Automation is what occurs as the implementation of new technologies is incorporated along with new capital equipment or software to replace human labor in the workplace. If automation is increasing 'exponentially' and displacing workers on a broad scale, one would expect to see accelerating productivity growth, robust capital spending, and more violent shifts in occupational shares. Exactly the opposite has occurred. Indeed, the report demonstrates that occupational employment shifts were far slower in the 2000-2015 period than in any decade in the 1900s (Chart 12). Box 1 The Threat From AI Is Overblown Media coverage of AI/Deep Learning has established a consensus view that we believe is well off the mark. A recent Special Report from BCA's Technology Sector Strategy service dispels the myths surrounding AI.8 We believe the consensus, in conjunction with warnings from a variety of sources, is leading to predictions, policy discussions, and even career choices based on a flawed premise. It is worth noting that the most vocal proponents of AI as a threat to jobs and even humanity are not AI experts. At the root of this consensus is the false view that emerging AI technology is anything like true intelligence. Modern AI is not remotely comparable in function to a biological brain. Scientists have a limited understanding of how brains work, and it is unlikely that a poorly understood system can be modeled on a computer. The misconception of intelligence is amplified by headlines claiming an AI "taught itself" a particular task. No AI has ever "taught itself" anything: All AI results have come about after careful programming by often PhD-level experts, who then supplied the system with vast amounts of high quality data to train it. Often these systems have been iterated a number of times and we only hear of successes, not the failures. The need for careful preparation of the AI system and the requirement for high quality data limits the applicability of AI to specific classes of problems where the application justifies the investment in development and where sufficient high-quality data exists. There may be numerous such applications but doubtless many more where AI would not be suitable. Similarly, an AI system is highly adapted to a single problem, or type of problem, and becomes less useful when its application set is expanded. In other words, unlike a human whose abilities improve as they learn more things, an AI's performance on a particular task declines as it does more things. There is a popular misconception that increased computing power will somehow lead to ever improving AI. It is the algorithm which determines the outcome, not the computer performance: Increased computing power leads to faster results, not different results. Advanced computers might lead to more advanced algorithms, but it is pointless to speculate where that may lead: A spreadsheet from 2001 may work faster today but it still gives the same answer. In any event, it is worth noting that a tool ceases to be a tool when it starts having an opinion: there is little reason to develop a machine capable of cognition even if that were possible. Chart 12U.S. Job Rotation Has Slowed The EPI report also notes that these indicators of automation increased rapidly in the late 1990s and early 2000s, a period that saw solid wage growth for American workers. These indicators weakened in the two periods of stagnant wage growth: from 1973 to 1995 and from 2002 to the present. Thus, there is no historical correlation between increases in automation and wage stagnation. Rather than automation, the report argues that it was China's entry into the global trading system that was largely responsible for the hollowing out of the U.S. manufacturing sector. We have also made this argument in previous research. The fact that the major advanced economies are all at, or close to, full employment supports the view that automation has not been an overwhelming headwind for job creation. Chart 13 demonstrates that there has been no relationship between the change in robot density and the loss of manufacturing jobs since 1993. Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. Interestingly, despite a worsening labor shortage, robot density among Japanese firms is falling. Moreover, the Japanese data show that the industries that have a high robot usage tend to be more, not less, generous with wages than the robot laggard industries. Please see Appendix 2 for more details. Chart 13Global Manufacturing Jobs Vs. Robot Density The bottom line is that it does not appear that labor displacement related to automation has been responsible in any meaningful way for the lackluster average real income growth in the advanced economies since 2007. 3. Inequality That said, there is evidence suggesting that robots are having important distributional effects. The CEP study found that robot use has reduced hours for low-skilled and (to a lesser extent) middle-skilled workers relative to the highly skilled. This finding makes sense conceptually. Technological change can exacerbate inequality by either increasing the relative demand for skilled over unskilled workers (so-called "skill-biased" technological change), or by inducing companies to substitute machinery and other forms of physical capital for workers (so-called "capital-biased" technological change). The former affects the distribution of labor income, while the latter affects the share of income in GDP that labor receives. A Special Report appearing in this publication in 2014 focused on the relationship between technology and inequality.9 The report highlighted that much of the recent technological change has been skill-biased, which heavily favors workers with the talent and education to perform cognitively-demanding tasks, even as it reduces demand for workers with only rudimentary skills. Moreover, technological innovations and globalization increasingly allow the most talented individuals to market their skills to a much larger audience, thus bidding up their wages. The evidence suggests that faster productivity growth leads to higher average real wages and improved living standards, at least over reasonably long horizons. Nonetheless, technological change can, and in the future almost certainly will, increase income inequality. The poor will gain, but not as much as the rich. The fact that higher-income households tend to maintain a higher savings rate than low-income households means that the shift in the distribution of income toward the higher-income households will continue to modestly weigh on aggregate demand. Can the distribution effect be large enough to have a meaningful depressing impact on inflation? We believe that it has played some role in the lackluster recovery since the Great Recession, with the result that an extended period of underemployment has delivered a persistent deflationary impulse in the major developed economies. However, as discussed above, stimulative monetary policy has managed to overcome the impact of inequality and other headwinds on aggregate demand, and has returned the major countries roughly to full employment. Indeed, this year will be the first since 2007 that the G20 economies as a group will be operating slightly above a full employment level. Inflation should respond to excess demand conditions, irrespective of any ongoing demand headwind stemming from inequality. Conclusions Technological change has led to rising living standards over the decades. It did not lead to widespread joblessness and did not prevent central banks from meeting their inflation targets over time. The pessimists argue that this time is different because robots/AI have a much larger displacement effect. Perhaps it will be 20 years before we will know the answer. But our main point is that we have found no evidence that recent advances in robotics and AI, while very impressive, will be any different in their macro impact. There is little evidence that the modern economy is less capable in replacing the jobs lost to automation, although the nature of new technologies may be affecting the distribution of income more than in the past. Real incomes for the middle- and lower-income classes have been stagnant for some time, but this is partly due to productivity growth that is too low, not too high. Moreover, it is not at all clear that positive productivity shocks are disinflationary beyond the near term. The link between robot usage and unit labor costs over the past couple of decades is loose at best at the industry level, and is non-existent when looking across the major countries. The Fed was able to roughly meet its 2% inflation target in the 1990s and the first half of the 2000s, despite IT's impressive contribution to productivity growth during that period. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. The global output gap will shift into positive territory this year for the first time since the Great Recession. Any resulting rise in inflation will come as a shock since the bond market has discounted continued low inflation for as far as the eye can see. We expect bond yields and implied volatility to rise this year, which may undermine risk assets in the second half. Mark McClellan Senior Vice President The Bank Credit Analyst Brian Piccioni Vice President Technology Sector Strategy Appendix 1 Why Is Productivity So Low? A recent study by the OECD10 reveals that, while frontier firms are charging ahead, there is a widening gap between these firms and the laggards. The study analyzed firm-level data on labor productivity and total factor productivity for 24 countries. "Frontier" firms are defined to be those with productivity in the top 5%. These firms are 3-4 times as productive as the remaining 95%. The authors argue that the underlying cause of this yawning gap is that the diffusion rate of new technologies from the frontier firms to the laggards has slowed within industries. This could be due to rising barriers to entry, which has reduced contestability in markets. Curtailing the creative-destruction process means that there is less pressure to innovate. Barriers to entry may have increased because "...the importance of tacit knowledge as a source of competitive advantage for frontier firms may have risen if increasingly complex technologies were to increase the amount and sophistication of complementary investments required for technological adoption." 11 The bottom line is that aggregate productivity is low because the robust productivity gains for the tech-savvy frontier companies are offset by the long tail of firms that have been slow to adopt the latest technology. Indeed, business spending has been especially weak in this expansion. Chart 14 highlights that the slowdown in U.S. productivity growth has mirrored that of the capital stock. Chart 14U.S. Capex Shortfall Partly To Blame For Poor Productivity Appendix 2 Japan - The Leading Edge Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. The popular press is full of stories of how robots are taking over. If the stories are to be believed, robots are the answer to the country's shrinking workforce. Robots now serve as helpers for the elderly, priests for weddings and funerals, concierges for hotels and even sexual partners (don't ask). Prime Minister Abe's government has launched a 5-year push to deepen the use of intelligent machines in manufacturing, supply chains, construction and health care. Indeed, Japan was the leader in robotics use for decades. Nonetheless, despite all the hype, Japan's stock of industrial robots has actually been eroding since the late 1990s (Chart 4). Numerous surveys show that firms plan to use robots more in the future because of the difficulty in hiring humans. And there is huge potential: 90% of Japanese firms are small- and medium-sized (SME) and most are not currently using robots. Yet, there has been no wave of robot purchases as of 2016. One problem is the cost; most sophisticated robots are simply too expensive for SMEs to consider. This suggests that one cannot blame robots for Japan's lack of wage growth. The labor shortage has become so acute that there are examples of companies that have turned down sales due to insufficient manpower. Possible reasons why these companies do not offer higher wages to entice workers are beyond the scope of this report. But the fact that the stock of robots has been in decline since the late 1990s does not support the view that Japanese firms are using automation on a broad scale to avoid handing out pay hikes. Indeed, Chart 15 highlights that wage deflation has been the greatest in industries that use almost no robots. Highly automated industries, such as Transportation Equipment and Electronics, have been among the most generous. This supports the view that the productivity afforded by increased robot usage encourages firms to pay their workers more. Looking ahead, it seems implausible that robots can replace all the retiring Japanese workers in the years to come. The workforce will shrink at an annual average pace of 0.33% between 2020 and 2030, according to the Japan Institute for Labour Policy and Training. Productivity growth would have to rise by the same amount to fully offset the dwindling number of workers. But that would require a surge in robot density of 4.1, assuming that each rise in robot density of one adds 0.08% to the level of productivity (Chart 16). The level of robot sales would have to jump by a whopping 2½ times in the first year and continue to rise at the same pace each year thereafter to make this happen. Of course, the productivity afforded by new robots may accelerate in the coming years, but the point is that robot usage would likely have to rise astronomically to offset the impact of the shrinking population. Chart 15Japan: Earnings Vs. Robot Density Chart 16Japan: Where Is The Flood Of Robots? The implication is that, as long as the Japanese economy continues to grow above roughly 1%, the labor market will continue to tighten and wage rates will eventually begin to rise. 1 Please see Technology Sector Strategy Special Report "The Coming Robotics Revolution," dated May 16, 2017, available at tech.bcaresearch.com 2 Note that this includes only robots used in manufacturing industry, and thus excludes robots used in the service sector and households. However, robot usage in services is quite limited and those used in households do not add to GDP. 3 Note that ICT investment and capital stock data includes robots. 4 Please see BCA Global Investment Strategy Special Report "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 5 Centre for Economic and Business Research (January 2017) "The Impact of Automation." A Report for Redwood. In this report, robot density is defined to be the number of robots per million hours worked. 6 Graetz, G., and Michaels, G. (2015): "Robots At Work." CEP Discussion Paper No 1335. 7 Mishel, L., and Bivens, J. (2017): "The Zombie Robot Argument Lurches On," Economic Policy Institute. 8 Please see BCA Technology Sector Strategy Special Report "Bad Information - Why Misreporting Deep Learning Advances Is A Problem," dated January 9, 2018, available at tech.bcaresearch.com 9 Please see The Bank Credit Analyst, "Rage Against the Machines: Is Technology Exacerbating Inequality?" dated June 2014, available at bca.bcaresearch.com. 10 OECD Productivity Working Papers, No. 05 (2016) "The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy." 11 Please refer to page 8.

