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Highlights Investors should view social media as a technological innovation with negative productivity growth. Social media has contributed to policy mistakes – such as fiscal austerity and protectionism – that have acted as shocks to aggregate demand over the past 15 years. The cyclical component of productivity was long lasting in nature during the last economic expansion. Forces that negatively impact economic growth but do not change the factors of production necessarily reduce measured productivity, and repeated policy mistakes strongly contributed to the slow growth profile of the last economic cycle. Political polarization in a rapidly changing world is the root cause of these policy shocks, but social media likely facilitated and magnified them. The risks of additional mistakes from populism remain present, even before considering other risks to society from social media: a reduction in mental health among young social media users, and the role that social media has played in spreading misinformation. A potential revival in protectionist sentiment is a risk to a constructive cyclical view that we will be closely monitoring over the coming 12-24 months. Investors with concentrated positions in social media stocks should be aware of the potential idiosyncratic risks facing these companies from the public’s impression of the impact of social media on society – especially if social media companies come to be widely associated with political gridlock, the polarization of society, and failed economic policies (as already appears to be the case). Feature Investors should view social media as a technological innovation with negative productivity growth. Social media has contributed to policy mistakes – such as fiscal austerity and protectionism – that have acted as shocks to aggregate demand over the past 15 years. Political polarization in a rapidly changing world is the root cause of these policy shocks, but social media likely facilitated and magnified them. While the risk of premature fiscal consolidation appears low today compared to the 2010-14 period, the pandemic and its aftermath could force the Biden administration or Congressional Democrats toward protectionist or otherwise populist actions over the coming year in the lead up to the 2022 mid-term elections. The midterms, for their part, are expected to bring gridlock back into US politics, which could remove fiscal options should the economy backslide. Frequent shocks during the last economic expansion reinforced the narrative of secular stagnation. In the coming years, any additional policy shocks following a return to economic normality will again be seen by both investors and the Fed as strong justification for low interest rates – despite the case for cyclically and structurally higher bond yields. In addition, investors with concentrated positions in social media companies should take seriously the long-term idiosyncratic risks facing these stocks. These risks stem from the public’s impression of the impact of social media on society, particularly if social media comes to be widely associated with political gridlock, the polarization of society, and failed economic policies. A Brief History Of Social Media The earliest social networking websites date back to the late 1990s, but the most influential social media platforms, such as Facebook and Twitter, originated in the mid-2000s. Prior to the advent of modern-day smartphones, user access to platforms such as Facebook and Twitter was limited to the websites of these platforms (desktop access). Following the release of the first iPhone in June 2007, however, mobile social media applications became available, allowing users much more convenient access to these platforms. Charts II-1 and II-2 highlight the impact that smartphones have had on the spread of social media, especially since the release of the iPhone 3G in 2008. In 2006, Facebook had roughly 12 million monthly active users; by 2009, this number had climbed to 360 million, growing to over 600 million the year after. Twitter, by contrast, grew somewhat later, reaching 100 million monthly active users in Q3 2011. Chart II-1Facebook: Monthly Active Users August 2021 August 2021 Chart II-2Twitter: Monthly Active Users Worldwide August 2021 August 2021   Social media usage is more common among those who are younger, but Chart II-3 highlights that usage has risen over time for all age groups. As of Q1 2021, 81% of Americans aged 30-49 reported using at least one social media website, compared to 73% of those aged 50-64 and 45% of those aged 65 and over. Chart II-4 highlights that the usage of Twitter skews in particular toward the young, and that, by contrast, Facebook and YouTube are the social media platforms of choice among older Americans. Chart II-3A Sizeable Majority Of US Adults Regularly Use Social Media A Sizeable Majority Of US Adults Regularly Use Social Media A Sizeable Majority Of US Adults Regularly Use Social Media Chart II-4Older Americans Use Facebook Far More Than Twitter August 2021 August 2021 Chart II-5Social Media Has Changed The Way People Consume News August 2021 August 2021 As a final point documenting the development and significance of social media, Chart II-5 highlights that more Americans now report consuming news often (roughly once per day) from a smartphone, computer, or tablet other than from television. Radio and print have been completely eclipsed as sources of frequent news. The major news publications themselves are often promoted through social media, but the rise of the Internet has weighed heavily on the journalism industry. Social media has, for better and for worse, enabled the rapid proliferation of alternative news, citizen journalism, rumor, conspiracy theories, and foreign disinformation. The Link Between Social Media And Post-GFC Austerity Following the 2008-2009 global financial crisis (GFC), there have been at least five deeply impactful non-monetary shocks to the US and global economies that have contributed to the disconnection between growth and interest rates: A prolonged period of US household deleveraging from 2008-2014 The Euro Area sovereign debt crisis Fiscal austerity in the US, UK, and Euro Area from 2010 – 2012/2014 The US dollar / oil price shock of 2014 The rise of populist economic policies, such as the UK decision to leave the European Union, and the US-initiated trade war of 2018-2019. Among these shocks to growth, social media has had a clear impact on two of them. In the case of austerity in the aftermath of the Great Recession, a sharp rise in fiscal conservatism in 2009 and 2010, emblematized by the rise of the US Tea Party, profoundly affected the 2010 US midterm elections. It is not surprising that there was a fiscally conservative backlash following the crisis: the US budget deficit and debt-to-GDP ratio soared after the economy collapsed and the government enacted fiscal stimulus to bail out the banking system. And midterm elections in the US often lead to significant gains for the opposition party However, Tea Party supporters rapidly took up a new means of communicating to mobilize politically, and there is evidence that this contributed to their electoral success. Chart II-6 illustrates that the number of tweets with the Tea Party hashtag rose significantly in 2010 in the lead-up to the election, which saw the Republican Party take control of the House of Representatives as well as the victory of several Tea Party-endorsed politicians. Table II-1 highlights that Tea Party candidates, who rode the wave of fiscal conservatism, significantly outperformed Democrats and non-Tea Party Republicans in the use of Twitter during the 2010 campaign, underscoring that social media use was a factor aiding outreach to voters. Chart II-6Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Table II-1Tea Party Candidates Significantly Outperformed In Their Use Of Social Media August 2021 August 2021   And while it is more difficult to analyze the use and impact of Facebook by Tea Party candidates and supporters owing to inherent differences in the structure of the Facebook platform, interviews with core organizers of both the Tea Party and Occupy Wall Street movements have noted that activists in these ideologically opposed groups viewed Facebook as the most important social networking service for their political activities.1 Under normal circumstances, we agree that fiscal policy should be symmetric, with reduced fiscal support during economic expansions following fiscal easing during recessions. But in the context of multi-year household deleveraging, the fiscal drag that occurred in following the 2010 midterm elections was clearly a policy mistake. This mistake occurred partially under full Democratic control of government and especially under a gridlocked Congress after 2010. Chart II-7 highlights that the contribution to growth from government spending turned sharpy negative in 2010 and continued to subtract from growth for some time thereafter. In addition, panel of Chart II-7 highlights that the US economic policy uncertainty index rose in 2010 after falling during the first year of the recovery, reaching a new high in 2011 during the Tea Party-inspired debt ceiling crisis. Chart II-7The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake Chart II-8Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile In addition to the negative impact of government spending on economic growth, this extreme uncertainty very likely damaged confidence in the economic recovery, contributing to the subpar pace of growth in the first half of the last economic expansion. Chart II-8 highlights the weak evolution in real per capita GDP from 2009-2019 compared with previous economic cycles, which was caused by a prolonged household balance sheet recovery process that was made worse by policy mistakes. To be sure, the UK and the EU did not have a Tea Party, and yet political elites imposed fiscal austerity. It is also the case that President Obama was the first president to embrace social media as a political and public relations tool. So it cannot be said that either social media or the Republican Party are uniquely to blame for the policy mistakes of that era. But US fiscal policy would have been considerably looser in the 2010s if not for the Tea Party backlash, which was partly enabled by social media. Too tight of fiscal policy in turn fed populism and produced additional policy mistakes down the road. From Fiscal Drag To Populism While social media is clearly not the root cause of the recent rise of populist policies, it has had a hand in bringing them about – in both a direct and indirect manner. The indirect link between social media use and the rise in populist policies has mainly occurred through the highly successful use of social media by international terrorist organizations (chiefly ISIL) and its impact on sentiment toward immigration in several developed market economies. Chart II-9Terrorism And Immigration Likely Contributed To Brexit Terrorism And Immigration Likely Contributed To Brexit Terrorism And Immigration Likely Contributed To Brexit Chart II-9 highlights that public concerns about immigration and race in the UK began to rise sharply in 2012, in lockstep with both the rise in UK immigrants from EU accession countries and a series of events: the Syrian refugee crisis, the establishment and reign of the Islamic State, and three major terrorist attacks in European countries for which ISIL claimed responsibility. Given that the main argument for “Brexit” was for the UK to regain control over its immigration policies, these events almost certainly increased UK public support for withdrawing from the EU. In other words, it is not clear that Brexit would have occurred (at least at that moment in time) without these events given the narrow margin of victory for the “leave” campaign. The absence of social media would not have prevented the rise of ISIL, as that occurred in response to the US’s precipitous withdrawal from Iraq. The inevitable rise of ISIL would still have generated a backlash against immigration. Moreover, fiscal austerity in the UK and EU also fed other grievances that supported the Brexit movement. But social media accelerated and amplified the entire process.  Chart II-10Brexit Weakened UK Economic Performance Prior To The Pandemic Brexit Weakened UK Economic Performance Prior To The Pandemic Brexit Weakened UK Economic Performance Prior To The Pandemic Chart II-10 presents fairly strong evidence that Brexit weakened UK economic performance relative to the Euro Area prior to the pandemic, with the exception of the 2018-2019 period. In this period Euro Area manufacturing underperformed during the Trump administration’s trade war as a result of its comparatively higher exposure to automobile production and its stronger ties to China. Panel 2 highlights that GBP-EUR fell sharply in advance of the referendum, and remains comparatively weak today. Turning to the US, Donald Trump’s election as US President in 2016 was aided by both the direct and indirect effects of social media. In terms of indirect effects, Trump benefited from similar concerns over immigration and terrorism that caused the UK to leave the EU: Chart II-11 highlights that terrorism and foreign policy were second and third on the list of concerns of registered voters in mid-2016, and Chart II-12 highlights that voters regarded Trump as the better candidate to defend the US against future terrorist attacks. Chart II-11Terrorism Ranked Highly As An Issue In The 2016 US Election August 2021 August 2021 Chart II-12Voters Regarded Trump As Better Equipped To Defend Against Terrorism August 2021 August 2021 Trump’s election; and the enactment of populist policies under his administration, were directly aided by Trump’s active use of social media (mainly Twitter) to boost his candidacy. Chart II-13 highlights that there were an average of 15-20 tweets per day from Trump’s Twitter account from 2013-2015, and 80% of those tweets occurred before he announced his candidacy for president in June 2015. This strongly underscores that Trump mainly used Twitter to lay the groundwork for his candidacy as an unconventional political outsider rather than as a campaign tool itself, which distinguishes his use of social media from that of other politicians. In other words, new technology disrupted the “good old boys’ club” of traditional media and elite politics. Some policies of the Trump administration were positive for financial markets, and it is fair to say that Trump fired up animal spirits to some extent: Chart II-14 highlights that the Tax Cuts and Jobs Act caused a significant rise in stock market earnings per share. But the Trump tax cuts were a conventional policy pushed mostly by the Congressional leadership of the Republican Party, and they did not meaningfully boost economic growth. Chart II-15 highlights that, while the US ISM manufacturing index rose sharply in the first year of Trump’s administration, an uptrend was already underway prior to the election as a result of a significant improvement in Chinese credit growth and a recovery in oil prices after the devastating collapse that took place in 2014-2015. Chart II-13Trump Used Twitter To Lay The Groundwork For His Candidacy Trump Used Twitter To Lay The Groundwork For His Candidacy Trump Used Twitter To Lay The Groundwork For His Candidacy Chart II-14The Trump Tax Cuts A Huge Rise In Corporate Earnings The Trump Tax Cuts A Huge Rise In Corporate Earnings The Trump Tax Cuts A Huge Rise In Corporate Earnings   Chart II-15But The Tax Cuts Did Not Do Much To Boost Growth But The Tax Cuts Did Not Do Much To Boost Growth But The Tax Cuts Did Not Do Much To Boost Growth Similarly, Chart II-15 highlights that the Trump trade war does not bear the full responsibility of the significant slowdown in growth in 2019, as China’s credit impulse decelerated significantly between the passage of the Tax Cuts and Jobs Act and the onset of the trade war because Chinese policymakers turned to address domestic concerns. Chart II-16The Trade War Caused An Explosion In Global Trade Uncertainty The Trade War Caused An Explosion In Global Trade Uncertainty The Trade War Caused An Explosion In Global Trade Uncertainty But Chart II-16 highlights that the aggressive imposition of tariffs, especially between the US and China, caused an explosion in trade uncertainty even when measured on an equally-weighted basis (i.e., when overweighting trade uncertainty, in countries other than the US and China), which undoubtedly weighed on the global economy and contributed to a very significant slowdown in US jobs growth in 2019 (panel 2). Moreover, Chinese policymakers responded to the trade onslaught by deleveraging, which weighed on the global economy; and consolidating their grip on power at home. In essence, Trump was a political outsider who utilized social media to bypass the traditional media and make his case to the American people. Other factors contributed to his surprising victory, not the least of which was the austerity-induced, slow-growth recovery in key swing states. While US policy was already shifting to be more confrontational toward China, the Trump administration was more belligerent in its use of tariffs than previous administrations. The trade war thus qualifies as another policy shock that was facilitated by the existence of social media. Viewing Social Media As A Negative Productivity-Innovation A rise in fiscal conservatism leading to misguided austerity, the UK’s decision to leave the European Union, and the Trump administration’s trade war have represented significant non-monetary shocks to both the US and global economies over the past 12 years. These shocks strongly contributed to the subpar growth profile of the last economic expansion, as demonstrated above. Chart II-17Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Given the above, it is reasonable for investors to view social media as a technological innovation with negative productivity growth, given that it has facilitated policy mistakes during the last economic expansion. Chart II-17 underscores this point, by highlighting that multi-factor productivity growth has been extremely weak in the post-GFC environment. While productivity is usually driven by supply-side factors over the longer term, it has a cyclical component to it – and in the case of the last economic expansion, the cyclical component was long lasting in nature. Any forces negatively impacting economic growth that do not change the factors of production necessarily reduce measured productivity; it is for this reason that measured productivity declines during recessions; and policy mistakes negatively impact productivity growth. The Risk Of Aggressive Austerity Seems Low Today… Chart II-18State & Local Government Finances Are In Much Better Shape Today State & Local Government Finances Are In Much Better Shape Today State & Local Government Finances Are In Much Better Shape Today Fiscal austerity in the early phase of the last economic cycle was the first social media-linked shock that we identified, but the risk of aggressive austerity appears low today. Much of the fiscal drag that occurred in the aftermath of the global financial crisis happened because of insufficient financial support to state and local governments – and the subsequent refusal by Congress