Special Report

Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. Technological advance in the past has not prevented improving living standards or led to ever rising joblessness over the decades, but pessimists argue that recent advances are different. The issue is important for financial markets. If structural factors such as automation are holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. We see no compelling evidence that the displacement effect of emerging technologies is any stronger than in the past. Robot usage has had a modest positive impact on overall productivity. Despite this contribution, overall productivity growth has been dismal over the past decade. If automation is increasing 'exponentially' and displacing workers on a broad scale as some claim, one would expect to see accelerating productivity growth, robust capital spending and more violent shifts in occupational shares. Exactly the opposite has occurred. Periods of strong growth in automation have historically been associated with robust, not lackluster, wage gains, contrary to the consensus view. The Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. This and other evidence suggest that it is difficult to make the case that robots will make it tougher for central banks to reach their inflation goals than did previous technological breakthroughs. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. Recent breakthroughs in technology are awe-inspiring and unsettling. These advances are viewed with great trepidation by many because of the potential to replace humans in the production process. Hype over robots is particularly shrill. Media reports warn of a "Robot Apocalypse" that is already laying waste to jobs and depressing wages on a broad scale. In the first in our series of Special Reports focusing on the structural factors that might be preventing central banks from reaching their inflation targets, we demonstrated that the impact of Amazon is overstated in the press. We estimated that E-commerce is depressing inflation in the U.S. by a mere 0.1 to 0.2 percentage points. This Special Report tackles the impact of automation. We are optimistic that robot technology and artificial intelligence will significantly boost future productivity, and thus reduce costs. But, is there any evidence at the macro level that robot usage has been more deflationary than technological breakthroughs in the past and is, thus, a major driver of the low inflation rates we observe today across the major countries? The question matters, especially for the outlook for central bank policy and the bond market. If structural factors are indeed holding back inflation by more than in previous decades, then the Fed will have to proceed very slowly in raising rates. However, if low inflation simply reflects long lags between wages and the tightening labor market, then inflation may suddenly lurch to life as it has at the end of past cycles. The bond market is not priced for that scenario. Are Robots Different? A Special Report from BCA's Technology Sector Strategy service suggested that the "robot revolution" could be as transformative as previous General Purpose Technologies (GPT), including the steam engine, electricity and the microchip.1 GPTs are technologies that radically alter the economy's production process and make a major contribution to living standards over time. The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks," and this encompasses a broad range of machines: From the Jacquard Loom, which was invented over 200 years ago, on to Numerically Controlled (NC) mills and lathes, pick and place machines used in the manufacture of electronics, Autonomous Vehicles (AVs), and even homicidal robots from the future such as the Terminator. Our Technology Sector report made the case that there is nothing particularly sinister about robots. They are just another chapter in a long history of automation. Nor is the displacement of workers unprecedented. The industrial revolution was about replacing human craft labor with capital (machines), which did high-volume work with better quality and productivity. This freed humans for work which had not yet been automated, along with designing, producing and maintaining the machinery. Agriculture offers a good example. This sector involved over 50% of the U.S. labor force until the late 1800s. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," contributed to a massive rise in output-per-man hour. The number of hours worked to produce a bushel of wheat fell by almost 98% from the mid-1800s to 1955. This put a lot of farm hands out of work, but these laborers were absorbed over time in other growing areas of the economy. It is the same story for all other historical technological breakthroughs. Change is stressful for those directly affected, but rising productivity ultimately lifts average living standards. Robots will be no different. As we discuss below, however, the increasing use of robots and AI may have a deeper and longer-lasting impact on inequality. Strong Tailwinds Chart II-1Robots Are Getting Cheaper Factory robots have improved immensely due to cheaper and more capable control and vision systems. As these systems evolve, the abilities of robots to move around their environment while avoiding obstacles will improve, as will their ability to perform increasingly complex tasks. Most importantly, robots are already able to do more than just routine tasks, thus enabling them to replace or aid humans in higher-skilled processes. Robot prices are also falling fast, especially after quality-adjusting the data (Chart II-1). Units are becoming easier to install, program and operate. These trends will help to reduce the barriers-to-entry for the large, untapped, market of small and medium sized enterprises. Robots also offer the ability to do low-volume "customized" production and still keep unit costs low. In the future, self-learning robots will be able to optimize their own performance by analyzing the production of other robots around the world. Robot usage is growing quickly according to data collected by the International Federation of Robotics (IFR) that covers 23 countries. Industrial robot sales worldwide increased to almost 300,000 units in 2016, up 16% from the year before (Chart II-2). The stock of industrial robots globally has grown at an annual average pace of 10% since 2010, reaching slightly more than 1.8 million units in 2016.2 Robot usage is far from evenly distributed across industries. The automotive industry is the major consumer of industrial robots, holding 45% of the total stock in 2016 (Chart II-3). The computer & electronics industry is a distant second at 17%. Metals, chemicals and electrical/electronic appliances comprise the bulk of the remaining stock. Chart II-2Global Robot Usage Chart II-3Global Robot Usage By Industry (2016) As far as countries go, Japan has traditionally been the largest market for robots in the world. However, sales have been in a long-term downtrend and the stock of robots has recently been surpassed by China, which has ramped up robot purchases in recent years (Chart II-4). Robot density, which is the stock of robots per 10 thousand employed in manufacturing, makes it easier to compare robot usage across countries (Chart II-5, panel 2). By this measure, China is not a heavy user of robots compared to other countries. South Korea stands at the top, well above the second-place finishers (Germany and Japan). Large automobile sectors in these three countries explain their high relative robot densities. Chart II-4Stock Of Robots By Country (I) Chart II-5Stock Of Robots By Country (II) (2016) While the growth rate of robot usage is impressive, it is from a very low base (outside of the automotive industry). The average number of robots per 10,000 employees is only 74 for the 23 countries in the IFR database. Robot use is tiny compared to total man hours worked. Chart II-6U.S. Investment In Robots In the U.S., spending on robots is only about 5% of total business spending on equipment and software (Chart II-6). To put this into perspective, U.S. spending on information, communication and technology (ICT) equipment represented 35-40% of total capital equipment spending during the tech boom in the 1990s and early 2000s.3 The bottom line is that there is a lot of hype in the press, but robots are not yet widely used across countries or industries. It will be many years before business spending on robots approaches the scale of the 1990s/2000s IT boom. A Deflationary Impact? As noted above, we view robotics as another chapter in a long history of technological advancements. Pessimists suggest that the latest advances are different because they are inherently more threatening to the overall job market and wage share of total income. If the pessimists are right, what are the theoretical channels though which this would have a greater disinflationary effect relative to previous GPT technologies? Faster Productivity Gains: Enhanced productivity drives down unit labor costs, which may be passed along to other industries (as cheaper inputs) and to the end consumer. More Human Displacement: The jobs created in other areas may be insufficient to replace the jobs displaced by robots, leading to lower aggregate income and spending. The loss of income for labor will simply go to the owners of capital, but the point is that the labor share of income might decline. Deflationary pressures could build as aggregate demand falls short of supply. Even in industries that are slow to automate, just the threat of being replaced by robots may curtail wage demands. Inequality: Some have argued that rising inequality is partly because the spoils of new technologies over the past 20 years have largely gone to the owners of capital. This shift may have undermined aggregate demand because upper income households tend to have a high saving rate, thereby depressing overall aggregate demand and inflationary pressures. The human displacement effect, described above, would exacerbate the inequality effect by transferring income from labor to the owners of capital. 1. Productivity It is difficult to see the benefits of robots on productivity at the economy-wide level. Productivity growth has been abysmal across the major developed countries since the Great Recession, but the productivity slowdown was evident long before Lehman collapsed (Chart II-7). The productivity slowdown continued even as automation using robots accelerated after 2010. Chart II-7Productivity Collapsed Despite Automation Some analysts argue that lackluster productivity is simply a statistical mirage because of the difficulties in measuring output in today's economy. We will not get into the details of the mismeasurement debate here. We encourage interested clients to read a Special Report by the BCA Global Investment Strategy service entitled "Weak Productivity Growth: Don't Blame The Statisticians." 4 Our colleague Peter Berezin makes the case that the unmeasured utility accruing from free internet services is large, but so was the unmeasured utility from antibiotics, radio, indoor plumbing and air conditioning. He argues that the real reason that productivity growth has slowed is that educational attainment has decelerated and businesses have plucked many of the low-hanging fruit made possible by the IT revolution. Cyclical factors stemming from the Great Recession and financial crisis are also to blame, as capital spending has been slow to recover in most of the advanced economies. Some other factors that help to explain the decline in aggregate productivity are provided in Appendix II-1. Nonetheless, the poor aggregate productivity performance does not mean that there are no benefits to using robots. The benefits are evident at the industrial level, where measurement issues are presumably less vexing for statisticians (i.e., it is easier to measure the output of the auto industry, for example, than for the economy as a whole). Chart II-8 plots the level of robot density in 2016 with average annual productivity growth since 2004 for 10 U.S. manufacturing industries (robot density is presented in deciles). A loose positive relationship is apparent. Chart II-8U.S.: Productivity Vs. Robot Density Academic studies estimate that robots have contributed importantly to economy-wide productivity growth. The Centre for Economic and Business Research (CEBR) estimated that labor productivity growth rises by 0.07 to 0.08 percentage points for every 1% rise in the rate of robot density.5 This implies that robots accounted for roughly 10% of the productivity growth experienced since the early 1990s in the major economies. Another study of 14 industries across 17 countries by the Centre for Economic Performance (CEP) found that robots boosted annual productivity growth by 0.36 percentage points over the 1993-2007 period.6 This is impressive because, if this estimate holds true for the U.S., robots' contribution to the 2½% average annual U.S. total productivity growth over the period was 14%. To put the importance of robotics into historical context, its contribution to productivity so far is roughly on par with that of the steam engine (Chart II-9). It falls well short of the 0.6 percentage point annual productivity contribution from the IT revolution. The implication is that, while the overall productivity performance has been dismal since 2007, it would have been even worse in the absence of robots. What does this mean for inflation? According to the "cost push" model of the inflation process, an increase in productivity of 0.36% that is not accompanied by associated wage gains would reduce unit labor costs (ULC) by the same amount. This should trim inflation if the cost savings are passed on to the end consumer, although by less than 0.36% because robots can only depress variable costs, not fixed costs. There indeed appears to be a slight negative relationship between robot density and unit labor costs at the industrial level in the U.S., although the relationship is loose at best (Chart II-10). Chart II-9GPT Contribution To Productivity Chart II-10U.S.: Unit Labor Costs Vs. Robot Density In theory, divergences in productivity across industries should only generate shifts in relative prices, and "cost push" inflation dynamics should only operate in the short term. Most economists believe that inflation is a purely monetary phenomenon in the long run, which means that central banks should be able to offset positive productivity shocks by lowering interest rates enough that aggregate demand keeps up with supply. Indeed, the Fed was successful in meeting the 2% inflation target on average from 2000 to 2007, when the impact of the IT revolution on productivity (and costs) was stronger than that of robot automation today. Also, note that inflation is currently low across the major advanced economies, irrespective of the level of robot intensity (Chart II-11). From this perspective, it is hard to see that robots should take much of the credit for today's low inflation backdrop. Chart II-11Inflation Vs. Robot Density 2. Human Displacement A key question is whether robots and humans are perfect substitutes. If new technologies introduced in the past were perfect substitutes, then it would have led to massive underemployment and all of the income in the economy would eventually have migrated to the owners of capital. The fact that average real household incomes have risen over time, and that there has been no secular upward trend in unemployment rates over the centuries, means that new technologies were at least partly complementary with labor (i.e., the jobs lost as a direct result of productivity gains were more than replaced in other areas of the economy over time). Rather than replacing workers, in many cases tech made humans more productive in their jobs. Rising productivity lifted income and thereby led to the creation of new jobs in other areas. The capital that workers bring to the production process - the skills, know-how and special talents - became more valuable as interaction with technology increased. Like today, there were concerns in the 1950s and 1960s that computerization would displace many types of jobs and lead to widespread idleness and falling household income. With hindsight, there was little to worry about. Some argue that this time is different. Futurists frequently assert that the pace of innovation is not just accelerating, it is accelerating 'exponentially'. Robots can now, or will soon be able to, replace humans in tasks that require cognitive skills. This means that they will be far less complementary to humans than in the past. The displacement effect could thus be much larger, especially given the impressive advances in artificial intelligence. However, Box II-1 discusses why the threat to workers posed by AI is also heavily overblown in the media. The CEP multi-country study cited above did not find a large displacement effect; robot usage did not affect the overall number of hours worked in the 23 countries studied (although it found distributional effects - see below). In other words, rather than suppressing overall labor input, robot usage has led to more output, higher productivity, more jobs and stronger wage and income growth. A report by the Economic Policy Institute (EPI)7 takes a broader look at automation, using productivity growth and capital spending as proxies. Automation is what occurs as the implementation of new technologies is incorporated along with new capital equipment or software to replace human labor in the workplace. If automation is increasing 'exponentially' and displacing workers on a broad scale, one would expect to see accelerating productivity growth, robust capital spending, and more violent shifts in occupational shares. Exactly the opposite has occurred. Indeed, the report demonstrates that occupational employment shifts were far slower in the 2000-2015 period than in any decade in the 1900s (Chart II-12). Box II-1 The Threat From AI Is Overblown Media coverage of AI/Deep Learning has established a consensus view that we believe is well off the mark. A recent Special Report from BCA's Technology Sector Strategy service dispels the myths surrounding AI.8 We believe the consensus, in conjunction with warnings from a variety of sources, is leading to predictions, policy discussions, and even career choices based on a flawed premise. It is worth noting that the most vocal proponents of AI as a threat to jobs and even humanity are not AI experts. At the root of this consensus is the false view that emerging AI technology is anything like true intelligence. Modern AI is not remotely comparable in function to a biological brain. Scientists have a limited understanding of how brains work, and it is unlikely that a poorly understood system can be modeled on a computer. The misconception of intelligence is amplified by headlines claiming an AI "taught itself" a particular task. No AI has ever "taught itself" anything: All AI results have come about after careful programming by often PhD-level experts, who then supplied the system with vast amounts of high quality data to train it. Often these systems have been iterated a number of times and we only hear of successes, not the failures. The need for careful preparation of the AI system and the requirement for high quality data limits the applicability of AI to specific classes of problems where the application justifies the investment in development and where sufficient high-quality data exists. There may be numerous such applications but doubtless many more where AI would not be suitable. Similarly, an AI system is highly adapted to a single problem, or type of problem, and becomes less useful when its application set is expanded. In other words, unlike a human whose abilities improve as they learn more things, an AI's performance on a particular task declines as it does more things. There is a popular misconception that increased computing power will somehow lead to ever improving AI. It is the algorithm which determines the outcome, not the computer performance: Increased computing power leads to faster results, not different results. Advanced computers might lead to more advanced algorithms, but it is pointless to speculate where that may lead: A spreadsheet from 2001 may work faster today but it still gives the same answer. In any event, it is worth noting that a tool ceases to be a tool when it starts having an opinion: there is little reason to develop a machine capable of cognition even if that were possible. Chart II-12U.S. Job Rotation Has Slowed The EPI report also notes that these indicators of automation increased rapidly in the late 1990s and early 2000s, a period that saw solid wage growth for American workers. These indicators weakened in the two periods of stagnant wage growth: from 1973 to 1995 and from 2002 to the present. Thus, there is no historical correlation between increases in automation and wage stagnation. Rather than automation, the report argues that it was China's entry into the global trading system that was largely responsible for the hollowing out of the U.S. manufacturing sector. We have also made this argument in previous research. The fact that the major advanced economies are all at, or close to, full employment supports the view that automation has not been an overwhelming headwind for job creation. Chart II-13 demonstrates that there has been no relationship between the change in robot density and the loss of manufacturing jobs since 1993. Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. Interestingly, despite a worsening labor shortage, robot density among Japanese firms is falling. Moreover, the Japanese data show that the industries that have a high robot usage tend to be more, not less, generous with wages than the robot laggard industries. Please see Appendix II-2 for more details. Chart II-13Global Manufacturing Jobs Vs. Robot Density The bottom line is that it does not appear that labor displacement related to automation has been responsible in any meaningful way for the lackluster average real income growth in the advanced economies since 2007. 3. Inequality That said, there is evidence suggesting that robots are having important distributional effects. The CEP study found that robot use has reduced hours for low-skilled and (to a lesser extent) middle-skilled workers relative to the highly skilled. This finding makes sense conceptually. Technological change can exacerbate inequality by either increasing the relative demand for skilled over unskilled workers (so-called "skill-biased" technological change), or by inducing companies to substitute machinery and other forms of physical capital for workers (so-called "capital-biased" technological change). The former affects the distribution of labor income, while the latter affects the share of income in GDP that labor receives. A Special Report appearing in this publication in 2014 focused on the relationship between technology and inequality.9 The report highlighted that much of the recent technological change has been skill-biased, which heavily favors workers with the talent and education to perform cognitively-demanding tasks, even as it reduces demand for workers with only rudimentary skills. Moreover, technological innovations and globalization increasingly allow the most talented individuals to market their skills to a much larger audience, thus bidding up their wages. The evidence suggests that faster productivity growth leads to higher average real wages and improved living standards, at least over reasonably long horizons. Nonetheless, technological change can, and in the future almost certainly will, increase income inequality. The poor will gain, but not as much as the rich. The fact that higher-income households tend to maintain a higher savings rate than low-income households means that the shift in the distribution of income toward the higher-income households will continue to modestly weigh on aggregate demand. Can the distribution effect be large enough to have a meaningful depressing impact on inflation? We believe that it has played some role in the lackluster recovery since the Great Recession, with the result that an extended period of underemployment has delivered a persistent deflationary impulse in the major developed economies. However, as discussed above, stimulative monetary policy has managed to overcome the impact of inequality and other headwinds on aggregate demand, and has returned the major countries roughly to full employment. Indeed, this year will be the first since 2007 that the G20 economies as a group will be operating slightly above a full employment level. Inflation should respond to excess demand conditions, irrespective of any ongoing demand headwind stemming from inequality. Conclusions Technological change has led to rising living standards over the decades. It did not lead to widespread joblessness and did not prevent central banks from meeting their inflation targets over time. The pessimists argue that this time is different because robots/AI have a much larger displacement effect. Perhaps it will be 20 years before we will know the answer. But our main point is that we have found no evidence that recent advances in robotics and AI, while very impressive, will be any different in their macro impact. There is little evidence that the modern economy is less capable in replacing the jobs lost to automation, although the nature of new technologies may be affecting the distribution of income more than in the past. Real incomes for the middle- and lower-income classes have been stagnant for some time, but this is partly due to productivity growth that is too low, not too high. Moreover, it is not at all clear that positive productivity shocks are disinflationary beyond the near term. The link between robot usage and unit labor costs over the past couple of decades is loose at best at the industry level, and is non-existent when looking across the major countries. The Fed was able to roughly meet its 2% inflation target in the 1990s and the first half of the 2000s, despite IT's impressive contribution to productivity growth during that period. For investors, this means that we cannot rely on automation to keep inflation depressed irrespective of how tight labor markets become. The global output gap will shift into positive territory this year for the first time since the Great Recession. Any resulting rise in inflation will come as a shock since the bond market has discounted continued low inflation for as far as the eye can see. We expect bond yields and implied volatility to rise this year, which may undermine risk assets in the second half. Mark McClellan Senior Vice President The Bank Credit Analyst Brian Piccioni Vice President Technology Sector Strategy Appendix II-1 Why Is Productivity So Low? A recent study by the OECD10 reveals that, while frontier firms are charging ahead, there is a widening gap between these firms and the laggards. The study analyzed firm-level data on labor productivity and total factor productivity for 24 countries. "Frontier" firms are defined to be those with productivity in the top 5%. These firms are 3-4 times as productive as the remaining 95%. The authors argue that the underlying cause of this yawning gap is that the diffusion rate of new technologies from the frontier firms to the laggards has slowed within industries. This could be due to rising barriers to entry, which has reduced contestability in markets. Curtailing the creative-destruction process means that there is less pressure to innovate. Barriers to entry may have increased because "...the importance of tacit knowledge as a source of competitive advantage for frontier firms may have risen if increasingly complex technologies were to increase the amount and sophistication of complementary investments required for technological adoption." 11 The bottom line is that aggregate productivity is low because the robust productivity gains for the tech-savvy frontier companies are offset by the long tail of firms that have been slow to adopt the latest technology. Indeed, business spending has been especially weak in this expansion. Chart II-14 highlights that the slowdown in U.S. productivity growth has mirrored that of the capital stock. Chart II-14U.S. Capex Shortfall Partly To Blame For Poor Productivity Appendix II-2 Japan - The Leading Edge Japan is an interesting case study because it is on the leading edge of the problems associated with an aging population. The popular press is full of stories of how robots are taking over. If the stories are to be believed, robots are the answer to the country's shrinking workforce. Robots now serve as helpers for the elderly, priests for weddings and funerals, concierges for hotels and even sexual partners (don't ask). Prime Minister Abe's government has launched a 5-year push to deepen the use of intelligent machines in manufacturing, supply chains, construction and health care. Indeed, Japan was the leader in robotics use for decades. Nonetheless, despite all the hype, Japan's stock of industrial robots has actually been eroding since the late 1990s (Chart II-4). Numerous surveys show that firms plan to use robots more in the future because of the difficulty in hiring humans. And there is huge potential: 90% of Japanese firms are small- and medium-sized (SME) and most are not currently using robots. Yet, there has been no wave of robot purchases as of 2016. One problem is the cost; most sophisticated robots are simply too expensive for SMEs to consider. This suggests that one cannot blame robots for Japan's lack of wage growth. The labor shortage has become so acute that there are examples of companies that have turned down sales due to insufficient manpower. Possible reasons why these companies do not offer higher wages to entice workers are beyond the scope of this report. But the fact that the stock of robots has been in decline since the late 1990s does not support the view that Japanese firms are using automation on a broad scale to avoid handing out pay hikes. Indeed, Chart II-15 highlights that wage deflation has been the greatest in industries that use almost no robots. Highly automated industries, such as Transportation Equipment and Electronics, have been among the most generous. This supports the view that the productivity afforded by increased robot usage encourages firms to pay their workers more. Looking ahead, it seems implausible that robots can replace all the retiring Japanese workers in the years to come. The workforce will shrink at an annual average pace of 0.33% between 2020 and 2030, according to the Japan Institute for Labour Policy and Training. Productivity growth would have to rise by the same amount to fully offset the dwindling number of workers. But that would require a surge in robot density of 4.1, assuming that each rise in robot density of one adds 0.08% to the level of productivity (Chart II-16). The level of robot sales would have to jump by a whopping 2½ times in the first year and continue to rise at the same pace each year thereafter to make this happen. Of course, the productivity afforded by new robots may accelerate in the coming years, but the point is that robot usage would likely have to rise astronomically to offset the impact of the shrinking population. Chart II-15Japan: Earnings Vs. Robot Density Chart II-16Japan: Where Is The Flood Of Robots? The implication is that, as long as the Japanese economy continues to grow above roughly 1%, the labor market will continue to tighten and wage rates will eventually begin to rise. 1 Please see Technology Sector Strategy Special Report "The Coming Robotics Revolution," dated May 16, 2017, available at tech.bcaresearch.com 2 Note that this includes only robots used in manufacturing industry, and thus excludes robots used in the service sector and households. However, robot usage in services is quite limited and those used in households do not add to GDP. 3 Note that ICT investment and capital stock data includes robots. 4 Please see BCA Global Investment Strategy Special Report "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 5 Centre for Economic and Business Research (January 2017): "The Impact of Automation." A Report for Redwood. In this report, robot density is defined to be the number of robots per million hours worked. 6 Graetz, G., and Michaels, G. (2015): "Robots At Work." CEP Discussion Paper No 1335. 7 Mishel, L., and Bivens, J. (2017): "The Zombie Robot Argument Lurches On," Economic Policy Institute. 8 Please see BCA Technology Sector Strategy Special Report "Bad Information - Why Misreporting Deep Learning Advances Is A Problem," dated January 9, 2018, available at tech.bcaresearch.com 9 Please see The Bank Credit Analyst, "Rage Against The Machines: Is Technology Exacerbating Inequality?" dated June 2014, available at bca.bcaresearch.com 10 OECD Productivity Working Papers, No. 05 (2016): "The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy." 11 Please refer to page 27.