to authorize more aid. But Chart II-18 highlights that state and local government finances have already meaningfully recovered, on the back of bipartisan stimulus in 2020, while the American Rescue Plan provides significant additional funding. While it is true that US fiscal policy is set to detract from growth over the coming 6-12 months, this will merely reflect the unwinding of fiscal aid that had aimed to support household income temporarily lost, as a result of a drastic reduction in services spending. As we noted in last month’s report,2 goods spending will likely slow as fiscal thrust turns to fiscal drag, but services spending will improve meaningfully – aided not just by a post-pandemic normalization in economic activity, but also by the deployment of some of the sizable excess savings that US households have accumulated over the past year. Fiscal drag will also occur outside of the US next year. For example, the IMF is forecasting a two percentage point increase in the Euro Area’s cyclically-adjusted primary budget balance, which would represent the largest annual increase over the past two decades. But here too the reduction in government spending will reflect the end of pandemic-related income support, and is likely to occur alongside a positive private-sector services impulse. During the worst of the Euro Area sovereign debt crisis, the impact of austerity was especially acute because it was persistent, and it occurred while the output gap was still large in several Euro Area economies. Chart II-19 highlights that Euro Area fiscal consolidation from 2010-2013 was negatively correlated with economic activity during that period, and Chart II-20 highlights that, with the potential exception of Spain, this austerity does not appear to have led to subsequently stronger rates of growth. Chart II-19Euro Area Austerity Lowered Growth During The Consolidation Phase… August 2021 August 2021 Chart II-20…And Did Not Seem To Subsequently Raise Growth August 2021 August 2021   This experiment in austerity led the IMF to conclude that fiscal multipliers are indeed large during periods of substantial economic slack, constrained monetary policy, and synchronized fiscal adjustment across numerous economies.3 Similarly, attitudes about austerity have shifted among policymakers globally in the wake of the populist backlash. Given this, despite the significant increase in government debt levels that has occurred as a result of the pandemic, we strongly doubt that advanced economies will attempt to engage in additional austerity prematurely, i.e., before unemployment rates have returned close-to steady-state levels. …But The Risk Of Protectionism And Other Populist Measures Looms Large The role that social media has played at magnifying populist policies should be concerning for investors, especially given that there has been a rising trend towards populism over the past 20 years. In a recent paper, Funke, Schularick, and Trebesch have compiled a cross-country database on populism dating back to 1900, defining populist leaders as those who employ a political strategy focusing on the conflict between “the people” and “the elites.” Chart II-21 highlights that the number of populist governments worldwide has risen significantly since the 1980s and 1990s, and Chart II-22 highlights that the economic performance of countries with populist leaders is clearly negative. Chart II-21Populism Has Been On The Rise For The Past 30 Years August 2021 August 2021 The authors found that countries’ real GDP growth underperformed by approximately one percentage point per year after a populist leader comes to power, relative to both the country’s own long-term growth rate and relative to the prevailing level of global growth. To control for the potential causal link between economic growth and the rise of populist leaders, Chart II-23 highlights the results of a synthetic control method employed by the authors that generates a similar conclusion to the unconditional averages shown in Chart II-22: populist economic policies are significantly negative for real economic growth. Chart II-22Populist Leaders Are Clearly Growth Killers Even After… August 2021 August 2021 Chart II-23… Controlling For The Odds That Weak Growth Leads To Populism August 2021 August 2021 Chart II-24Inequality: The Most Important Structural Cause Of Populism And Polarization Inequality: The Most Important Structural Cause Of Populism And Polarization Inequality: The Most Important Structural Cause Of Populism And Polarization This is especially concerning given that wealth and income inequality, perhaps the single most important structural cause of rising populism and political polarization, is nearly as elevated as it was in the 1920s and 1930s (Chart II-24). This trend, at least in the US, has been exacerbated by a decline in public trust of mainstream media among independents and Republicans that began in the early 2000s and helped to fuel the public’s adoption of alternative news and social media. The decline in trust clearly accelerated as a result of erroneous reporting on what turned out to be nonexistent weapons of mass destruction in Iraq and other controversies of the Bush administration. Chart II-21 showed that the rise in populism has also yet to abate, suggesting that social media has the potential to continue to amplify policy mistakes for the foreseeable future. It is not yet clear what economic mistakes will occur under the Biden administration, but investors should not rule out the possibility of policies that are harmful for growth. The likely passage of a bipartisan infrastructure bill or a partisan reconciliation bill in the second half of this year will most likely be the final word on fiscal policy until at least 2025,4 underscoring that active fiscal austerity is not likely a major risk to investors. Spending levels will probably freeze after 2022: Republicans will not be able to slash spending, and Democrats will not be able to hike spending or taxes, if Republicans win at least one chamber of Congress in the midterms (as is likely). Biden has preserved the most significant of Trump’s protectionist policies by maintaining US import tariffs against China, and the lesson from the Tea Party’s surge following the global financial crisis is that major political shifts, magnified by social media, can manifest themselves as policy with the potential to impact economic activity within a two-year window. Attitudes toward China have shifted negatively around the world because of deindustrialization and now the pandemic.5 White collar workers in DM countries have clearly fared better during lockdowns than those of lower-income households. This has created extremely fertile ground for a revival in populist sentiment, which could force the Biden administration or Congressional Democrats toward protectionist or otherwise populist actions over the coming year, in the lead up to the 2022 mid-term elections. Investment Conclusions In this report, we have documented the historical link between social media, populism, and policy mistakes during the last economic expansion. It is clear that neither social media nor even populism is solely responsible for all mistakes – the UK’s and EU’s ill-judged foray into austerity was driven by elites. Furthermore, we have not addressed in this report the impact of populism on actions of emerging markets, such as China and Russia, whose own behavior has dealt disinflationary blows to the global economy. Nevertheless, populism is a potent force that clearly has the power to harness new technology and deliver shocks to the global economy and financial markets. The risks of additional mistakes from populism are still present, and that is even before considering other risks to society from social media: a reduction in mental health among young social media users, and the role that social media has played in spreading misinformation – contributing to the vaccine hesitancy in some DM countries that we discussed in Section 1 of our report. Two investment conclusions emerge from our analysis. First, we noted in our April report that there is a chance that investor expectations for the natural rate of interest (“R-star”) will rise once the economy normalizes post-pandemic, but that this will likely not occur as long as investors continue to believe in the narrative of secular stagnation. Despite the fact that the past decade’s shocks occurred against the backdrop of persistent household deleveraging (which has ended in the US), these shocks reinforced that narrative, and any additional policy shocks following a return to economic normality will again be seen by both investors and the Fed as strong justification for low interest rates. Thus, while the rapid closure of output gaps in advanced economies over the coming year argues for both cyclically and structurally higher bond yields, a revival in protectionist sentiment is a risk to this view that we will be closely monitoring over the coming 12-24 months. Chart II-25The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market Second, for tech investors, the bipartisan shift in public sentiment to become more critical of social media companies is gradually becoming a real risk, potentially affecting user growth. Based solely on Facebook, Twitter, Pinterest, and Snapchat, social media companies do not account for a very significant share of the overall equity market (Chart II-25), suggesting that the impact of a negative shift in sentiment toward social media companies would not be an overly significant event for equity investors in general. Chart II-25 highlights that the share of social media companies as a percent of the broad tech sector rises if Google is included; YouTube accounts for less than 15% of Google’s total advertising revenue, however, suggesting modest additional exposure beyond the solid line in Chart II-25. Still, investors with concentrated positions in social media stocks should be aware of the potential idiosyncratic risks facing social media companies as a result of the public’s impression of the impact of social media on society. If social media companies come to be widely associated with political gridlock, the polarization of society, and failed economic policies (as already appears to be the case), then the fundamental performance of these stocks is likely to be quite poor regardless of whether or not tech companies ultimately enjoy a relatively friendly regulatory environment under the Biden administration. Jonathan LaBerge, CFA Vice President The Bank Credit Analyst Footnotes 1 Grassroots Organizing in the Digital Age: Considering Values and Technology in Tea Party and Occupy Wall Street by Agarwal, Barthel, Rost, Borning, Bennett, and Johnson, Information, Communication & Society, 2014. 2 Please see The Bank Credit Analyst “July 2021,” dated June 24, 2021, available at bca.bcaresearch.com 3 “Are We Underestimating Short-Term Fiscal Multipliers?” IMF World Economic Outlook, October 2012 4 Please see US Political Strategy Outlook "Third Quarter Outlook 2021: Game Time," dated June 30, 2021, available at usps.bcaresearch.com 5 “Unfavorable Views of China Reach Historic Highs in Many Countries,” PEW Research Center, October 2020.

Highlights Recent progress on the path to a post-pandemic state and the return to pre-COVID economic conditions has been mixed. The share of vaccinated individuals continues to rise globally, and the number of confirmed UK cases has recently peaked. However, vaccine penetration remains comparatively low in the US, and there has been no meaningful change in the pace of vaccination. Given the emergence of the delta variant as well as vaccine hesitancy in some countries, policymakers currently face a trilemma that is conceptually similar to the Mundell-Fleming Impossible Trinity. The pandemic version of the Impossible Trinity suggests that policymakers cannot simultaneously prevent the reintroduction of pandemic control measures while maintaining a functioning medical system and the complete freedom of individuals to choose whether or not to be vaccinated. Were they to occur, the imposition of renewed pandemic control measures or a dangerous rise in hospitalizations this fall would likely weigh on earnings expectations, at a time when income support for households negatively impacted by the pandemic will be withdrawn. The delta variant of COVID-19 is not vaccine-resistant, meaning that a delta-driven surge in hospitalizations this fall could delay – but not prevent – eventual asset purchase tapering and rate hikes from the Fed. 10-year Treasury yields are well below the fair value implied by a mid-2023 rate hike scenario, underscoring that the recent decline in long-maturity yields is overdone. The recent (slight) tick higher in China’s credit impulse is perhaps a sign that the worst of the credit slowdown has already occurred, but we do not expect a rising trend without a genuine shift toward a looser monetary policy stance. As such, a normalization in services spending in advanced economies remains the likely impulse for global growth over the coming year, at least over the coming 3-6 months. On a 12-month time horizon, we would recommend that investors position for the underperformance of financial assets that are negatively correlated with long-maturity government bond yields. However, for investors more focused on the near term, we would note the potential for further underperformance of cyclical sectors, value stocks, international equities, and most global ex-US currencies versus the US dollar – depending heavily on the evolution of the medical situation in the US and the subsequent response from policymakers. Feature Since we published our last report, progress made on the path to a post-pandemic state and the return to pre-COVID economic conditions have been mixed. Encouragingly, Chart I-1 highlights that the share of people who have received at least one dose of COVID-19 vaccine continues to rise outside of Africa, which continues to be impacted by India’s ban on vaccine exports. By the end of September, at least a quarter of the world’s population will have been fully vaccinated against COVID-19, and many more will have received at least one dose. Pfizer’s plan to request emergency authorization for its vaccine for children aged 5-11 by October also stands to raise total vaccination rates in advanced economies even further by the end of the year. In addition, Chart I-2 presents further evidence that the relationship between new cases of COVID-19 and hospitalization has truly been altered. The chart shows that the number of patients in UK hospitals is much lower than what would be implied by the number of new cases, which itself now appears to have peaked at a lower level than that of January. Given that the strain on the medical system is the dominant constraint facing policymakers, a modest rise in hospitalizations implies a durable end to pandemic restrictions and a return to economic normality. Chart I-1Global Vaccination Progress Continues Global Vaccination Progress Continues Global Vaccination Progress Continues Chart I-2Vaccines Have Truly Altered The Relationship Between Cases And Hospitalizations Vaccines Have Truly Altered The Relationship Between Cases And Hospitalizations Vaccines Have Truly Altered The Relationship Between Cases And Hospitalizations   However, the risk from the delta variant appears to be higher in the US than in the UK, due to a lower level of vaccine penetration. Only 56% of the US population has received at least one dose of a COVID-19 vaccine, compared with 67% in Israel, 69% in the UK, and 71% in Canada. And thus far, there has been no meaningful change in the pace of vaccination in the US in response to the threat from the delta variant, despite recent exhortations from politicians and media personalities from both sides of the political spectrum. The Impossible Trinity: Pandemic Edition Last year, most investors would have said that the existence of a safe and effective vaccine would likely be enough to durably end the pandemic. But given the development of more dangerous variants of the disease, and the existence of vaccine hesitancy in many countries, policymakers now face a trilemma that is conceptually similar to the concept of the “Impossible Trinity” as described by Mundell and Fleming. The upper portion of Chart I-3 illustrates the standard view of the Impossible Trinity, which posits that policymakers must choose one side of the triangle, while foregoing the opposite economic attribute. For example, most modern economies have chosen “B,” gaining the free flow of capital and independent monetary policy by giving up a fixed exchange rate regime (and allowing currency volatility). By contrast, Hong Kong has chosen side “A,” meaning that its monetary policy is driven by the Federal Reserve in exchange for a pegged currency and an open capital account. The lower portion of Chart I-3 presents the pandemic version of the trilemma, which sees policymakers having to choose two of these three outcomes: No economically-damaging pandemic control restrictions placed on society A functioning medical system The complete freedom of individuals to choose whether or not to be vaccinated Chart I-3Variants And Vaccine Hesitancy Have Created A Difficult Choice For Policymakers August 2021 August 2021 In reality, the pandemic version of the Impossible Trinity is likely to be resolved in a fashion similar to how China views the original trilemma,1 which is to distribute a 200% “adoption rate” among the three competing choices. In essence, this means that policymakers will likely partially adopt all three measures with a degree of intensity that will change over time in response to the prevailing circumstances. Chart I-4No Sign Yet Of A Pickup In US Vaccination Rates No Sign Yet Of A Pickup In US Vaccination Rates No Sign Yet Of A Pickup In US Vaccination Rates But Chart I-4 is a clear example of the differences in approach adopted by the US in response to vaccine hesitancy compared to other. So far, attempts to convince vaccine-hesitant Americans to get their shot have relied mostly on “carrot” approaches in an attempt to preserve individual freedom of choice, i.e. side “B” in Chart I-3. As noted above, these measures, so far, have failed, as there has been no noticeable uptick in the pace of vaccine doses administered in the US over the past month. By contrast, France, like several other countries, has begun to use “stick” approaches that push it more toward side “A” of the trilemma. In mid-July, French President Emmanuel Macron announced that French citizens who want to visit cafes, bars or shopping centers must show proof of vaccination or a negative test result. The policy also mandated that French health care and nursing home workers must be vaccinated. The result was a sharp, and thus far sustained, uptick in the pace of doses administered. For equity investors, the risk is that the politically contentious nature of vaccine mandates in the US will cause policymakers to acquiesce to renewed pandemic control measures this fall if the delta variant continues to spread widely over the coming few months (as seems likely). Alternatively, policymakers may allow a dangerous increase in hospitalizations, but this would merely postpone the imposition of control measures – and they would be more severe once reintroduced. Thus, there is a legitimate risk that the spread of the delta