Highlights An increase in the "synthetic" supply of bitcoins via financial derivatives, along with the launch of bitcoin-like alternatives by large established tech companies, will cause the cryptocurrency market to collapse under its own weight. Other areas that could see supply-induced pressures over the coming years include oil, high-yield debt, global real estate, and low-volatility trades. In contrast, the U.S. stock market has seen an erosion in the supply of shares due to buybacks and voluntary delistings. Investors should consider going long U.S. equities relative to high-yield credit, while positioning for higher volatility. Such an outcome would be similar to what happened in the late 1990s, a period when the VIX and credit spreads were trending higher, while stocks continued to hit new highs. A breakdown in NAFTA talks remains the key risk for the Canadian dollar and Mexican peso. Feature Bubbles Burst By Too Much Supply The "cure" for higher prices is higher prices. The dotcom and housing bubbles did not die fully of their own accord. Their demise was expedited by a wave of new supply hitting the market. In the case of the dotcom bubble, a flood of shares from initial and secondary public offerings inundated investors in 2000 (Chart 1). This put significant downward pressure on the prices of internet stocks. The housing boom was similarly subverted by a slew of new construction - residential investment rose to a 55-year high of 6.6% of GDP in 2006 (Chart 2). Chart 1Burst By Too Much Supply: Example 1 Chart 2Burst By Too Much Supply: Example 2 Is bitcoin about to experience a similar fate? On the surface, the answer may seem to be "no." As more bitcoins are "mined," the computational cost of additional production rises exponentially. In theory, this should limit the number of bitcoins that can ever circulate to 21 million, about 80% of which have already been created (Chart 3). Yet if one looks beneath the surface, bitcoin may also be vulnerable to a variety of "supply-side" factors. Chart 3Bitcoin: Most Of It Has Been Mined First, the expansion of financial derivatives tied to the value of bitcoin threatens to create a "synthetic" supply of the cryptocurrency. When someone writes a call option on a stock, the seller of the option is effectively taking a bearish bet while the buyer is taking a bullish bet. The very act of writing the option creates an additional long position, which is exactly offset by an additional short position. Moreover, to the extent that a decision to sell a particular call option will depress the price of similar call options, it will also depress the underlying price of the stock. This is simply because one can have long exposure to a stock either by owning it outright or owning a call option on it. Anything that hurts the price of the latter will also hurt the price of the former. As bitcoin futures begin to trade, investors who are bearish on bitcoin will be able to create short positions that cause the effective number of bitcoins in circulation to rise. This will happen even if the official number of bitcoins outstanding remains the same. Imitation Is The Sincerest Form Of Flattery An increase in synthetic forms of bitcoin supply is one worry for bitcoin investors. Another is the prospect of increased competition from bitcoin-like alternatives. There are now hundreds of cryptocurrencies, most of which use a slight variant of the same blockchain technology that underpins bitcoin. Chart 4Governments Will Want Their Cut So far, the proliferation of new currencies has been largely driven by technologically savvy entrepreneurs working out of their bedrooms or garages. But now companies are getting in on the act. The stock price of Kodak, which apparently is still in business, tripled earlier this week when it announced the launch of its own cryptocurrency. That's just a small taste of what's to come. What exactly is stopping giants such as Facebook, Amazon, Netflix, and Google from issuing their own cryptocurrencies? After all, they already have secure, global networks. Amazon could start giving out a few coins with every sale, and allow shoppers to purchase goods from the online retailer using its new currency. It's simple.1 The only plausible restriction is a legal one: The threat that governments will quash upstart cryptocurrencies for fear that will drive down demand for their own fiat monies. As we noted several weeks ago, the U.S. government derives $100 billion per year in seigniorage revenue from its ability to print currency and use that money to buy goods and services (Chart 4).2 As large companies get into the cryptocurrency arena, governments are likely to respond harshly - sooner rather than later. This week's news that the South Korean government will consider banning the trading of cryptocurrencies on exchanges is a sign of what's to come. Who Else? What other areas are vulnerable to an eventual tsunami of new supply? Four come to mind: Oil: BCA's bullish oil call has paid off in spades. Brent has climbed from $44 last June to $69 currently. Further gains may not be as easily attainable, however. Our energy strategists estimate that the breakeven cost of oil for U.S. shale producers is in the low-$50 range.3 We are now well above this number, which means that shale supply will accelerate. This does not mean that prices cannot go up further in the near term, but it does limit the long-term potential for crude. Real estate: Ultra-low interest rates across much of the world have fueled sharp rallies in home prices. Inflation-adjusted home prices in Canada, Australia, New Zealand, and parts of Europe are well above their pre-Great Recession levels (Chart 5). U.S. real residential home prices are still below their 2006 peak, but commercial real estate (CRE) prices have galloped to new highs (Chart 6). Rent growth within the U.S. CRE sector is starting to slow, suggesting that supply is slowly catching up with demand (Chart 7). Chart 5Where Low Rates Have ##br##Fueled House Prices Chart 6Commercial Real Estate Prices Have ##br##Surpassed Pre-Recession Levels Chart 7Rent Growth Is Cooling Corporate debt: Low rates have also encouraged companies to feast on credit. The ratio of corporate debt-to-GDP in the U.S. and many other countries is close to record-high levels (Chart 8A and Chart 8B). Credit spreads remain extremely tight, but that may change as more corporate bonds reach the market. Chart 8ACorporate Debt-To-GDP ##br##Is Close To Record Highs Chart 8BCorporate Debt-To-GDP ##br##Is Close To Record Highs Low-volatility trades: A recent Bloomberg headline screamed "Short-Volatility Funds Are Being Flooded With Cash."4 The number of volatility contracts traded on the Cboe has increased more than tenfold since 2012. Net short speculative positions now stand at record-high levels (Chart 9). Traders have been able to reap huge gains over the past few years by betting that volatility will decline. The problem is that if volatility starts to rise, those same traders could start to unload their positions, leading to even higher volatility. In contrast to the aforementioned areas, the stock market has seen an erosion in the supply of shares due to buybacks and voluntary delistings. The S&P divisor is down by over 8% since 2005. The number of U.S. publicly-listed companies has nearly halved since the late 1990s (Chart 10). This trend is unlikely to reverse any time soon, given the elevated level of profit margins and the temptation that many companies will have to use corporate tax cuts to step up the pace of share repurchases. Chart 9Low Volatility Is In High Demand Chart 10Erosion Of Supply In The Stock Market Bet On Higher Equity Prices, But Also Higher Volatility And Higher Credit Spreads The discussion above suggests that the relationship between equity prices and both volatility and credit spreads may shift over the coming months. This would not be the first time. Chart 11 shows that the VIX and credit spreads began to trend higher in the late 1990s, even as the S&P 500 continued to hit new record highs. We may be entering a similar phase now. Continued above-trend growth in the U.S. and rising inflation will push up Treasury yields. We declared "The End Of The 35-Year Bond Bull Market" on July 5, 2016 - the exact same day that the 10-year Treasury yield hit a record closing low of 1.37%.5 Higher interest rates will punish financially-strapped borrowers, leading to wider credit spreads. Equity volatility is also likely to rise as corporate health deteriorates and the timing of the next downturn draws closer. Our baseline expectation is that the U.S. and the rest of the world will fall into a recession in late 2019. Financial markets will sniff out a recession before it happens. However, if history is any guide, this will only happen about six months before the start of the recession (Table 1). This suggests that global equities can continue to rally for the next 12 months. With this in mind, we are opening a new trade going long the S&P 500 versus high-yield credit. Chart 11Volatility Can Increase And Spreads ##br##Can Widen As Stock Prices Rise Table 1Too Soon To Get Out Four Currency Quick Hits Four items buffeted currency and fixed-income markets this week. The first was a news story suggesting that China will slow or stop its purchases of U.S. Treasury debt. China's State Administration of Foreign Exchange (SAFE) decried the report as "fake news." Lost in the commotion was the fact that China's holdings of Treasurys have been largely flat since 2011 (Chart 12). China still has a highly managed currency. Now that capital is no longer pouring out of the country, the PBoC will start rebuilding its foreign reserves. Given that the U.S. Treasury market remains the world's largest and most liquid, it is hard to see how China can avoid having to park much of its excess foreign capital in the United States. The second item this week was the Bank of Japan's announcement that it will reduce its target for how many government bonds it buys. This just formalizes something that has already been happening for over a year. The BoJ's purchases of JGBs have plunged over the past twelve months, mainly because its ¥80 trillion target is more than double the ¥30-35 trillion annual net issuance of JGBs (Chart 13). Chart 12China's Holdings Of Treasurys: ##br##Largely Flat Since 2011 Chart 13BoJ Has Been Reducing ##br##Its Bond Purchases Ultimately, none of this should matter that much. The Bank of Japan can target prices (the yield on JGBs) or it can target quantities (the number of bonds it owns), but it cannot target both. The fact that the BoJ is already doing the former makes the latter irrelevant. And with long-term inflation expectations still nowhere near the BoJ's target, the former is unlikely to change. What does this mean for the yen? The Japanese currency is cheap and its current account surplus has swollen to 4% of GDP (Chart 14). Speculators are also very short the currency (Chart 15). This increases the likelihood of a near-term rally, as my colleague Mathieu Savary flagged this week.6 Nevertheless, if global bond yields continue to rise while Japanese yields stay put, it is hard to see the yen moving up and staying up a lot. On balance, we expect USD/JPY to strengthen somewhat this year. Chart 14Yen Is Already Cheap... Chart 15...And Unloved The third item was the revelation in the ECB's December meeting minutes that the central bank will be revisiting its communication stance in early 2018. The speculation is that the ECB will renormalize monetary policy more quickly than what the market is currently discounting. If that were to happen, EUR/USD would strengthen further. All this is possible, of course, but it would likely require that euro area growth surprise on the upside. That is far from a done deal. The euro area economic surprise index has begun to edge lower, and in relative terms, has plunged against the U.S. (Chart 16). Unlike in the U.S., the euro area credit impulse is now negative (Chart 17). Euro area financial conditions have also tightened significantly relative to the U.S. (Chart 18). Chart 16Euro Area Economic ##br##Surprises Edging Lower Chart 17Negative Credit Impulse In The Euro ##br##Area Will Weigh On Growth Chart 18Diverging Financial Conditions ##br##Favor U.S. Over The Euro Area Meanwhile, EUR/USD has appreciated more since 2016 than what one would expect based on changes in interest rate differentials (Chart 19). Speculative positioning towards the euro has also gone from being heavily short at the start of 2017 to heavily long today (Chart 20). Reasonably cheap valuations and a healthy current account surplus continue to work in the euro's favor, but our best bet is that EUR/USD will give up some of its gains over the coming months. Chart 19The Euro Has Strengthened More Than ##br##Justified By Interest Rate Differentials Chart 20Euro Positioning: From Deeply ##br##Short To Record Long Lastly, the Canadian dollar and Mexican peso came under pressure this week on news reports that the U.S. will be pulling out of NAFTA negotiations. Of the four items discussed in this section, this is the one that worries us most. The global supply chain has become highly integrated. Anything that sabotages it would be greatly disruptive. At some level, Trump realizes this, but he also knows that his base wants him to get tough on trade, and unless he does so, his chances of reelection will be even slimmer than they are now. Ultimately, we expect a new NAFTA deal to be reached, but the path from here to there will be a bumpy one. Housekeeping Notes Our long global industrials/short utilities trade is up 12.4% since we initiated it on September 29. We are raising the stop to 10% to protect gains. We are also letting our long 2-year USD/Saudi Riyal forward contract trade expire for a loss of 2.9%. Given the recent improvement in Saudi Arabia's finances, we are not reinstating the trade. Peter Berezin, Chief Global Strategist Global Investment Strategy peterb@bcaresearch.com 1 My thanks to Igor Vasserman, President of SHIG Partners LLC, for his valuable insights on this topic. 2 Please see Global Investment Strategy Special Report, "Bitcoin's Macro Impact," dated September 15, 2017; and Global Investment Strategy Weekly Report, "Don't Fear A Flatter Yield Curve," dated December 22, 2017. 3 Please see Energy Sector Strategy Weekly Report, "Breakeven Analysis: Shale Companies Need ~$50 Oil To Be Self-Sufficient," dated March 15, 2017. 4 Dani Burger, "Short-Volatility Funds Are Being Flooded With Cash," Bloomberg, November 6, 2017. 5 Please see Global Investment Strategy Special Alert, "End Of The 35-year Bond Bull Market," dated July 5, 2016. 6 Please see Foreign Exchange Strategy, "Yen: QQE Is Dead! Long Live YCC!" dated January 12, 2018. Tactical Global Asset Allocation Recommendations Strategy & Market Trends Tactical Trades Strategic Recommendations Closed Trades

Special Report

Highlights Bitcoin and other virtual currencies have sold off sharply in recent days. However, as the turn of the millennium dotcom boom and bust illustrates, wild swings in asset prices can sometimes mask important structural changes that new technologies have unleashed on the global economy. If the proliferation of virtual currencies continues, it will have real macroeconomic effects. Globally, the volume of currency in circulation - the largest component of base money - has grown by 5.5% year-over-year. However, the growth rate would be 7% if virtual currencies were included in the tally. The indirect increase in global liquidity coming from virtual currencies should provide a modest boost to spending. This is somewhat bearish for bonds but bullish for equities. The implications for gold and the dollar are mixed. Governments derive significant "seigniorage revenue" from their ability to issue fiat currency. This is likely to impede the widespread adoption of virtual currencies, ultimately capping their prices. Feature Bitcoin And Beyond The price of bitcoin has been extremely volatile lately, falling by more than 10% last week after the Chinese government announced a ban on so-called Initial Coin Offerings. The downdraft continued into this week, spurred on by JPMorgan CEO Jamie Dimon's description of bitcoin as a "fraud." The recent selloff followed a dizzying ascent which saw the price of the upstart currency surpass $5000 earlier this month (Chart 1). Despite the pullback, one thousand dollars of bitcoin purchased in July 2010 would still be worth $58 million today. Such mind-boggling returns have caught the public's attention. There were more Google searches for "bitcoin" in August and September than for "Donald Trump" (Chart 2). Public appetite is so high that the Bitcoin Investment Trust, though officially an open-ended vehicle, has traded as high as twice its net asset value in recent months. Chart 1Bitcoin Prices: It's Been A Wild Ride So Far Chart 2President Trump: Bitcoin Is More Popular Than You! Other virtual currencies have also seen staggering returns. Ethereum is still up more than 3000% year-to-date, giving it a market cap of $23 billion. Dogecoin, a currency that was started "as a joke" according to its founders, commands a market cap of $114 million. Wider Effects? The run-up in bitcoin prices bears a close resemblance to classic bubbles (Chart 3). Yet, as the turn of the millennium dotcom boom and bust illustrates, wild swings in asset prices can sometimes mask important structural changes that new technologies have unleashed on the global economy. This raises the question of whether the explosion in virtual currencies is relevant for the broader investment community, including those investors who would never consider buying bitcoin. We would answer yes, albeit in a limited form thus far. The market capitalization of all virtual currencies currently stands at $120 billion (Chart 4). Globally, there is about $6 trillion in currency outstanding, so the value of virtual currencies is now 2% that of traditional cash and currency. That's not huge, but it's no longer trivial either. Chart 3Bitcoin Bubble? Chart 4Virtual Currencies: Market Cap Is Now Non-Trivial The importance of virtual currencies increases if we look at rates of change. The global stock of currency in circulation has risen by 5.5% over the past 12 months. However, if we add virtual currencies to the mix, the rate of growth jumps to 7%. The contribution of virtual currencies to the rate of growth of the broad money supply - which includes such items as bank deposits - is still fairly small. However, economists focus on currency in circulation for a reason: It is the largest component of base money (also known as "high-powered" money). The stock of base money helps determine the total money supply through the magic of the money multiplier and fractional reserve banking. The Monetary Hot Potato For the time being, the macro impact of virtual currencies has been constrained by the fact that most people are buying them as a store of value, rather than as a medium of exchange. It is no coincidence that up until recently, a disproportionately large amount of demand for virtual currencies has come out of China, an economy that suffers from a plethora of savings and a dearth of safe investable assets (Chart 5). In addition to squirrelling away their wealth in overpriced condos, the Chinese are now snapping up bitcoins. Chart 5Bitcoin Trading Volume By Top Three Currencies Over time, the public may begin to regard virtual currencies as legitimate substitutes for dollars, euros, yen, and yuan. This could lead people to want to hold fewer of these traditional currencies, causing them in turn to either spend their excess cash holdings or deposit them in commercial banks. The first outcome would obviously be inflationary, but so would the second if rising deposit inflows caused banks to increase lending. What would happen if people began transacting more in virtual currencies? At that point, the Fed and other central banks would need to decide whether to take some traditional paper money out of circulation in order to make room for the growing share of private virtual currencies. The merits of doing so would depend on the state of the business cycle.1 When inflation is low, as it is today in most of the world, central banks would gladly welcome anything that boosts spending and liquidity. Indeed, in some ways, the issuance of private currencies could have similar effects to helicopter drops of money. However, if inflation were to accelerate too rapidly, central banks would have to begin withdrawing their own currencies from circulation, or push for the withdrawal of private currencies. Governments Want Their Cut Chart 6U.S. Seigniorage Revenue The former outcome would not please the fiscal authorities. When the U.S. Treasury issues a $100 bill, it gains the ability to buy $100 of goods and services with it. The government's cost is whatever it pays to print the bill, which is close to zero. This so-called "seigniorage revenue" is quite large, averaging close to $70 billion per year for the U.S. government alone over the past decade (Chart 6). Why would the U.S. or any other country that issues its own currency want to part with this revenue? The answer is that it wouldn't. Instead, governments are likely to introduce their own competitors to bitcoin. The blockchain technology on which bitcoin is built is ingenious but completely within the public domain. Central banks are already thinking about how to issue their own virtual currencies. The creation of such parallel electronic currencies would allow people to send funds to one another and purchase goods and services without the need for an intermediary, a potentially negative development for banks and other financial institutions. These government-sponsored virtual currencies are unlikely to offer the full anonymity of bitcoin, but for most people, that may not be such a bad thing. As our Technology Sector Strategy service has emphasized, private virtual currencies suffer from numerous deficiencies which expose their users to fraud.2 When thieves stole 6% of all outstanding bitcoins from the Mt. Gox exchange in 2014, the victims had nothing to fall back on. A government-sponsored virtual currency could at least offer some protection to its holders, thereby making it more valuable to use. It would also allow central banks to fulfill their responsibilities as lenders of last resort. The Free Banking Era in the U.S., which at one point saw 8000 different currencies in circulation, experienced multiple banking crises. A world with myriad private currencies all competing with one another would be similarly unstable. Bitcoin: A Solution In Search Of A Problem? Chart 7The Boom In Cryptocurrencies This gets to a more fundamental issue, which is that bitcoin often comes across as a solution in need of a problem. People can already transfer money fairly easily when it is legal to do so. If the main practical advantage of bitcoin is to overcome capital controls and empower tax cheats, junkies, and hackers, it is hard to see how this does not beget a government crackdown. Ironically, the "mining" of additional bitcoins requires significant investment in specialized computers and dollops of electricity. Virtual currencies may exist in bits and bytes, but real resources must be expended to create them. In contrast, governments can create money with simply the stroke of a pen. Granted, if governments used this power to devalue the value of money - as they have periodically done from time to time - the virtues of bitcoin as a store of value would become more evident. The algorithms that power bitcoin limit the total number of coins that can ever be created to 21 million. Bitcoin is not the only game in town, however. Dozens of competitors have sprung up (Chart 7). While each may cap the number of coins in circulation, collectively they represent a potentially significant (and possibly unlimited) addition to the monetary base. Thus, it is not clear how well virtual currencies would perform as inflation hedges compared to more traditional instruments such as gold and land, let alone modern hedges such as inflation-linked securities. Investment Conclusions The role that money plays in modern economies is one of those things that people tend to tie themselves into pretzels thinking about. It's actually not that complicated. For the most part, inflation occurs when the demand for goods and services outstrips the supply of goods and services. Outside of extreme situations, the choice of monetary regime does not affect the supply-side of the economy (that's determined by productivity and the size of the labor force, neither of which central banks have much control over). Thus, it really is just a question of how the monetary regime affects aggregate demand. As noted above, there are reasons to think that the proliferation of virtual currencies will boost the demand for goods and services, either through the wealth effect channel (people who acquired bitcoin in its early days feel richer today), or via the currency substitution channel (if people start transacting in bitcoin, they may try to dispose of their excess dollars, euros, yen, and yuan either by spending them or depositing them in banks, leading to higher loan growth). Neither of these effects is terribly significant right now, but both have the potential to increase in importance over time. At some point, governments will take steps to rein in virtual currencies. However, until then, their existence is likely to spur inflation in the fiat currencies in which most prices are measured. That's bad for high-quality government bonds, but potentially good for stocks. The implications for gold are mixed. On the one hand, if the growth of virtual currencies translates into an increase in the global money supply and rising inflation, that is good for bullion. On the other hand, if people see bitcoin as a competitor to gold as a store of value, they may wish to hold less of the yellow metal. The dollar could lose out from the proliferation of virtual currencies if central banks allocate some of their USD reserves into these new currencies. However, it is doubtful this will happen to any significant degree since most central banks are likely to see virtual currencies as unwanted competitors to their own monies. In the meantime, stronger global demand growth could put disproportionately more upward pressure on U.S. inflation, given that the U.S. is closer to full employment than most economies. This could cause the Fed to raise rates more aggressively than it otherwise would, leading to a firmer dollar. Peter Berezin, Chief Global Strategist Global Investment Strategy peterb@bcaresearch.com 1 To appreciate this point, ponder the question of who suffers when someone goes shopping with counterfeit currency. If the economy is operating at full potential, the answer is that everyone else suffers because they have to pay higher prices for the things that they buy. However, if there are plenty of idle workers, the additional spending is unlikely to raise prices. Rather, it will translate into higher output and income. 2 Please see Technology Sector Strategy, "Blockchain and Cryptocurrencies," dated May 5, 2017. Strategy & Market Trends Tactical Trades Strategic Recommendations Closed Trades

Special Report

Dear Client, In addition to this Special Report written by my colleague Mark McClellan, we are sending you an abbreviated weekly report, which includes the Tactical Global Asset Allocation Monthly Update. Best regards, Peter Berezin, Chief Global Strategist Global Investment Strategy Highlights A "culture of profound cost reduction" has gripped the business sector since the GFC according to one school of thought, permanently changing the relationship between labor market slack and wages or inflation. If true, it could mean that central banks are almost powerless to reach their inflation targets. Amazon, Airbnb, Uber, robotics, contract workers, artificial intelligence, horizontal drilling and driverless cars are just a few examples of companies and technologies that are cutting costs and depressing prices and wages. In the first of our series on inflation, we will focus on the rise of e-commerce and the related "Amazonification" of the economy. In theory, positive supply shocks should not have more than a temporary impact on inflation if the price level is indeed a monetary phenomenon in the long term. But a series of positive supply shocks could make it appear for quite a while that low inflation is structural in nature. We are keeping an open mind and reserving judgement on the disinflationary impact of robotics, artificial intelligence and the gig economy until we do more research. But in terms of the impact of e-commerce, it is difficult to find supportive evidence at the macro level. The admittedly inadequate measures of online prices available today do not suggest that e-commerce sales are depressing the overall inflation rate by more than 0.1 or 0.2 percentage points. Moreover, it does not appear that the disinflationary impact of competition in the retail sector has intensified over the years. Today's creative destruction in retail may be no more deflationary than the shift to 'big box' stores in the 1990s. Perhaps lower online prices are forcing traditional retailers match the e-commerce vendors, allowing for a larger disinflationary effect than we estimate. However, the fact that retail margins are near secular highs outside of department stores argues against this thesis. The sectors potentially affected by e-commerce make up a small part of the CPI index. The deceleration of inflation since the GFC has been in areas unaffected by online sales. High profit margins for the overall corporate sector and depressed productivity growth also argue against the idea that e-commerce represents a large positive macro supply shock. Perhaps the main way that e-commerce is affecting the macro economy and financial markets is not through inflation, but via the reduction in the economy's capital spending requirement. This would reduce the equilibrium level of interest rates, since the Fed has to stimulate other parts of the economy to offset the loss of demand in capital spending in the retail sector. Feature Anecdotal evidence is all around us. The global economy is evolving and it seems that all of the major changes are deflationary. Amazon, Airbnb, Uber, robotics, contract workers, artificial intelligence, horizontal drilling and driverless cars are just a few examples of companies and technologies that are cutting costs and depressing prices and wages. Central banks in the major advanced economies are having difficulty meeting their inflation targets, even in the U.S. where the labor market is tight by historical standards. Based on the depressed level of bond yields, it appears that the majority of investors believe that inflation headwinds will remain formidable for a long time. One school of thought is that low inflation reflects a lack of demand growth in the post-Great Financial Crisis (GFC) period. Another school points to the supply side of the economy. A recent report by Prudential Financial highlights "...obvious examples of ... new business models and new organizational structures, whereby higher-cost traditional methods of production, transportation, and distribution are displaced by more nontraditional cost-effective ways of conducting business." 1 A "culture of profound cost reduction" has gripped the business sector since the GFC according to this school, permanently changing the relationship between labor market slack and wages or inflation (i.e., the Phillips Curve). Employees are less aggressive in their wage demands in a world where robots are threatening humans in a broadening array of industrial categories. Many feel lucky just to have a job. In a highly sensationalized article called "How The Internet Economy Killed Inflation," Forbes argued that "the internet has reduced many of the traditional barriers to entry that protect companies from competition and created a race to the bottom for prices in a number of categories." Forbes believes that new technologies are placing downward pressure on inflation by depressing wages, increasing productivity and encouraging competition. There are many factors that have the potential to weigh on prices, but analysts are mainly focusing on e-commerce, robotics, artificial intelligence, and the gig economy. In the first of our series on inflation, we will focus on the rise of e-commerce and the related "Amazonification" of the economy. The latter refers to the advent of new business models that cut out layers of middlemen between producers and consumers. Amazonification E-commerce has grown at a compound annual rate of more than 9% over the past 15 years, and now accounts for about 8½% of total U.S. retail sales (Chart 1). Amazon has been leading the charge, accounting for 43% of all online sales in 2016 (Chart 2). Amazon's business model not only cuts costs by eliminating middlemen and (until recently) avoiding expensive showrooms, but it also provides a platform for improved price discovery on an extremely broad array of goods. In 2013, Amazon carried 230 million items for sale in the United States, nearly 30 times the number sold by Walmart, one of the largest retailers in the world. Chart 1E-Commerce: Steady Increase In Market Share Chart 2Amazon Dominates With the use of a smartphone, consumers can check the price of an item on Amazon while shopping in a physical store. Studies show that it does not require a large price gap for shoppers to buy online rather than in-store. Amazon appears to be impacting other retailers' ability to pass though cost increases, leading to a rash of retail outlet closings. Sears alone announced the closure of 300 retail outlets this year. The devastation that Amazon inflicted on the book industry is well known. It is no wonder then, that Amazon's purchase of Whole Foods Market, a grocery chain, sent shivers down the spines of CEOs not only in the food industry, but in the broader retail industry as well. What would prevent Amazon from applying its model to furniture and appliances, electronics or drugstores? It seems that no retail space is safe. A Little Theory Before we turn to the evidence, let's review the macro theory related to positive supply shocks. The internet could be lowering prices by moving product markets toward the "perfect competition" model. The internet trims search costs, improves price transparency and reduces barriers to entry. The internet also allows for shorter supply chains, as layers of wholesalers and other intermediaries are removed and e-commerce companies allow more direct contact between consumers and producers. Fewer inventories and a smaller "brick and mortar" infrastructure take additional costs out of the system. Economic theory suggests that the result of this positive supply shock will be greater product market competition, increased productivity and reduced profitability. In the long run, workers should benefit from the productivity boost via real wage gains (even if nominal wage growth is lackluster). Workers may lower their reservation wage if they feel that increased competitive pressures or technology threaten their jobs. The internet is also likely to improve job matching between the unemployed and available vacancies, which should lead to a fall in the full-employment level of unemployment (NAIRU). Nonetheless, the internet should not have a permanent impact on inflation. The lower level of NAIRU and the direct effects of the internet on consumer prices discussed above allow inflation to fall below the central bank's target. The bank responds by lowering interest rates, stimulating demand and thereby driving unemployment down to the new lower level of NAIRU. Over time, inflation will drift back up toward target. In other words, a greater degree of the competition should boost the supply side of the economy and lower NAIRU, but it should not result in a permanently lower rate of inflation if inflation is indeed a monetary phenomenon and central banks strive to meet their targets. Still, one could imagine a series of supply shocks that are spread out over time, with each having a temporary negative impact on prices such that it appears for a while that inflation has been permanently depressed. This could be an accurate description of the current situation in the U.S. and some of the other major countries. We have sympathy for the view that the internet and new business models are increasing competition, cutting costs and thereby limiting price increases in some areas. But is there any hard evidence? Is the competitive effect that large, and is it any more intense than in the past? There are a number of reasons to be skeptical because most of the evidence does not support Forbes' claim that the internet has killed inflation. 