variant in the US does weigh on earnings expectations, especially for consumer-oriented services companies, at a time when income support for households negatively impacted by the pandemic will be withdrawn. Bond Yields, Delta, And Slowing Growth Momentum Chart I-5Growth Momentum Has Slowed... Growth Momentum Has Slowed... Growth Momentum Has Slowed... Of course, many investors would point to the significant decline in US 10-year bond yields since mid-March as having already acted in response to waning growth momentum. For example, the peak in US bond yields coincided with the March peak in the ISM manufacturing PMI, as well as a meaningful shift lower in the US economic surprise index (Chart I-5). Without a soaring inflation surprise index, the overall economic surprise index for the US would likely already be negative. The takeaway for some investors has been that a decline in yields has been normal given that the economy has passed its point of maximum strength. But there are two aspects of this narrative that do not accord with the data. First, Chart I-6 highlights that growth is peaking from an extremely strong pace, making it difficult to justify the magnitude of the decline in long-term yields over the past few months. And second, Chart I-7 highlights that the decline in the US 10-year yield closely corresponds to delta variant developments in the US. The chart shows that the 10-year yield broke below 1.5% shortly after the effective US COVID-19 reproduction rate (“R0”) began to rise, and the significant decline in yields over the past month began once R0 rose above 1. Chart I-7 does suggest that yields have reacted in response to the growth outlook, but in a different way than the “maximum strength” narrative suggests. Chart I-6…But Growth Itself Remains Quite Strong August 2021 August 2021 Chart I-7The Yield Decline Over The Past Month Seems Related To Delta The Yield Decline Over The Past Month Seems Related To Delta The Yield Decline Over The Past Month Seems Related To Delta Chart I-810-Year Yields Are Too Low, Even If Variants Delay The Fed 10-Year Yields Are Too Low, Even If Variants Delay The Fed 10-Year Yields Are Too Low, Even If Variants Delay The Fed While we can identify the apparent trigger for the decline in bond yields since mid-March, we do not agree that the decline is fundamentally justified. The delta variant of COVID-19 is not vaccine-resistant, meaning that a delta-driven surge in hospitalizations this fall could delay – but not prevent – eventual asset purchase tapering and rate hikes from the Fed. For example, Chart I-8 highlights that the 10-year yield is now 60 basis points below its fair value level in a scenario in which the Fed only begins to raise interest rates in mid-2023, underscoring that the recent decline in yields is overdone. And, although it is also true that market-based measures of inflation compensation have eased from their May highs, we have noted in previous reports that the Fed’s reaction function is almost exclusively driven by progress in the labor market back toward “maximum employment” levels – not inflation. Chart I-9 highlights that US real output per worker has grown at a much faster pace since the onset of the pandemic than what occurred on average over the past four economic recoveries, reflecting the success that US fiscal policy has had in supporting aggregate demand as well as constraints on labor supply in services industries. These factors will wane in intensity over the coming year, suggesting that real output per worker is unlikely to rise meaningfully further over that time horizon. Based on consensus market expectations for growth as well as the Fed’s most recent forecasts, a flat trend in real output per worker over the coming year would imply that the employment gap will be closed by Q2 of next year. This would be consistent with the recent trend in high frequency mobility data, such as US air traveler throughput and public transportation use in New York City (Chart I-10), the epicenter of the negative impact on urban core services employment stemming from the pandemic “work from home” effect. Chart I-9Real Output Per Worker Unlikely To Rise Much Further Over The Coming Year Real Output Per Worker Unlikely To Rise Much Further Over The Coming Year Real Output Per Worker Unlikely To Rise Much Further Over The Coming Year Chart I-10High-Frequency Data Points To A Closed Jobs Gap By Mid-2022 High-Frequency Data Points To A Closed Jobs Gap By Mid-2022 High-Frequency Data Points To A Closed Jobs Gap By Mid-2022   A closed employment gap by the middle of next year would imply that the Fed will begin to raise rates sometime in 2H 2022. Even if this were delayed by several months due to delta, Chart I-8 illustrated that 10-year Treasury yields are still too low. No Help From China If the spread of the delta variant over the coming few months does temporarily weigh on developed market economic activity via renewed pandemic control measures, investors should note that the lack of a countervailing growth impulse from China may act as an aggravating factor. Chart I-11 highlights that China’s PMI remains persistently below its 12-month trend, as it has tended to do following a decline in China’s credit impulse. And while some investors were hoping that the PBOC’s recent cut to the reserve requirement ratio represented a pivot in Chinese monetary policy towards sustained easing, Chart I-12 highlights that the 3-month repo rate remains well off its low from last year – and is only modestly lower than it was on average during most of the 2018/2019 period. Chart I-11China Is Slowing, And Policy Has Not Yet Reversed Course August 2021 August 2021 Chart I-12The Recent RRR Cut Was Not The Start Of A Dovish PBOC Shift The Recent RRR Cut Was Not The Start Of A Dovish PBOC Shift The Recent RRR Cut Was Not The Start Of A Dovish PBOC Shift   The recent (slight) tick higher in China’s credit impulse is perhaps a sign that the worst of the credit slowdown has already occurred, but we do not expect a rising trend without a genuine shift toward a looser monetary policy stance. As such, a normalization in services spending in advanced economies remains the likely impulse for global growth over the coming year, at least over the coming three to six months. Investment Conclusions Chart I-13Assets That Benefit From Lower Yields May Remain Well-Bid In The Near Term Assets That Benefit From Lower Yields May Remain Well-Bid In The Near Term Assets That Benefit From Lower Yields May Remain Well-Bid In The Near Term The unprecedented nature of the pandemic, as well as the unclear impact the delta variant will have given prevailing rates of vaccination in advanced economies, has clouded the near-term economic outlook. It is unlikely that the delta variant of SARS-COV-2 will have a long-lasting impact on economic activity in advanced economies, but it does have the potential to cause the temporary reintroduction of some pandemic restrictions and, thus, modestly delay the transition to a post-pandemic state. While long-term government bond yields are set to rise on a 12-month time horizon, financial assets that are negatively correlated with long-term bond yields could remain well-bid over the next few months. Chart I-13 highlights that cyclical equity sectors have underperformed defensive equity sectors over the past month, and banks have underperformed the overall index. The correlation between long-maturity real Treasury yields and the relative performance of value and growth stocks has also held up, with growth stocks outperforming since the end of March. Global ex-US equities have also underperformed US stocks, and the dollar has modestly risen. On a 12-month time horizon, we would recommend that investors position for a reversal of all these recent moves. However, for investors more focused on the near term, we would note the potential for further underperformance of cyclical sectors, value stocks, international equities, and most global ex-US currencies versus the US dollar – depending heavily on the evolution of the medical situation in the US and the subsequent response from policymakers. This underscores that cyclical investment strategy will be even more data dependent than usual throughout the second half of the calendar year. The pace of nonfarm payrolls growth in the US remains the single most important data release driving US monetary policy, and investors should especially focus on whether jobs growth this fall is consistent with the Fed’s maximum employment objective, as the impact of the delta variant becomes clearer, as constraints to labor supply are removed, and as employees progressively return to work. Jonathan LaBerge, CFA Vice President The Bank Credit Analyst July 29, 2021 Next Report: August 26, 2021 II. The Social Media Magnification Effect: Austerity, Populism, And Slower Growth Investors should view social media as a technological innovation with negative productivity growth. Social media has contributed to policy mistakes – such as fiscal austerity and protectionism – that have acted as shocks to aggregate demand over the past 15 years. The cyclical component of productivity was long lasting in nature during the last economic expansion. Forces that negatively impact economic growth but do not change the factors of production necessarily reduce measured productivity, and repeated policy mistakes strongly contributed to the slow growth profile of the last economic cycle. Political polarization in a rapidly changing world is the root cause of these policy shocks, but social media likely facilitated and magnified them. The risks of additional mistakes from populism remain present, even before considering other risks to society from