1. E-commerce affects only a small part of the Consumer Price Index As mentioned above, online shopping for goods represents 8.5% of total retail sales in the U.S. E-commerce is concentrated in four kinds of businesses (Table 1): Furniture & Home Furnishings (7% of total retail sales), Electronics & Appliances (20%), Health & Personal Care (15%), and Clothing (10%). Since goods make up 40% of the CPI, then 3.2% (8% times 40%) is a ballpark estimate for the size of goods e-commerce in the CPI. Table 1E-Commerce Market Share Of Goods Sector Table 2 shows the relative size of e-commerce in the service sector. The analysis is complicated by the fact that the data on services includes B-to-B sales in addition to B-to-C.2 However, e-commerce represents almost 4% of total sales for the service categories tracked by the BLS. Services make up 60% of the CPI, but the size drops to 26% if we exclude shelter (which is probably not affected by online shopping). Thus, e-commerce in the service sector likely affects 1% (3.9% times 26%) of the CPI. Table 2E-Commerce Market Share Of Service Sector Adding goods and services, online shopping affects about 4.2% of the CPI index at most. The bottom line is that the relatively small size of e-commerce at the consumer level limits any estimate of the impact of online sales on the broad inflation rate. 2. Most of the deceleration in inflation since 2007 has been in areas unaffected by e-commerce Table 3 compares the average contribution to annual average CPI inflation during 2000-2007 with that of 2007-2016. Average annual inflation fell from 2.9% in the seven years before the Great Recession to 1.8% after, for a total decline of just over 1 percentage point. The deceleration is almost fully explained by Energy, Food and Owners' Equivalent Rent. The bottom part of Table 3 highlights that the sectors with the greatest exposure to e-commerce had a negligible impact on the inflation slowdown. Table 3Comparison Of Pre- And Post-Lehman Inflation Rates 3. The cost advantages for online sellers are overstated Bain & Company, a U.S. consultancy, argues that e-commerce will not grow in importance indefinitely and come to dominate consumer spending.3 E-commerce sales are already slowing. Market share is following a classic S-shaped curve that, Bain estimates, will top out at under 30% by 2030. First, not everyone wants to buy everything online. Products that are well known to consumers and purchased on a regular basis are well suited to online shopping. But for many other products, consumers need to see and feel the product in person before making a purchase. Second, the cost savings of online selling versus traditional brick and mortar stores is not as great as many believe. Bain claims that many e-commerce businesses struggle to make a profit. The information technology, distribution centers, shipping, and returns processing required by e-commerce companies can cost as much as running physical stores in some cases. E-tailers often cannot ship directly from manufacturers to consumers; they need large and expensive fulfillment centers and a very generous returns policy. Moreover, online and offline sales models are becoming blurred. Retailers with physical stores are growing their e-commerce operations, while previously pure e-commerce plays are adding stores or negotiating space in other retailers' stores. Even Amazon now has storefronts. The shift toward an "multichannel" selling model underscores that there are benefits to traditional brick-and-mortar stores that will ensure that they will not completely disappear. 4. E-commerce is not the first revolution in the retail sector The retail sector has changed significantly over the decades and it is not clear that the disinflationary effect of the latest revolution, e-commerce, is any more intense than in the past. Economists at Goldman Sachs point out that the growth of Amazon's market share in recent years still lags that of Walmart and other "big box" stores in the 1990s (Chart 3).4 This fact suggests that "Amazonification" may not be as disinflationary as the previous big-box revolution. 5. Weak productivity growth and high profit margins are inconsistent with a large supply-side benefit from e-commerce As discussed above, economic theory suggests that a positive supply shock that cuts costs and boosts competition should trim profit margins and lift productivity. The problem is that the margins and productivity have moved in the opposite direction that economic theory would suggest (Chart 4). Chart 3Comparison Of Pre- And Post-Lehman Inflation Rates Chart 4Incompatible With A Supply Shock By definition, productivity rises when firms can produce the same output with fewer or cheaper inputs. However, it is well documented that productivity growth has been in a downtrend since the 1990s, and has been dismally low since the Great Recession. A Special Report from BCA's Global Investment Strategy 5 service makes a convincing case that mismeasurement is not behind the low productivity figures. In fact, in many industries it appears that productivity is over-estimated. If e-commerce is big enough to "move the dial" on overall inflation, it should be big enough to see in the aggregate productivity figures. Chart 5Retail Margin Squeeze Only In Department Stores One would also expect to see a margin squeeze across industries if e-commerce is indeed generating a lot of deflationary competitive pressure. Despite dismally depressed productivity, however, corporate profit margins are at the high end of the historical range across most of the sectors of the S&P 500. This is the case even in the retailing sector outside of department stores (Chart 5). These facts argue against the idea that the internet has moved the economy further toward a disinflationary "perfect competition" model. 6. Online price setting is characterized by frictions comparable to traditional retail We would expect to observe a low price dispersion across online vendors since the internet has apparently lowered the cost of monitoring competitors' prices and the cost of searching for the lowest price. We would also expect to see fairly synchronized price adjustments; if one vendor adjusts its price due to changing market conditions, then the rest should quickly follow to avoid suffering a massive loss of market share. However, a recent study of price-setting practices in the U.S. and U.K. found that this is not the case.6 The dataset covered a broad spectrum of consumer goods and sellers over a two-year period, comparing online with offline prices. The researchers found that market pricing "frictions" are surprisingly elevated in the online world. Price dispersion is high in absolute terms and on par with offline pricing. Academics for years have puzzled over high price rigidities and dispersion in retail stores in the context of an apparently stiff competitive environment, and it appears that online pricing is not much better. The study did not cover a long enough period to see if frictions were even worse in the past. Nonetheless, the evidence available suggests that the lower cost of monitoring prices afforded by the internet has not led to significant price convergence across sellers online or offline. Another study compared online and offline prices for multichannel retailers, using the massive database provided by the Billion Prices Project at MIT.7 The database covers prices across 10 countries. The study found that retailers charged the same price online as in-store in 72% of cases. The average discount was 4% for those cases in which there was a markdown online. If the observations with identical prices are included, the average online/offline price difference was just 1%. 7. Some measures of online prices have grown at about the same pace as the CPI index The U.S. Bureau of Labor Statistics does include online sales when constructing the Consumer Price Index. It even includes peer-to-peer sales by companies such as Airbnb and Uber. However, the BLS admits that its sample lags the popularity of such services by a few years. Moreover, while the BLS is trying to capture the rising proportion of sales done via e-commerce, "outlet bias" means that the CPI does not capture the price effect in cases where consumers are finding cheaper prices online. This is because the BLS weights the growth rate of online and offline prices, not the price levels. While there may be level differences, there is no reason to believe that the inflation rates for similar goods sold online and offline differ significantly. If the inflation rates are close, then the growing share of online sales will not affect overall inflation based on the BLS methodology. The BLS argues that any bias in the CPI due to outlet bias is mitigated to the extent that physical stores offer a higher level of service. Thus, price differences may not be that great after quality-adjustment. All this suggests that the actual consumer price inflation rate could be somewhat lower than the official rate. Nonetheless, it does not necessarily mean that inflation, properly measured, is being depressed by e-commerce to a meaningful extent. Indeed, Chart 6 highlights that the U.S. component of the Billion Prices Index rose at a faster pace than the overall CPI between 2009 and 2014. The Online Price Index fell in absolute and relative terms from 2014 to mid-2016, but rose sharply toward the end of 2016. Applying our guesstimate of the weight of e-commerce in the CPI (3.2% for goods), online price inflation added to overall annual CPI inflation by about 0.3 percentage points in 2016 (bottom panel of Chart 6). There is more deflation evident in the BLS' index of prices for Electronic Shopping and Mail Order Houses (Chart 7). Online prices fell relative to the overall CPI for most of the time since the early 1990s, with the relative price decline accelerating since the GFC. However, our estimate of the contribution to overall annual CPI inflation is only about -0.15 percentage points in June 2017, and has never been more than -0.3 percentage points. This could be an underestimate because it does not include the impact of services, although the service e-commerce share of the CPI is very small. Chart 6Online Price Index Chart 7Electronic Shopping Price Index Another way to approach this question is to focus on the parts of the CPI that are most exposed to e-commerce. It is impossible to separate the effect of e-commerce on inflation from other drivers of productivity. Nonetheless, if online shopping is having a significant deflationary impact on overall inflation, we should see large and persistent negative contributions from these parts of the CPI. We combined the components of the CPI that most closely matched the sectors that have high e-commerce exposure according to the BLS' annual Retail Survey (Chart 8). The sectors in our aggregate e-commerce price proxy include hotels/motels, taxicabs, books & magazines, clothing, computer hardware, drugs, health & beauty aids, electronics & appliances, alcoholic beverages, furniture & home furnishings, sporting goods, air transportation, travel arrangement and reservation services, educational services and other merchandise. The sectors are weighted based on their respective weights in the CPI. Our e-commerce price proxy has generally fallen relative to the overall CPI index since 2000. However, while the average contribution of these sectors to the overall annual CPI inflation rate has fallen in the post GFC period relative to the 2000-2007 period, the average difference is only 0.2 percentage points. The contribution has hovered around the zero mark for the past 2½ years. Surprisingly, price indexes have increased by more than the overall CPI since 2000 in some sectors where one would have expected to see significant relative price deflation, such as taxis, hotels, travel arrangement and even books. One could argue that significant measurement error must be a factor. How could the price of books have gone up faster than the CPI? Sectors displaying the most relative price declines are clothing, computers, electronics, furniture, sporting goods, air travel and other goods. We recalculated our e-commerce proxy using only these deflating sectors, but we boosted their weights such that the overall weight of the proxy in the CPI is kept the same as our full e-commerce proxy discussed above. In other words, this approach implicitly assumes that the excluded sectors (taxis, books, hotels and travel arrangement) actually deflated at the average pace of the sectors that remain in the index. Our adjusted e-commerce proxy suggests that online pricing reduced overall CPI inflation by about 0.1-to-0.2 percentage points in recent years (Chart 9). This contribution is below the long-term average of the series, but the drag was even greater several times in the past. Chart 8BCA E-Commerce Proxy Price Index Chart 9BCA E-Commerce Adjusted Proxy Price Index Admittedly, data limitations mean that all of the above estimates of the impact of e-commerce are ballpark figures. Conclusions We are keeping an open mind and reserving judgement on the disinflationary impact of robotics, artificial intelligence and the gig economy until we do more research. But in terms of the impact of e-commerce, it is difficult to find supportive evidence. The available data are admittedly far from ideal for confirming or disproving the "Amazonification" thesis. Perhaps better measures of e-commerce pricing will emerge in the future. Nonetheless, the measures available today do not suggest that online sales are depressing the overall inflation rate by more than 0.1 or 0.2 percentage points, and it does not appear that the disinflationary impact has intensified by much. One could argue that lower online prices are forcing traditional retailers to match the e-commerce vendors, allowing for a larger disinflationary effect than we estimate. Nonetheless, if this were the case, then we would expect to see significant margin compression in the retail sector. The sectors potentially affected by e-commerce make up a small part of the CPI index. The deceleration of inflation since the GFC has been in areas unaffected by online sales. High corporate profit margins and depressed productivity growth also argue against the idea that e-commerce represents a large positive macro supply shock. Finally, today's creative destruction in retail may be no more deflationary than the shift to 'big box' stores in the 1990s. Perhaps the main way that e-commerce is affecting the macro economy and financial markets is not through inflation, but via the reduction in the economy's capital spending requirement. Rising online activity means that we need fewer shopping malls and big box outlets to support a given level of consumer spending. This would reduce the equilibrium level of interest rates, since the Fed has to stimulate other parts of the economy to offset the loss of demand in capital spending in the retail sector. To the extent that central banks were slow to recognize that equilibrium rates had fallen to extremely low levels, then policy was behind the curve and this might have contributed to the current low inflation environment. Mark McClellan, Senior Vice President The Bank Credit Analyst markm@bcaresearch.com 1 Robert F. DeLucia, "Economic Perspective: A Nontraditional Analysis of Inflation," Prudential Capital Group (August 21, 2017). 2 Business to business, and business to consumer. 3 Aaron Cheris, Darrell Rigby and Suzanne Tager, "The Power Of Omnichannel Stores," Bain & Company Insights: Retail Holiday Newsletter 2016-2017 (December 19, 2016) 4 "US Daily: The Internet and Inflation: How Big is the Amazon Effect?" Goldman Sachs Economic Research (August 2, 2017). 5 Please see Global Investment Strategy Weekly Report, "Weak Productivity Growth: Don't Blame the Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 6 Yuriy Gorodnichenko, Viacheslav Sheremirov, and Oleksandr Talavera, "Price Setting In Online Markets: Does IT Click?" Journal of the European Economic Association (July 2016). 7 Alberto Cavallo, "Are Online and Offline Prices Similar? Evidence from Large Multi-Channel Retailers," NBER Working Paper No. 22142 (March 2016).

Special Report

A "culture of profound cost reduction" has gripped the business sector since the GFC according to one school of thought, permanently changing the relationship between labor market slack and wages or inflation. If true, it could mean that central banks are almost powerless to reach their inflation targets. Amazon, Airbnb, Uber, robotics, contract workers, artificial intelligence, horizontal drilling and driverless cars are just a few examples of companies and technologies that are cutting costs and depressing prices and wages. In the first of our series on inflation, we will focus on the rise of e-commerce and the related "Amazonification" of the economy. In theory, positive supply shocks should not have more than a temporary impact on inflation if the price level is indeed a monetary phenomenon in the long term. But a series of positive supply shocks could make it appear for quite a while that low inflation is structural in nature. We are keeping an open mind and reserving judgement on the disinflationary impact of robotics, artificial intelligence and the gig economy until we do more research. But in terms of the impact of e-commerce, it is difficult to find supportive evidence at the macro level. The admittedly inadequate measures of online prices available today do not suggest that e-commerce sales are depressing the overall inflation rate by more than 0.1 or 0.2 percentage points. Moreover, it does not appear that the disinflationary impact of competition in the retail sector has intensified over the years. Today's creative destruction in retail may be no more deflationary than the shift to 'big box' stores in the 1990s. Perhaps lower online prices are forcing traditional retailers to match the e-commerce vendors, allowing for a larger disinflationary effect than we estimate. However, the fact that retail margins are near secular highs outside of department stores argues against this thesis. The sectors potentially affected by e-commerce make up a small part of the CPI index. The deceleration of inflation since the GFC has been in areas unaffected by online sales. High profit margins for the overall corporate sector and depressed productivity growth also argue against the idea that e-commerce represents a large positive macro supply shock. Perhaps the main way that e-commerce is affecting the macro economy and financial markets is not through inflation, but via the reduction in the economy's capital spending requirement. This would reduce the equilibrium level of interest rates, since the Fed has to stimulate other parts of the economy to offset the loss of demand in capital spending in the retail sector. Anecdotal evidence is all around us. The global economy is evolving and it seems that all of the major changes are deflationary. Amazon, Airbnb, Uber, robotics, contract workers, artificial intelligence, horizontal drilling and driverless cars are just a few examples of companies and technologies that are cutting costs and depressing prices and wages. Central banks in the major advanced economies are having difficulty meeting their inflation targets, even in the U.S. where the labor market is tight by historical standards. Based on the depressed level of bond yields, it appears that the majority of investors believe that inflation headwinds will remain formidable for a long time. One school of thought is that low inflation reflects a lack of demand growth in the post-Great Financial Crisis (GFC) period. Another school points to the supply side of the economy. A recent report by Prudential Financial highlights "...obvious examples of ... new business models and new organizational structures, whereby higher-cost traditional methods of production, transportation, and distribution are displaced by more nontraditional cost-effective ways of conducting business."1 A "culture of profound cost reduction" has gripped the business sector since the GFC according to this school, permanently changing the relationship between labor market slack and wages or inflation (i.e., the Phillips Curve). Employees are less aggressive in their wage demands in a world where robots are threatening humans in a broadening array of industrial categories. Many feel lucky just to have a job. In a highly sensationalized article called "How The Internet Economy Killed Inflation," Forbes argued that "the internet has reduced many of the traditional barriers to entry that protect companies from competition and created a race to the bottom for prices in a number of categories." Forbes believes that new technologies are placing downward pressure on inflation by depressing wages, increasing productivity and encouraging competition. There are many factors that have the potential to weigh on prices, but analysts are mainly focusing on e-commerce, robotics, artificial intelligence, and the gig economy. In the first of our series on inflation, we will focus on the rise of e-commerce and the related "Amazonification" of the economy. The latter refers to the advent of new business models that cut out layers of middlemen between producers and consumers. Amazonification E-commerce has grown at a compound annual rate of more than 9% over the past 15 years, and now accounts for about 8½% of total U.S. retail sales (Chart II-1). Amazon has been leading the charge, accounting for 43% of all online sales in 2016 (Chart II-2). Amazon's business model not only cuts costs by eliminating middlemen and (until recently) avoiding expensive showrooms, but it also provides a platform for improved price discovery on an extremely broad array of goods. In 2013, Amazon carried 230 million items for sale in the United States, nearly 30 times the number sold by Walmart, one of the largest retailers in the world. Chart II-1E-Commerce: Steady Increase In Market Share Chart II-2Amazon Dominates With the use of a smartphone, consumers can check the price of an item on Amazon while shopping in a physical store. Studies show that it does not require a large price gap for shoppers to buy online rather than in-store. Amazon appears to be impacting other retailers' ability to pass though cost increases, leading to a rash of retail outlet closings. Sears alone announced the closure of 300 retail outlets this year. The devastation that Amazon inflicted on the book industry is well known. It is no wonder then, that Amazon's purchase of Whole Foods Market, a grocery chain, sent shivers down the spines of CEOs not only in the food industry, but in the broader retail industry as well. What would prevent Amazon from applying its model to furniture and appliances, electronics or drugstores? It seems that no retail space is safe. A Little Theory Before we turn to the evidence, let's review the macro theory related to positive supply shocks. The internet could be lowering prices by moving product markets toward the "perfect competition" model. The internet trims search costs, improves price transparency and reduces barriers to entry. The internet also allows for shorter supply chains, as layers of wholesalers and other intermediaries are removed and e-commerce companies allow more direct contact between consumers and producers. Fewer inventories and a smaller "brick and mortar" infrastructure take additional costs out of the system. Economic theory suggests that the result of this positive supply shock will be greater product market competition, increased productivity and reduced profitability. In the long run, workers should benefit from the productivity boost via real wage gains (even if nominal wage growth is lackluster). Workers may lower their reservation wage if they feel that increased competitive pressures or technology threaten their jobs. The internet is also likely to improve job matching between the unemployed and available vacancies, which should lead to a fall in the full-employment level of unemployment (NAIRU). Nonetheless, the internet should not have a permanent impact on inflation. The lower level of NAIRU and the direct effects of the internet on consumer prices discussed above allow inflation to fall below the central bank's target. The bank responds by lowering interest rates, stimulating demand and thereby driving unemployment down to the new lower level of NAIRU. Over time, inflation will drift back up toward target. In other words, a greater degree of the competition should boost the supply side of the economy and lower NAIRU, but it should not result in a permanently lower rate of inflation if inflation is indeed a monetary phenomenon and central banks strive to meet their targets. Still, one could imagine a series of supply shocks that are spread out over time, with each having a temporary negative impact on prices such that it appears for a while that inflation has been permanently depressed. This could be an accurate description of the current situation in the U.S. and some of the other major countries. We have sympathy for the view that the internet and new business models are increasing competition, cutting costs and thereby limiting price increases in some areas. But is there any hard evidence? Is the competitive effect that large, and is it any more intense than in the past? There are a number of reasons to be skeptical because most of the evidence does not support Forbes' claim that the internet has killed inflation. (1) E-commerce affects only a small part of the Consumer Price Index As mentioned above, online shopping for goods represents 8.5% of total retail sales in the U.S. E-commerce is concentrated in four kinds of businesses (Table II-1): Furniture & Home Furnishings (7% of total retail sales), Electronics & Appliances (20%), Health & Personal Care (15%), and Clothing (10%). Since goods make up 40% of the CPI, then 3.2% (8% times 40%) is a ballpark estimate for the size of goods e-commerce in the CPI. Table II-1E-Commerce Market Share Of Goods Sector (2015) Table II-2 shows the relative size of e-commerce in the service sector. The analysis is complicated by the fact that the data on services includes B-to-B sales in addition to B-to-C.2 However, e-commerce represents almost 4% of total sales for the service categories tracked by the BLS. Services make up 60% of the CPI, but the size drops to 26% if we exclude shelter (which is probably not affected by online shopping). Thus, e-commerce in the service sector likely affects 1% (3.9% times 26%) of the CPI. Table II-2E-Commerce Market Share Of Service Sector (2015) Adding goods and services, online shopping affects about 4.2% of the CPI index at most. The bottom line is that the relatively small size of e-commerce at the consumer level limits any estimate of the impact of online sales on the broad inflation rate. (2) Most of the deceleration in inflation since 2007 has been in areas unaffected by e-commerce Table II-3 compares the average contribution to annual average CPI inflation during 2000-2007 with that of 2007-2016. Average annual inflation fell from 2.9% in the seven years before the Great Recession to 1.8% after, for a total decline of just over 1 percentage point. The deceleration is almost fully explained by Energy, Food and Owners' Equivalent Rent. The bottom part of Table II-3 highlights that the sectors with the greatest exposure to e-commerce had a negligible impact on the inflation slowdown. Table II-3Comparison Of Pre- and Post-Lehman Inflation Rates (3) The cost advantages for online sellers are overstated Bain & Company, a U.S. consultancy, argues that e-commerce will not grow in importance indefinitely and come to dominate consumer spending.3 E-commerce sales are already slowing. Market share is following a classic S-shaped curve that, Bain estimates, will top out at under 30% by 2030. First, not everyone wants to buy everything online. Products that are well known to consumers and purchased on a regular basis are well suited to online shopping. But for many other products, consumers need to see and feel the product in person before making a purchase. Second, the cost savings of online selling versus traditional brick and mortar stores is not as great as many believe. Bain claims that many e-commerce businesses struggle to make a profit. The information technology, distribution centers, shipping, and returns processing required by e-commerce companies can cost as much as running physical stores in some cases. E-tailers often cannot ship directly from manufacturers to consumers; they need large and expensive fulfillment centers and a very generous returns policy. Moreover, online and offline sales models are becoming blurred. Retailers with physical stores are growing their e-commerce operations, while previously pure e-commerce plays are adding stores or negotiating space in other retailers' stores. Even Amazon now has storefronts. The shift toward an "multichannel" selling model underscores that there are benefits to traditional brick-and-mortar stores that will ensure that they will not completely disappear. (4) E-commerce is not the first revolution in the retail sector The retail sector has changed significantly over the decades and it is not clear that the disinflationary effect of the latest revolution, e-commerce, is any more intense than in the past. Economists at Goldman Sachs point out that the growth of Amazon's market share in recent years still lags that of Walmart and other "big box" stores in the 1990s (Chart II-3).4 This fact suggests that "Amazonification" may not be as disinflationary as the previous big-box revolution. (5) Weak productivity growth and high profit margins are inconsistent with a large supply-side benefit from e-commerce As discussed above, economic theory suggests that a positive supply shock that cuts costs and boosts competition should trim profit margins and lift productivity. The problem is that the margins and productivity have moved in the opposite direction that economic theory would suggest (Chart II-4). Chart II-3Amazon Vs. Walmart: ##br##Who's More Deflationary? Chart II-4Incompatible With A Supply Shock By definition, productivity rises when firms can produce the same output with fewer or cheaper inputs. However, it is well documented that productivity growth has been in a downtrend since the 1990s, and has been dismally low since the Great Recession. A Special Report from BCA's Global Investment Strategy5 service makes a convincing case that mismeasurement is not behind the low productivity figures. In fact, in many industries it appears that productivity is over-estimated. If e-commerce is big enough to "move the dial" on overall inflation, it should be big enough to see in the aggregate productivity figures. Chart II-5Retail Margin Squeeze ##br##Only In Department Stores One would also expect to see a margin squeeze across industries if e-commerce is indeed generating a lot of deflationary competitive pressure. Despite dismally depressed productivity, however, corporate profit margins are at the high end of the historical range across most of the sectors of the S&P 500. This is the case even in the retailing sector outside of department stores (Chart II-5). These facts argue against the idea that the internet has moved the economy further toward a disinflationary "perfect competition" model. (6) Online price setting is characterized by frictions comparable to traditional retail We would expect to observe a low price dispersion across online vendors since the internet has apparently lowered the cost of monitoring competitors' prices and the cost of searching for the lowest price. We would also expect to see fairly synchronized price adjustments; if one vendor adjusts its price due to changing market conditions, then the rest should quickly follow to avoid suffering a massive loss of market share. However, a recent study of price-setting practices in the U.S. and U.K. found that this is not the case.6 The dataset covered a broad spectrum of consumer goods and sellers over a two-year period, comparing online with offline prices. The researchers found that market pricing "frictions" are surprisingly elevated in the online world. Price dispersion is high in absolute terms and on par with offline pricing. Academics for years have puzzled over high price rigidities and dispersion in retail stores in the context of an apparently stiff competitive environment, and it appears that online pricing is not much better. The study did not cover a long enough period to see if frictions were even worse in the past. Nonetheless, the evidence available suggests that the lower cost of monitoring prices afforded by the internet has not led to significant price convergence across sellers online or offline. Another study compared online and offline prices for multichannel retailers, using the massive database provided by the Billion Prices Project at MIT.7 The database covers prices across 10 countries. The study found that retailers charged the same price online as in-store in 72% of cases. The average discount was 4% for those cases in which there was a markdown online. If the observations with identical prices are included, the average online/offline price difference was just 1%. (7) Some measures of online prices have grown at about the same pace as the CPI index The U.S. Bureau of Labor Statistics does include online sales when constructing the Consumer Price Index. It even includes peer-to-peer sales by companies such as Airbnb and Uber. However, the BLS admits that its sample lags the popularity of such services by a few years. Moreover, while the BLS is trying to capture the rising proportion of sales done via e-commerce, "outlet bias" means that the CPI does not capture the price effect in cases where consumers are finding cheaper prices online. This is because the BLS weights the growth rate of online and offline prices, not the price levels. While there may be level differences, there is no reason to believe that the inflation rates for similar goods sold online and offline differ significantly. If the inflation rates are close, then the growing share of online sales will not affect overall inflation based on the BLS methodology. The BLS argues that any bias in the CPI due to outlet bias is mitigated to the extent that physical stores offer a higher level of service. Thus, price differences may not be that great after quality-adjustment. All this suggests that the actual consumer price inflation rate could be somewhat lower than the official rate. Nonetheless, it does not necessarily mean that inflation, properly measured, is being depressed by e-commerce to a meaningful extent. Indeed, Chart II-6 highlights that the U.S. component of the Billion Prices Index rose at a faster pace than the overall CPI between 2009 and 2014. The Online Price Index fell in absolute and relative terms from 2014 to mid-2016, but rose sharply toward the end of 2016. Applying our guesstimate of the weight of e-commerce in the CPI (3.2% for goods), online price inflation added to overall annual CPI inflation by about 0.3 percentage points in 2016 (bottom panel of Chart II-6). There is more deflation evident in the BLS' index of prices for Electronic Shopping and Mail Order Houses (Chart II-7). Online prices fell relative to the overall CPI for most of the time since the early 1990s, with the relative price decline accelerating since the GFC. However, our estimate of the contribution to overall annual CPI inflation is only about -0.15 percentage points in June 2017, and has never been more than -0.3 percentage points. This could be an underestimate because it does not include the impact of services, although the service e-commerce share of the CPI is very small. Chart II-6Online Price Index Chart II-7Electronic Shopping Price Index Another way to approach this question is to focus on the parts of the CPI that are most exposed to e-commerce. It is impossible to separate the effect of e-commerce on inflation from other drivers of productivity. Nonetheless, if online shopping is having a significant deflationary impact on overall inflation, we should see large and persistent negative contributions from these parts of the CPI. We combined the components of the CPI that most closely matched the sectors that have high e-commerce exposure according to the BLS' annual Retail Survey (Chart II-8). The sectors in our aggregate e-commerce price proxy include hotels/motels, taxicabs, books & magazines, clothing, computer hardware, drugs, health & beauty aids, electronics & appliances, alcoholic beverages, furniture & home furnishings, sporting goods, air transportation, travel arrangement and reservation services, educational services and other merchandise. The sectors are weighted based on their respective weights in the CPI. Our e-commerce price proxy has generally fallen relative to the overall CPI index since 2000. However, while the average contribution of these sectors to the overall annual CPI inflation rate has fallen in the post GFC period relative to the 2000-2007 period, the average difference is only 0.2 percentage points. The contribution has hovered around the zero mark for the past 2½ years. Surprisingly, price indexes have increased by more than the overall CPI since 2000 in some sectors where one would have expected to see significant relative price deflation, such as taxis, hotels, travel arrangement and even books. One could argue that significant measurement error must be a factor. How could the price of books have gone up faster than the CPI? Sectors displaying the most relative price declines are clothing, computers, electronics, furniture, sporting goods, air travel and other goods. We recalculated our e-commerce proxy using only these deflating sectors, but we boosted their weights such that the overall weight of the proxy in the CPI is kept the same as our full e-commerce proxy discussed above. In other words, this approach implicitly assumes that the excluded sectors (taxis, books, hotels and travel arrangement) actually deflated at the average pace of the sectors that remain in the index. Our adjusted e-commerce proxy suggests that online pricing reduced overall CPI inflation by about 0.1-to-0.2 percentage points in recent years (Chart II-9). This contribution is below the long-term average of the series, but the drag was even greater several times in the past. Chart II-8BCA E-Commerce Proxy Price Index Chart II-9BCA E-Commerce Adjusted Proxy Price Index Admittedly, data limitations mean that all of the above estimates of the impact of e-commerce are ballpark figures. Conclusions We are keeping an open mind and reserving judgement on the disinflationary impact of robotics, artificial intelligence and the gig economy until we do more research. But in terms of the impact of e-commerce, it is difficult to find supportive evidence. The available data are admittedly far from ideal for confirming or disproving the "Amazonification" thesis. Perhaps better measures of e-commerce pricing will emerge in the future. Nonetheless, the measures available today do not suggest that online sales are depressing the overall inflation rate by more than 0.1 or 0.2 percentage points, and it does not appear that the disinflationary impact has intensified by much. One could argue that lower online prices are forcing traditional retailers to match the e-commerce vendors, allowing for a larger disinflationary effect than we estimate. Nonetheless, if this were the case, then we would expect to see significant margin compression in the retail sector. The sectors potentially affected by e-commerce make up a small part of the CPI index. The deceleration of inflation since the GFC has been in areas unaffected by online sales. High corporate profit margins and depressed productivity growth also argue against the idea that e-commerce represents a large positive macro supply shock. Finally, today's creative destruction in retail may be no more deflationary than the shift to 'big box' stores in the 1990s. Perhaps the main way that e-commerce is affecting the macro economy and financial markets is not through inflation, but via the reduction in the economy's capital spending requirement. Rising online activity means that we need fewer shopping malls and big box outlets to support a given level of consumer spending. This would reduce the equilibrium level of interest rates, since the Fed has to stimulate other parts of the economy to offset the loss of demand in capital spending in the retail sector. To the extent that central banks were slow to recognize that equilibrium rates had fallen to extremely low levels, then policy was behind the curve and this might have contributed to the current low inflation environment. Mark McClellan Senior Vice President The Bank Credit Analyst 1 Robert F. DeLucia, "Economic Perspective: A Nontraditional Analysis Of Inflation," Prudential Capital Group (August 21, 2017). 2 Business to business, and business to consumer. 3 Aaron Cheris, Darrell Rigby and Suzanne Tager, "The Power Of Omnichannel Stores," Bain & Company Insights: Retail Holiday Newsletter 2016-2017 (December 19, 2016). 4 "US Daily: The Internet And Inflation: How Big Is The Amazon Effect?" Goldman Sachs Economic Research (August 2, 2017). 5 Please see Global Investment Strategy Weekly Report, "Weak Productivity Growth: Don't Blame The Statisticians," dated March 25, 2016, available at gis.bcaresearch.com 6 Yuriy Gorodnichenko, Viacheslav Sheremirov, and Oleksandr Talavera, "Price Setting In Online Markets: Does IT Click?" Journal of the European Economic Association (July 2016). 7 Alberto Cavallo, "Are Online And Offline Prices Similar? Evidence From Large Multi-Channel Retailers," NBER Working Paper No. 22142 (March 2016).