social media: a reduction in mental health among young social media users, and the role that social media has played in spreading misinformation. A potential revival in protectionist sentiment is a risk to a constructive cyclical view that we will be closely monitoring over the coming 12-24 months. Investors with concentrated positions in social media stocks should be aware of the potential idiosyncratic risks facing these companies from the public’s impression of the impact of social media on society – especially if social media companies come to be widely associated with political gridlock, the polarization of society, and failed economic policies (as already appears to be the case). Investors should view social media as a technological innovation with negative productivity growth. Social media has contributed to policy mistakes – such as fiscal austerity and protectionism – that have acted as shocks to aggregate demand over the past 15 years. Political polarization in a rapidly changing world is the root cause of these policy shocks, but social media likely facilitated and magnified them. While the risk of premature fiscal consolidation appears low today compared to the 2010-14 period, the pandemic and its aftermath could force the Biden administration or Congressional Democrats toward protectionist or otherwise populist actions over the coming year in the lead up to the 2022 mid-term elections. The midterms, for their part, are expected to bring gridlock back into US politics, which could remove fiscal options should the economy backslide. Frequent shocks during the last economic expansion reinforced the narrative of secular stagnation. In the coming years, any additional policy shocks following a return to economic normality will again be seen by both investors and the Fed as strong justification for low interest rates – despite the case for cyclically and structurally higher bond yields. In addition, investors with concentrated positions in social media companies should take seriously the long-term idiosyncratic risks facing these stocks. These risks stem from the public’s impression of the impact of social media on society, particularly if social media comes to be widely associated with political gridlock, the polarization of society, and failed economic policies. A Brief History Of Social Media The earliest social networking websites date back to the late 1990s, but the most influential social media platforms, such as Facebook and Twitter, originated in the mid-2000s. Prior to the advent of modern-day smartphones, user access to platforms such as Facebook and Twitter was limited to the websites of these platforms (desktop access). Following the release of the first iPhone in June 2007, however, mobile social media applications became available, allowing users much more convenient access to these platforms. Charts II-1 and II-2 highlight the impact that smartphones have had on the spread of social media, especially since the release of the iPhone 3G in 2008. In 2006, Facebook had roughly 12 million monthly active users; by 2009, this number had climbed to 360 million, growing to over 600 million the year after. Twitter, by contrast, grew somewhat later, reaching 100 million monthly active users in Q3 2011. Chart II-1Facebook: Monthly Active Users August 2021 August 2021 Chart II-2Twitter: Monthly Active Users Worldwide August 2021 August 2021   Social media usage is more common among those who are younger, but Chart II-3 highlights that usage has risen over time for all age groups. As of Q1 2021, 81% of Americans aged 30-49 reported using at least one social media website, compared to 73% of those aged 50-64 and 45% of those aged 65 and over. Chart II-4 highlights that the usage of Twitter skews in particular toward the young, and that, by contrast, Facebook and YouTube are the social media platforms of choice among older Americans. Chart II-3A Sizeable Majority Of US Adults Regularly Use Social Media A Sizeable Majority Of US Adults Regularly Use Social Media A Sizeable Majority Of US Adults Regularly Use Social Media Chart II-4Older Americans Use Facebook Far More Than Twitter August 2021 August 2021 Chart II-5Social Media Has Changed The Way People Consume News August 2021 August 2021 As a final point documenting the development and significance of social media, Chart II-5 highlights that more Americans now report consuming news often (roughly once per day) from a smartphone, computer, or tablet other than from television. Radio and print have been completely eclipsed as sources of frequent news. The major news publications themselves are often promoted through social media, but the rise of the Internet has weighed heavily on the journalism industry. Social media has, for better and for worse, enabled the rapid proliferation of alternative news, citizen journalism, rumor, conspiracy theories, and foreign disinformation. The Link Between Social Media And Post-GFC Austerity Following the 2008-2009 global financial crisis (GFC), there have been at least five deeply impactful non-monetary shocks to the US and global economies that have contributed to the disconnection between growth and interest rates: A prolonged period of US household deleveraging from 2008-2014 The Euro Area sovereign debt crisis Fiscal austerity in the US, UK, and Euro Area from 2010 – 2012/2014 The US dollar / oil price shock of 2014 The rise of populist economic policies, such as the UK decision to leave the European Union, and the US-initiated trade war of 2018-2019. Among these shocks to growth, social media has had a clear impact on two of them. In the case of austerity in the aftermath of the Great Recession, a sharp rise in fiscal conservatism in 2009 and 2010, emblematized by the rise of the US Tea Party, profoundly affected the 2010 US midterm elections. It is not surprising that there was a fiscally conservative backlash following the crisis: the US budget deficit and debt-to-GDP ratio soared after the economy collapsed and the government enacted fiscal stimulus to bail out the banking system. And midterm elections in the US often lead to significant gains for the opposition party However, Tea Party supporters rapidly took up a new means of communicating to mobilize politically, and there is evidence that this contributed to their electoral success. Chart II-6 illustrates that the number of tweets with the Tea Party hashtag rose significantly in 2010 in the lead-up to the election, which saw the Republican Party take control of the House of Representatives as well as the victory of several Tea Party-endorsed politicians. Table II-1 highlights that Tea Party candidates, who rode the wave of fiscal conservatism, significantly outperformed Democrats and non-Tea Party Republicans in the use of Twitter during the 2010 campaign, underscoring that social media use was a factor aiding outreach to voters. Chart II-6Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Tea Party Supporters Rapidly Adopted Social Media To Mobilize Politically Table II-1Tea Party Candidates Significantly Outperformed In Their Use Of Social Media August 2021 August 2021   And while it is more difficult to analyze the use and impact of Facebook by Tea Party candidates and supporters owing to inherent differences in the structure of the Facebook platform, interviews with core organizers of both the Tea Party and Occupy Wall Street movements have noted that activists in these ideologically opposed groups viewed Facebook as the most important social networking service for their political activities.2 Under normal circumstances, we agree that fiscal policy should be symmetric, with reduced fiscal support during economic expansions following fiscal easing during recessions. But in the context of multi-year household deleveraging, the fiscal drag that occurred in following the 2010 midterm elections was clearly a policy mistake. This mistake occurred partially under full Democratic control of government and especially under a gridlocked Congress after 2010. Chart II-7 highlights that the contribution to growth from government spending turned sharpy negative in 2010 and continued to subtract from growth for some time thereafter. In addition, panel of Chart II-7 highlights that the US economic policy uncertainty index rose in 2010 after falling during the first year of the recovery, reaching a new high in 2011 during the Tea Party-inspired debt ceiling crisis. Chart II-7The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake The Fiscal Drag That Followed The 2010 Midterm Elections Was A Clear Policy Mistake Chart II-8Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile Policy Mistakes Significantly Contributed To Last Cycle's Subpar Growth Profile In addition to the negative impact of government spending on economic growth, this extreme uncertainty very likely damaged confidence in the economic recovery, contributing to the subpar pace of growth in the first half of the last economic expansion. Chart II-8 highlights the weak evolution in real per capita GDP from 2009-2019 compared with previous economic cycles, which was caused by a prolonged household balance sheet recovery process that was made worse by policy mistakes. To be sure, the UK and the EU did not have a Tea Party, and yet political elites imposed fiscal austerity. It is also the case that President Obama was the first president to embrace social media as a political and public relations tool. So it cannot be said that either social media or the Republican Party are uniquely to blame for the policy mistakes of that era. But US fiscal policy would have been considerably looser in the 2010s if not for the Tea Party backlash, which was partly enabled by social media. Too tight of