Special Report

Feature This is the second of three Special Reports on Electric Vehicles. In the first report published two weeks ago,1 we looked at the current costs of ownership of a typical mass-market EV, including and excluding subsidies, versus a similar Internal Combustion Engine Vehicle (ICEV). Based on current manufacturing costs and battery capabilities, EVs carry a significantly higher total cost per mile, even including current subsidies. In this second report, we determine that EV-specific manufacturers (specifically, TSLA) do not hold any material manufacturing advantage over conventional auto manufacturers, and lack their financial resources and intellectual experiences managing mass production operations. In addition to the risks from increased mass-market competition, the EV market faces risks of today's EV subsidies morphing into tomorrow's EV taxes, retarding the exponential growth of adoption many EV enthusiasts are betting on today. In our forthcoming third report, we will look at the potential regional and global impacts EV adoption will have on energy, power, and commodity markets. Despite the current cost and utility disadvantages of EVs, we expect governments (especially Europe and China) will continue to provide subsidies (carrots) and mandates (sticks) to further the adoption of EVs for the purposes of reducing CO2 emissions and tailpipe particulate pollution. The longer-term hope is that by forcing the EV market to expand, meaningful technological breakthroughs on batteries will eventually enable EVs to exceed ICEVs on a cost and utility basis. In this report, we conclude that: EV-specific manufacturers (TSLA) will face increasingly stiff competition from conventional auto manufacturers, who may enjoy lower manufacturing, distribution, and service costs and have ICEV profits to subsidize near-term EV losses. Access to chargers will be a growing problem for widespread EV adoption, especially for EVs to penetrate apartment-dwellers. Government EV subsidies will become fiscally difficult to continue as adoption increases and gasoline taxes are lost (especially in Europe). The small amount of carbon saved by EVs does not justify the subsidies, further increasing the risk subsidies are reduced or allowed to phase out (especially in the U.S.). EVs: Winners And Losers Investor interest in EVs tends to focus on the only publicly traded play in the space, Tesla Motors (TSLA, Q). Tesla has an enthusiastic fan base, which seems to extend well beyond the rather modest number of people who actually own the vehicles (Chart 1). That enthusiasm is probably somewhat responsible for favorable media coverage and the company's speculatively-high market cap (Chart 2), which is currently on a par with General Motors (GM, N), despite the fact that Tesla has never made a profit. (Chart 3 and Chart 4).When we read media and analyst coverage of Tesla, we often wonder if those writing the articles know anything about automobiles besides how to drive them. An example is this Forbes article regarding Tesla as uniquely visionary, building up a big lead on its sleepy competition. Chart 1Tesla's EV Sales Are Modest Chart 2Tesla's Market Cap Surpasses GM's Chart 3Tesla: Financial Performance Chart 4GM: Financial Performance "[Manufacturer] complacency about electric vehicle (EV) technology is worse than perceived. Despite more talk of developing EVs for mass-market adoption, a lack of real action and strategic commitments betray their underlying conviction, with no clear pathway to high-volume EV production before the mid-2020s"2 Setting aside for a moment the question as to whether Tesla, as a serial destroyer of capital (to date), will have access to the financial resources needed to become itself a "high-volume" producer of EVs, most commentators ignore the fact that building an EV is far less complicated than building an ICEV, and the conventional car companies are likely to have cost advantages (not to mention the benefits of decades of experience with mass production) once they do commit to the EV. What's The Difference Between An EV And An ICEV? In a general sense, an automobile consists of two main components: the drivetrain and the rest of the vehicle. What differentiates an EV from an ICEV is almost entirely the drivetrain and battery pack. Although the shape and weight of the battery pack requires some alteration to the body frame of the vehicle, and many EVs include regenerative brakes, substantially everything else in the rest of the EV is very similar. Drivetrain The drivetrain of an ICEV is where the vast majority of precision parts are located. A typical ICEV has hundreds of precision parts and must be manufactured and assembled to exact tolerances in order to last beyond the typically expected 100,000+ mile trouble-free life. Engines are also subject to extremes in temperatures ranging from -40°C (-40°F) at start up in a cold winter to close to 90°C (190°F) under operation. Transmissions are similarly complicated. In contrast, the drivetrain of an EV is extremely simple, consisting essentially of an electric motor and a transmission, which is also greatly simplified due to the nature of the torque curve of electric motors (Illustration 1). Illustration 1Key Components Of A Bolt EV Drive Unit Unlike an ICEV which has numerous reciprocating parts (which are hard to engineer), all parts of an EV drivetrain rotate (which are much easier to engineer). Similarly, while there are numerous parts on an ICEV which require precision machining, friction bearings, and pressurized lubrication and cooling, analogous parts on an EV drivetrain are much fewer in number, can use ball bearings, and are lubricated for life. The fact that an EV drivetrain does not require pressurized lubrication and has a much simpler cooling system further simplifies the design and reduces the number of parts. It would not be an exaggeration to suggest that the drivetrain of an EV has an order of magnitude fewer parts than an ICEV of similar size. Any automotive company capable of designing and manufacturing an ICEV drivetrain should be capable of producing an EV drivetrain or outsourcing one if necessary. Battery Pack And Electronics Similarly, the battery pack of an EV is a mechanically simple thing to make. Battery cells are assembled into modules and the modules are assembled into the final battery pack (Illustration 2). The major challenge and potential differentiator is in the battery cells, which are effectively commodities (see below), and not in the manufacture or design of the battery pack. EV battery packs can produce a lot of heat when running or charging, and the battery packs tend to have simple cooling systems which vary from manufacturer to manufacturer.3 Illustration 2Battery Packs Are Battery Cells Assembled In Groups An EV requires a significant amount of power electronics for the control of the motor, charging, and so on. Such power systems have been designed and made for decades, and, besides some unusual requirements due to the need to operate at extreme temperatures, there is no great technical challenge inherent in such systems. Indeed, while the operating life of an ICEV is typically on the order of 5,000 to 10,000 hours (100,000-200,000 miles), power electronics are often designed to operate for 100,000 hours or more. The drivetrain will not be the limiting factor on the longevity of an EV. Most likely, the cost of an EV's drivetrain (excluding the battery pack) and typical features such as regenerative brakes, a more robust suspension (due to the greater weight of the EV on account of the heavy battery), and accommodation for the battery pack, is somewhat less than that of an equivalent ICEV. Although the EV drivetrain is simpler to build, high-output electric motors and related control electronics are not cheap to manufacture due to the requirement for materials such as copper and exotic alloys. The reason for the substantially higher cost of EVs is the battery pack. And The Winners Are ... Despite investor enthusiasm for the "technological revolution" EVs represent, it is actually far more complicated and technologically difficult to design and manufacture an ICEV than an EV. The EV has far fewer precision-made parts, and few such components are truly proprietary. Electric motors have been made for over a century, and their design and manufacture are not complicated - at least when compared to the vastly more complicated and precision-made ICEV. Similarly, an EV transmission is significantly simpler than the transmissions found in all ICEVs. We conclude that the design and manufacture of an EV drivetrain should be simple for a company accustomed to making ICEVs. Even the power and charging electronics are similar to the sorts of things electrical engineers have been making for a long time. Similarly, the assembly of a battery pack from commodity cells should be a relatively straightforward process for any company used to volume manufacturing. As we predicted, battery production appears to be scaling up, and sourcing commodity batteries should not be difficult if demand for EVs emerges as some predict. Although we have largely skipped over a discussion of the non-drivetrain components of an automobile, traditional manufacturers have been manufacturing these for a very long time and are capable of producing them at a reasonable cost and in vast numbers. The major difference between the non-drivetrain components of an EV and ICEV is accommodation for the shape and weight of the battery pack, which, again, should not be a substantial engineering challenge for any large auto manufacturer. For many years, auto manufacturers have developed "platforms" that allow them to mass produce standardized components that are used on what are apparently very different vehicles. Most likely, traditional vendors will produce a platform which can be used for both ICEVs and EVs, meaning that they can reuse parts produced for their ICEVs in EVs, saving money in terms of design, tooling, and volume manufacturing. Obviously, an EV-only vendor does not have that option. Finally, large automobile manufacturers have a global distribution channel as well as nearly omnipresent parts and service networks, including parts and service available from an assortment of third party providers. Developing this support system is particularly important for EVs to enter the mainstream: it is false to assume the simpler drivetrain of an EV will mean the vehicles never need repairs, as there are many failure modes. Beyond wealthy early-adopting EV enthusiasts who purchase EVs as a second or third auto, the typical consumer owns only a single vehicle, making prompt and affordable repairs critical to the utility of a mass-market vehicle, regardless of whether that vehicle is an EV or an ICEV. In summary, we conclude that there is no particular engineering challenge for existing large automakers to enter and dominate the EV business (Tables 1 and 2). Most likely, profit margins on EVs will be low or negative for some time (see Part 1), and large vendors will be in a position to use their profitable ICEV sales to subsidize their market share in the EV business. The main competitive uncertainty for EV manufacturing is how much battery performance and price can be improved from current levels. The battery cells themselves are rather commoditized, making it difficult for any single auto company to develop a substantial lead on the field in battery pack performance. Table 1Conventional Auto Manufacturers Are Ramping Up EV Penetration Table 2TSLA Will Lose Market Share As Mass-Market Competition Expands Rate Of Adoption As we showed in Part 1, costs of ownership of EVs are quite high compared to ICEVs over the EV's assumed 100,000 mile life. Although we believe accelerated depreciation of the EV will significantly increase the differential, most consumers are unaware of that likelihood. Governments and EV manufacturers heavily subsidize EVs; without such subsidies, consumers' costs of ownership would be materially higher. If EVs become a significant share of the vehicle market, such subsidies will have to be reduced, and high taxes would have to be applied to either the vehicle or the fuel (electricity) to make up for the loss of massive government revenues from today's gasoline taxes. The most expensive item in an EV is the battery pack (Chart 5). It appears to be an article of faith among EV advocates that existing batteries will somehow see cost reductions to below their current materials costs, and/or that revolutionary battery technology will emerge in (rapid) due course. It is interesting to speculate as to what might occur in the future. However, we prefer to be data driven. After all, why confine speculation on technological advancements only to things battery-related? Rapid technological advancements in oil production have cut gasoline prices dramatically in the past few years, while continued improvements of conventional engines can raise fuel efficiency and dramatically lower pollution/CO2 emissions of ICEVs, stiffening the competition against the rise of EVs. Chart 5As The Battery Pack Increases In Size,##BR##It Commands A Larger Share Of The Total Cost Of The EV Besides cost, there are numerous compromises associated with an EV which may temper adoption. These include the limited range and slow refueling times, which are important if the owner regularly--or even occasionally--makes long trips; degraded performance in temperature extremes, and so on. An important consideration for many buyers is the size of the car: a soccer mom is not likely to find a Bolt a suitable replacement for a minivan. Larger EVs require disproportionally larger batteries: the Tesla Model S 85 has a 40% larger battery but only a 10% greater range compared to the Bolt. EVs More Likely To Be Popular In The EU Than In North America Europeans tend to drive fewer kilometers and take fewer long trips than North Americans. The average distance traveled by car is 14,000 km4 (8,700 miles) in Europe compared to 20,000 km (12,000 miles) in the U.S., so a European would likely get a few more years out an EV - though not many more kilometers. Similarly, most of the population of Europe lives in areas where temperature extremes are less severe than they are in certain areas of the U.S. and Canada, meaning some of the compromises associated with operating an EV would be less significant. Europe has a much higher population density than the U.S., making particulate pollution a larger issue, and Europeans have more concerns regarding climate change. Much higher gasoline taxes and narrow roads in Europe also incentivize drivers to own smaller vehicles, similar to the Bolt. Due to these factors and the "carrot and stick" approach of subsidies and mandates favored by some EU countries, we conclude EVs are likely to be much more popular in the EU than in the U.S. (Chart 6) Chart 6European EV Sales Are Outpacing U.S. Sales Regardless, even EV adoption in the EU is bound to be constrained by: Higher costs of EVs compared to ICEVs; Driving habits which may preclude ownership by some people; Access to both private and public chargers; Long lives of ICEVs; and Availability of EVs for purchase. In Part 1 of our EV analysis, we break down the substantially higher cost of ownership for an EV compared to an ICEV. Driving habits boil down to the question of standard deviation: although the average EU driver may travel about 70 km (43 miles) per work day, a sizeable minority may travel much more than that or regularly make round trips beyond the range of their EVs. Alternatively, some may want to pull a trailer (caravan), etc... These drivers would be less likely to purchase an EV except perhaps as a second vehicle. Access to private chargers depends on the nature of the buyer's housing: somebody living in a house with a driveway can pay to have a slow charger installed, whereby somebody who relies on street parking or a nearby parking lot does not have that option. Due to the far greater population density of Europe, access to public chargers may be more of a constraint in the EU than in the U.S. In Part 1, we explained why we believe that ICEVs will outlast EVs for the foreseeable future due to degradation inherent with all battery technologies. There may be a dramatic breakthrough in battery technology, but batteries have numerous parameters which must be acceptable before they can be used in an EV. Most likely, an EV will be scrapped rather than have its battery replaced after about 160,000 km, whereas many ICEVs are routinely kept on the road for double that range. Consumers will eventually realize this and incorporate accelerated depreciation into their costs of ownership calculation. Not only that, but many will choose to keep their ICEVs on the road as long as possible simply to save the expense of purchasing a new vehicle, especially if the inherent limitations of EVs mean they are not suitable for that particular driver. Despite still-generous government subsidies, GM is believed to lose $9,000 for every Bolt it sells. Similarly, the CEO of Fiat lamented some time ago the company was losing $14,000 for every Fiat 500 EV it sold,5 and Tesla loses money despite selling into a premium segment. There is no reason to believe any EV vendor will actually make money on EVs for many years. After all, they all have the same problems with respect to the cost of batteries. We believe auto vendors are likely to limit sales of EVs through rationing or high prices in order to limit their own losses. EVs Are Unlikely To Replace All ICEVs The compromises/deficiencies associated with EVs mean that they will not be suitable for many consumers unless a massive battery breakthrough is achieved. The limited range is an obvious issue: a consumer might, for example, travel an average of 12,000 miles (20,000 km) per year but may regularly take a drive of a few hundred miles, which would require one or more recharging stops. It is all well and good to speak of rapid charging, but even this would quickly lose its allure after long trips, especially given the issues noted in "EVs Will Require a Sizeable Charging Infrastructure" below. Almost 3 million pickup trucks are sold in the U.S. every year, out of 17.5 million vehicle sales. Light trucks, including SUVs and Crossovers, make up another 10.5 million sales. Whether or not the trucks are actually used for hauling, the battery size, and therefore cost of ownership, would have to be particularly large for a pickup truck. A 120 kWh battery would add about 1,600 pounds (720 kg) to the vehicle, which is about half the cargo capacity of a Ford F-150 full size pickup truck. Many pickup trucks have significantly oversized engines in order to tow heavy loads. It is questionable an EV pickup truck would have the range or towing capacity required by many buyers. EVs Will Require A Sizeable Charging Infrastructure First-time EV owners will either have to invest in a charging station for their homes or somehow get access to one. Charging stations come in different types. In the case of the Bolt, a typical home charger delivers 4 miles (6.5 km) of range/hour of charge or about 32 miles (52 km) of range for 8 hours. What GM calls "Fast Charging" delivers almost a full charge over 8 hours. What GM refers to as "Super Fast Charging", or true fast charging, delivers 90 miles (145 km) of range in 30 minutes or 160 miles (258 km) in 1 hour, but is only available in public locations6 and requires a special option on the vehicle. "Super Fast Charging" means that a customer planning a trip of over 238 miles will have to plan for at least one 30 minute stop for every 90 miles of additional travel. Of course, this is when the vehicle is new and under ideal conditions without any temperature extremes, etc. An older EV may require a 30 minute stop after the first 150 miles and a subsequent 30 minute stop for every hour of travel (60-70 miles) after that. Private Chargers Unless they are satisfied with multi-day charging, new EV buyers have to pay an electrician to install a high current charger outlet which is accessible to the vehicle. Not all homes have ample parking, nor is it easy to install a high current port accessible to a vehicle in all homes. A typical high current charging port required for a "slow charger" requires a 40, 50, or 60 amp outlet. Many homes have only a 100 amp service, which may pose issues if the vehicle is charging and, for example, an air conditioner starts up. Similarly, apartment/condo dwellers with access to parking may have access to EV chargers provided by the building, though the electric service to the building/parking lot may require upgrading in the event a significant number of owners buy EVs. Publicly Available Chargers The largest challenge might be for would-be EV buyers who park on the street, as is fairly common in many urban areas. The cost of installing EV chargers is not trivial, and it is hard to believe cities will accept the costs of installing a large number of chargers to ensure EV owners can charge their vehicles. This doesn't even account for the fact that somebody has to pay for the electricity, and street-side chargers are both expensive and dangerous, require maintenance and snow removal, and may be subject to vandalism. Additionally, some parking lots feature a couple of EV chargers, and most EV vendors provide access to a rather sparse assortment of chargers. On the surface, a 6:1 ratio of global EVs to publicly available chargers may not appear to be as much of a concern, however, the ratio is about 16:1 for slow chargers and 105:1 for fast chargers in the U.S., and 6:1 and 68:1 in the EU, respectively (Charts 7 and 8). Recall that the Bolt's "Fast charger" only supplies about 25 miles of range for every hour of charging, so public units would only be useful as a "top-up". Public chargers will have to become far more common as the number of EVs increases or owners risk planning a trip which assumes access to a charger only to discover the unit is in use and the EV owner who is using it is off shopping. Chart 7Globally, There Is One Public Charger ##br##Per Six EVs Chart 8Fast Chargers Are Much More Scarce ##br##Than Slow Chargers Fast chargers are of particular significance in the event an EV owner wishes to make a trip in excess of the vehicle's fully-charged range. "Fast charge" times - whether with a Bolt or any other EV - assume a charging station is available when the EV arrives. This may be the case on typical days, but less likely during holiday or vacation season: "A video shot yesterday at the Supercharger in Barstow, CA shows a line at the station of Teslas waiting to juice up. The driver who shot the video was number 21 in the queue, and with wait times upwards of two hours just to get to the charger, Tesla's going to have some unhappy customers on its hands."7 One can only imagine how frustrated the owner of an aged Bolt would be if they had to wait 2 hours every 60 miles. Impact Of EV Adoption On Pollution And Greenhouse Gas Emissions The production and operation of any product leaves an environmental impact in terms of pollution and Greenhouse Gas (GHG) emissions. The environmental impact associated with vehicles arises from the production of the commodities used to make the components, the manufacture of the vehicle components, the assembly of the vehicle itself, and the operation of the vehicle. EVs are not "zero emission vehicles" in any meaningful sense. It is true that they do not discharge particulate or CO2 emissions from the tailpipe, but emissions arise from the production of the vehicle platform, the battery pack, and the production of electricity used to charge the battery. The fuel mix of power generation in a particular region has a significant impact on the GHG emissions associated with electric power: countries with significant hydroelectric or nuclear power sources will have lower GHG emissions per kW than those which burn coal, oil, or natural gas. Similarly, the GHG emissions associated with the manufacture of a vehicle and its components depend on the power mix in the country in which those components are manufactured. As previously noted, an EV is very similar to an ICEV except for the drivetrain and battery. The EV's drivetrain is simpler than an ICEV's, but total GHG emissions associated with manufacturing an EV and equivalent ICEV are estimated to be quite similar, excluding the battery pack. GHG emissions associated with the manufacture and recycling of a battery pack are quite hard to pin down. The best and most recent example we found comes from IVL Swedish Environmental Research Institute, and notes: "Based on our review, greenhouse gas emissions of 150-200 kg CO2-eq/kWh battery looks to correspond to the greenhouse gas burden of current battery production."8 To put things in perspective, the GHG burden associated with the lifecycle of a 60 kWh Bolt battery pack is between 9,000 and 12,000 kg, or 9 to 12 metric tons. Because the battery pack is likely larger than advertised to limit degradation, the actual figure is probably at least 20% more, or 10.8 to 14.4 metric tons. At just 9 metric tons, assuming a 160,000 km life, the GHG burden associated manufacture and recycling of a Bolt battery pack is about 56 g CO2/km, and at 14.4 metric tons the burden is about 88 g CO2/km. To be as favorable as possible to the Bolt's potential to reduce GHG emissions, we have used the lower bound of the estimated CO2 burden of the Bolt's 60 kWh battery, 9 metric tons, in our GHG analysis in Table 3. The actual CO2 burden could be as much as 5.4 metric tons more. Note that the above calculations do not include the GHG emissions associated with recharging the battery. Recall that in Part 1, we estimated the power consumption associated with a Bolt operating for 160,000 km would be about 31,250 kWh, or ~0.20 kWh/km (0.3125 kWh/mile). The GHG burden of recharging the battery varies considerably depending on the regional mix of power generation. As shown in Table 3: Table 3EVs Will Reduce Carbon Emissions Only If Power Grid Is Green In France, where power is primarily generated via carbon-free nuclear energy, recharging the Bolt will release just 2 metric tons of CO2 during its 160,000KM life (11g/km). In coal-heavy Germany (40+% coal), recharging the Bolt will generate ~18 metric tons of CO2 (109g/km), slightly more carbon than the fuel-efficient gasoline-powered ICEV Opel Astra (104g/km). In the U.S., with the current diversified mix of power generated by natural gas (34%), coal (30%), nuclear (20%), hydro (7%), wind (6%) and solar (1%), CO2 emissions from recharging the Bolt would be only 13 metric tons (83g/km), 60% lower than the 32 tons of CO2 emitted by the ICEV Chevy Sonic. As shown, despite the higher CO2 footprint associated with manufacturing the EV's battery pack, an EV may indeed lead to an overall reduction in GHG emissions in a region where electricity generation is already low-carbon; however, the EV actually emits more CO2 in Germany, a coal-heavy country (40% coal) with fuel-efficient ICEVs. This implies EVs would create even greater CO2 increases in countries like China or India, which both generate over 70% of power from coal. The carbon intensity of U.S. power generation has been reduced by roughly 23% over the past decade due to the increased displacement of coal with natural gas (~70% of the carbon reduction) and renewables. As the U.S. and other countries continue to de-carbonize their power grids, the emissions to recharge EVs will further decline. However, even where reductions are achieved, the lifecycle emissions of the EV is nothing close to what is implied by the term "Zero Emission Vehicle." Using our generous assumptions for the carbon footprint of the EV's battery, we calculate the approximate lifecycle CO2 reductions for an EV are ~9 metric tons in the U.S., and ~6 metric tons in France. In Germany, the EV actually emits ~10 metric tons more CO2 than a comparable ICEV. EVs in coal-heavy China and India would also be expected to emit more lifecycle CO2 than a fuel-efficient ICEV. Even if power generation were 100% carbon-free in the EU and in the U.S., the CO2 savings would be only 23 tons per vehicle in the U.S and 8 tons per vehicle in the EU (lower savings in the EU due to the higher fuel efficiency of the European ICEV). One area where the EV is bound to come out ahead is in reducing particulates, NOx, and other non-GHG related pollutants, at least in the areas where the vehicles are operated, which provides cleaner air in highly populated areas. EV Subsidies Are Not Justified By Carbon Emissions In order to simplify the cost/benefit debate over legislation and regulation aimed at reducing carbon emissions, the U.S. EPA and other various U.S. agencies have calculated/estimated a "Social Cost of Carbon," i.e., the estimated economic damage created by emitting a ton of CO2 in a given year.9 In the base case, the social cost of carbon was pegged at $36/metric ton in 2015, with expectations that it would rise to $50/metric ton in 2030 and $69/metric ton in 2050 as climate issues became more severe. By comparison, the "market value" for a ton of CO2 on traded exchanges in California and in the E.U. is between $5-$15/ton. Assuming an average value of $50/metric ton, the current CO2 savings of the EV will yield about an economic benefit per vehicle of ~$450 in the U.S, and ~$300 benefit in France. In Germany, where CO2 emissions for the EV are higher than the ICEV, it adds another ~$500 to the economic cost of the EV. At a value of $50/ton, the value of CO2 savings in each region are only ~4-5% of the value of the public subsidies of $7,200-$9,500/vehicle in the U.S. and France, and only 1-2% of the total ~$22,000-$27,000 total extra societal costs of the vehicles (Table 4). In other words, the subsidies alone cost 20x more than the economic benefit of the CO2 reductions, while the total extra costs of the EV are 55-75x higher than the economic value of the CO2 reductions. Germany is offering subsidies for vehicles that increase CO2 emissions. Table 4EV Carbon Reductions Are Way Too Expensive Of course, industry may be able to lower emissions associated with battery manufacturing and recycling, and power generation may continue to be de-carbonized as well, leading to lower GHG emissions associated with EVs in the future. However, the same might be said regarding continuing improvements in ICEVs as well. For example: If U.S. drivers changed preferences to drive European-style cars with smaller engines and greater fuel efficiency (that is, wider adoption of technology that already exists today), that alone could save ~17 tons of carbon per vehicle in the U.S., dwarfing the ~10 tons of carbon savings achieved by owning an EV, at a much lower economic cost. Again, one area where the EV is bound to come out ahead is in reducing particulates, NOx, and other non-GHG related pollutants, at least in the areas where the vehicles are operated, which provides cleaner air in highly populated areas. This reduction/transfer of pollution from the city center to the power generation stations has a real health/quality of life value that we have not included in the above analysis, as the overwhelming amount of EV interest we read and receive is specifically based on EVs' (overestimated) ability to reduce global carbon emissions.10 Bottom Line: TSLA does not have an insurmountable technological lead on conventional car producers in the mass-production EV market, and is likely to lose market share to larger competitors that have better costs, infrastructure, and experience supporting a global fleet of mass-produced vehicles. Near-term adoption of EVs will be forced higher by governmental carrot and stick incentives, but these will become too expensive to continue as EVs' market share increases. Today's EV subsidies will turn into tomorrow's EV taxes as gasoline taxes are diminished, weighing on the longer-term arc of commonly-forecasted EV adoption. Finally, EVs do not necessarily reduce CO2 emissions, and when they do, the value of those CO2 reductions is exceedingly small compared to the added cost of the vehicles to producers, consumers, and government coffers. A modest ICEV only emits ~$2,000 worth of CO2 over 100,000 miles in the first place, elucidating how difficult it will be for an EV to reduce GHG emissions on a cost-competitive basis. For mass-market EVs to successfully displace ICEVs in the eyes of cost-conscious consumers and taxpayers, EV battery technology needs to improve massively, not incrementally. The batteries need to provide multiples of today's energy storage capacity with lower weight, lower cost, faster recharge abilities, and a lower carbon footprint. Furthermore, since an EV's battery recharging is only as green as the power source behind it, continued (expensive) greening and expansion of global power generation would also be necessary for EVs to demonstrate a positive impact on GHG emissions, as will be discussed more in Part 3 of this report series. Brian Piccioni, Vice President Technology Sector Strategy brianp@bcaresearch.com Matt Conlan, Senior Vice President Energy Sector Strategy mattconlan@bcaresearchny.com Robert P. Ryan, Senior Vice President Commodity & Energy Strategy rryan@bcaresearch.com Michael Commisso, Research Analyst michaelc@bcaresearch.com Johanna El-Hayek, Research Assistant johannah@bcaresearch.com Hugo Bélanger, Research Assistant HugoB@bcaresearch.com 1 Please see Technology Sector Strategy Special Report, "Electric Vehicles Part 1: Costs of Ownership", dated August 1, 2017, available at tech.bcaresearch.com. 2 https://www.forbes.com/sites/neilwinton/2017/06/29/tesla-focus-means-victory-versus-complacent-mainstream-in-electric-car-market-report/#4d0d4684577e 3 http://www.hybridcars.com/2017-chevy-bolt-battery-cooling-and-gearbox-details/ 4 http://www.acea.be/publications/article/cars-trucks-and-the-environment 5 http://jalopnik.com/sergio-marchionne-doesnt-want-you-to-buy-a-fiat-500e-1579578914 6 https://www.chevyevlife.com/bolt-ev-charging-guide 7 http://bgr.com/2016/12/27/tesla-supercharger-wait-times-lines-california/ 8 http://www.ivl.se/download/18.5922281715bdaebede9559/1496046218976/C243+The+life+cycle+energy+consumption+and+CO2+emissions+from+lithium+ion+batteries+.pdf (page 42) 9 https://www.epa.gov/sites/production/files/2016-12/documents/social_cost_of_carbon_fact_sheet.pdf 10 It is worth pointing out that if the incentive structure is such that entrepreneurs are rewarded for finding ways to economically reduce carbon emissions in ICEVs in a way that is cost-competitive with EVs, the principal advantage of EVs would be challenged. There is no ironclad rule of physics we are aware of that precludes such a development. Investment Views and Themes Recommendations Strategic Recommendations Tactical Trades Commodity Prices and Plays Reference Table Trades Closed in 2017 Summary of Trades Closed in 2016