fiscal policy in turn fed populism and produced additional policy mistakes down the road. From Fiscal Drag To Populism While social media is clearly not the root cause of the recent rise of populist policies, it has had a hand in bringing them about – in both a direct and indirect manner. The indirect link between social media use and the rise in populist policies has mainly occurred through the highly successful use of social media by international terrorist organizations (chiefly ISIL) and its impact on sentiment toward immigration in several developed market economies. Chart II-9Terrorism And Immigration Likely Contributed To Brexit Terrorism And Immigration Likely Contributed To Brexit Terrorism And Immigration Likely Contributed To Brexit Chart II-9 highlights that public concerns about immigration and race in the UK began to rise sharply in 2012, in lockstep with both the rise in UK immigrants from EU accession countries and a series of events: the Syrian refugee crisis, the establishment and reign of the Islamic State, and three major terrorist attacks in European countries for which ISIL claimed responsibility. Given that the main argument for “Brexit” was for the UK to regain control over its immigration policies, these events almost certainly increased UK public support for withdrawing from the EU. In other words, it is not clear that Brexit would have occurred (at least at that moment in time) without these events given the narrow margin of victory for the “leave” campaign. The absence of social media would not have prevented the rise of ISIL, as that occurred in response to the US’s precipitous withdrawal from Iraq. The inevitable rise of ISIL would still have generated a backlash against immigration. Moreover, fiscal austerity in the UK and EU also fed other grievances that supported the Brexit movement. But social media accelerated and amplified the entire process.  Chart II-10Brexit Weakened UK Economic Performance Prior To The Pandemic Brexit Weakened UK Economic Performance Prior To The Pandemic Brexit Weakened UK Economic Performance Prior To The Pandemic Chart II-10 presents fairly strong evidence that Brexit weakened UK economic performance relative to the Euro Area prior to the pandemic, with the exception of the 2018-2019 period. In this period Euro Area manufacturing underperformed during the Trump administration’s trade war as a result of its comparatively higher exposure to automobile production and its stronger ties to China. Panel 2 highlights that GBP-EUR fell sharply in advance of the referendum, and remains comparatively weak today. Turning to the US, Donald Trump’s election as US President in 2016 was aided by both the direct and indirect effects of social media. In terms of indirect effects, Trump benefited from similar concerns over immigration and terrorism that caused the UK to leave the EU: Chart II-11 highlights that terrorism and foreign policy were second and third on the list of concerns of registered voters in mid-2016, and Chart II-12 highlights that voters regarded Trump as the better candidate to defend the US against future terrorist attacks. Chart II-11Terrorism Ranked Highly As An Issue In The 2016 US Election August 2021 August 2021 Chart II-12Voters Regarded Trump As Better Equipped To Defend Against Terrorism August 2021 August 2021 Trump’s election; and the enactment of populist policies under his administration, were directly aided by Trump’s active use of social media (mainly Twitter) to boost his candidacy. Chart II-13 highlights that there were an average of 15-20 tweets per day from Trump’s Twitter account from 2013-2015, and 80% of those tweets occurred before he announced his candidacy for president in June 2015. This strongly underscores that Trump mainly used Twitter to lay the groundwork for his candidacy as an unconventional political outsider rather than as a campaign tool itself, which distinguishes his use of social media from that of other politicians. In other words, new technology disrupted the “good old boys’ club” of traditional media and elite politics. Some policies of the Trump administration were positive for financial markets, and it is fair to say that Trump fired up animal spirits to some extent: Chart II-14 highlights that the Tax Cuts and Jobs Act caused a significant rise in stock market earnings per share. But the Trump tax cuts were a conventional policy pushed mostly by the Congressional leadership of the Republican Party, and they did not meaningfully boost economic growth. Chart II-15 highlights that, while the US ISM manufacturing index rose sharply in the first year of Trump’s administration, an uptrend was already underway prior to the election as a result of a significant improvement in Chinese credit growth and a recovery in oil prices after the devastating collapse that took place in 2014-2015. Chart II-13Trump Used Twitter To Lay The Groundwork For His Candidacy Trump Used Twitter To Lay The Groundwork For His Candidacy Trump Used Twitter To Lay The Groundwork For His Candidacy Chart II-14The Trump Tax Cuts A Huge Rise In Corporate Earnings The Trump Tax Cuts A Huge Rise In Corporate Earnings The Trump Tax Cuts A Huge Rise In Corporate Earnings   Chart II-15But The Tax Cuts Did Not Do Much To Boost Growth But The Tax Cuts Did Not Do Much To Boost Growth But The Tax Cuts Did Not Do Much To Boost Growth Similarly, Chart II-15 highlights that the Trump trade war does not bear the full responsibility of the significant slowdown in growth in 2019, as China’s credit impulse decelerated significantly between the passage of the Tax Cuts and Jobs Act and the onset of the trade war because Chinese policymakers turned to address domestic concerns. Chart II-16The Trade War Caused An Explosion In Global Trade Uncertainty The Trade War Caused An Explosion In Global Trade Uncertainty The Trade War Caused An Explosion In Global Trade Uncertainty But Chart II-16 highlights that the aggressive imposition of tariffs, especially between the US and China, caused an explosion in trade uncertainty even when measured on an equally-weighted basis (i.e., when overweighting trade uncertainty, in countries other than the US and China), which undoubtedly weighed on the global economy and contributed to a very significant slowdown in US jobs growth in 2019 (panel 2). Moreover, Chinese policymakers responded to the trade onslaught by deleveraging, which weighed on the global economy; and consolidating their grip on power at home. In essence, Trump was a political outsider who utilized social media to bypass the traditional media and make his case to the American people. Other factors contributed to his surprising victory, not the least of which was the austerity-induced, slow-growth recovery in key swing states. While US policy was already shifting to be more confrontational toward China, the Trump administration was more belligerent in its use of tariffs than previous administrations. The trade war thus qualifies as another policy shock that was facilitated by the existence of social media. Viewing Social Media As A Negative Productivity-Innovation A rise in fiscal conservatism leading to misguided austerity, the UK’s decision to leave the European Union, and the Trump administration’s trade war have represented significant non-monetary shocks to both the US and global economies over the past 12 years. These shocks strongly contributed to the subpar growth profile of the last economic expansion, as demonstrated above. Chart II-17Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Policy Mistakes, Partially Enabled By Social Media, Reduced Productivity During The Last Expansion Given the above, it is reasonable for investors to view social media as a technological innovation with negative productivity growth, given that it has facilitated policy mistakes during the last economic expansion. Chart II-17 underscores this point, by highlighting that multi-factor productivity growth has been extremely weak in the post-GFC environment. While productivity is usually driven by supply-side factors over the longer term, it has a cyclical component to it – and in the case of the last economic expansion, the cyclical component was long lasting in nature. Any forces negatively impacting economic growth that do not change the factors of production necessarily reduce measured productivity; it is for this reason that measured productivity declines during recessions; and policy mistakes negatively impact productivity growth. The Risk Of Aggressive Austerity Seems Low Today… Chart II-18State & Local Government Finances Are In Much Better Shape Today State & Local Government Finances Are In Much Better Shape Today State & Local Government Finances Are In Much Better Shape Today Fiscal austerity in the early phase of the last economic cycle was the first social media-linked shock that we identified, but the risk of aggressive austerity appears low today. Much of the fiscal drag that occurred in the aftermath of the global financial crisis happened because of insufficient financial support to state and local governments – and the subsequent refusal by Congress to authorize more aid. But Chart II-18 highlights that state and local government finances have already meaningfully recovered, on the back of bipartisan stimulus in 2020, while the American Rescue Plan provides significant additional funding. While it is true that US fiscal policy is set to detract from growth over the coming 6-12 months, this will merely reflect the unwinding of fiscal aid that had aimed to support household income temporarily lost, as a result of a drastic reduction in services spending. As we noted in last month’s report,3 goods spending will likely slow as fiscal thrust turns to fiscal drag, but services spending will improve meaningfully – aided not just by a post-pandemic normalization in economic activity, but also by the deployment of some of the sizable excess savings that US households have accumulated over the past year. Fiscal drag will also occur outside of the US next year. For example, the IMF is forecasting a two percentage point increase in the Euro Area’s cyclically-adjusted primary budget balance, which would represent the largest annual increase over the past two decades. But here too the reduction in government spending will reflect the end of pandemic-related income support, and is likely to occur alongside a positive private-sector services impulse. During the worst of the Euro Area sovereign debt crisis, the impact of austerity was especially acute because it was persistent, and it occurred while the output gap was still large in several Euro Area economies. Chart II-19 highlights that Euro Area fiscal consolidation from 2010-2013 was negatively correlated with economic activity during that period, and Chart II-20 highlights that, with the potential exception of Spain, this austerity does not appear to have led to subsequently stronger rates of growth. Chart II-19Euro Area Austerity Lowered Growth During The Consolidation Phase… August 2021 August 2021 Chart II-20…And Did Not Seem To Subsequently Raise Growth August 2021 August 2021   This experiment in austerity led the IMF to conclude that fiscal multipliers are indeed large during periods of substantial economic slack, constrained monetary policy, and synchronized fiscal adjustment across numerous economies.4 Similarly, attitudes about austerity have shifted among policymakers globally in the wake of the populist backlash. Given this, despite the significant increase in government debt levels that has occurred as a result of the pandemic, we strongly doubt that advanced economies will attempt to engage in additional austerity prematurely, i.e., before unemployment rates have returned close-to steady-state levels. …But The Risk Of Protectionism And Other Populist Measures Looms Large The role that social media has played at magnifying populist policies should be concerning for investors, especially given that there has been a rising trend towards populism over the past 20 years. In a recent paper, Funke, Schularick, and Trebesch have compiled a cross-country database on populism dating back to 1900, defining populist leaders as those who employ a political strategy focusing on the conflict between “the people” and “the elites.” Chart II-21 highlights that the number of populist governments worldwide has risen significantly since the 1980s and 1990s, and Chart II-22 highlights that the economic performance of countries with populist leaders is clearly negative. Chart II-21Populism Has Been On The Rise For The Past 30 Years August 2021 August 2021 The authors found that countries’ real GDP growth underperformed by approximately one percentage point per year after a populist leader comes to power, relative to both the country’s own long-term growth rate and relative to the prevailing level of global growth. To control for the potential causal link between economic growth and the rise of populist leaders, Chart II-23 highlights the results of a synthetic control method employed by the authors that generates a similar conclusion to the unconditional averages shown in Chart II-22: populist economic policies are significantly negative for real economic growth. Chart II-22Populist Leaders Are Clearly Growth Killers Even After… August 2021 August 2021 Chart II-23… Controlling For The Odds That Weak Growth Leads To Populism August 2021 August 2021 Chart II-24Inequality: The Most Important Structural Cause Of Populism And Polarization Inequality: The Most Important Structural Cause Of Populism And Polarization Inequality: The Most Important Structural Cause Of Populism And Polarization This is especially concerning given that wealth and income inequality, perhaps the single most important structural cause of rising populism and political polarization, is nearly as elevated as it was in the 1920s and 1930s (Chart II-24). This trend, at least in the US, has been exacerbated by a decline in public trust of mainstream media among independents and Republicans that began in the early 2000s and helped to fuel the public’s adoption of alternative news and social media. The decline in trust clearly accelerated as a result of erroneous reporting on what turned out to be nonexistent weapons of mass destruction in Iraq and other controversies of the Bush administration. Chart II-21 showed that the rise in populism has also yet to abate, suggesting that social media has the potential to continue to amplify policy mistakes for the foreseeable future. It is not yet clear what economic mistakes will occur under the Biden administration, but investors should not rule out the possibility of policies that are harmful for growth. The likely passage of a bipartisan infrastructure bill or a partisan reconciliation bill in the second half of this year will most likely be the final word on fiscal policy until at least 2025,5 underscoring that active fiscal austerity is not likely a major risk to investors. Spending levels will probably freeze after 2022: Republicans will not be able to slash spending, and Democrats will not be able to hike spending or taxes, if Republicans win at least one chamber of Congress in the midterms (as is likely). Biden has preserved the most significant of Trump’s protectionist policies by maintaining US import tariffs against China, and the lesson from the Tea Party’s surge following the global financial crisis is that major political shifts, magnified by social media, can manifest themselves as policy with the potential to impact economic activity within a two-year window. Attitudes toward China have shifted negatively around the world because of deindustrialization and now the pandemic.6 White collar workers in DM countries have clearly fared better during lockdowns than those of lower-income households. This has created extremely fertile ground for a revival in populist sentiment, which could force the Biden administration or Congressional Democrats toward protectionist or otherwise populist actions over the coming year, in the lead up to the 2022 mid-term elections. Investment Conclusions In this report, we have documented the historical link between social media, populism, and policy mistakes during the last economic expansion. It is clear that neither social media nor even populism is solely responsible for all mistakes – the UK’s and EU’s ill-judged foray into austerity was driven by elites. Furthermore, we have not addressed in this report the impact of populism on actions of emerging markets, such as China and Russia, whose own behavior has dealt disinflationary blows to the global economy. Nevertheless, populism is a potent force that clearly has the power to harness new technology and deliver shocks to the global economy and financial markets. The risks of additional mistakes from populism are still present, and that is even before considering other risks to society from social media: a reduction in mental health among young social media users, and the role that social media has played in spreading misinformation – contributing to the vaccine hesitancy in some DM countries that we discussed in Section 1 of our report. Two investment conclusions emerge from our analysis. First, we noted in our April report that there is a chance that investor expectations for the natural rate of interest (“R-star”) will rise once the economy normalizes post-pandemic, but that this will likely not occur as long as investors continue to believe in the narrative of secular stagnation. Despite the fact that the past decade’s shocks occurred against the backdrop of persistent household deleveraging (which has ended in the US), these shocks reinforced that narrative, and any additional policy shocks following a return to economic normality will again be seen by both investors and the Fed as strong justification for low interest rates. Thus, while the rapid closure of output gaps in advanced economies over the coming year argues for both cyclically and structurally higher bond yields, a revival in protectionist sentiment is a risk to this view that we will be closely monitoring over the coming 12-24 months. Chart II-25The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market The Underperformance Of Social Media Would Not Excessively Weigh On The Broad Market Second, for tech investors, the bipartisan shift in public sentiment to become more critical of social media companies is gradually becoming a real risk, potentially affecting user growth. Based solely on Facebook, Twitter, Pinterest, and Snapchat, social media companies do not account for a very significant share of the overall equity market (Chart II-25), suggesting that the impact of a negative shift in sentiment toward social media companies would not be an overly significant event for equity investors in general. Chart II-25 highlights that the share of social media companies as a percent of the broad tech sector rises if Google is included; YouTube accounts for less than 15% of Google’s total advertising revenue, however, suggesting modest additional exposure beyond the solid line in Chart II-25. Still, investors with concentrated positions in social media stocks should be aware of the potential idiosyncratic risks facing social media companies as a result of the public’s impression of the impact of social media on society. If social media companies come to be widely associated with political gridlock, the polarization of society, and failed economic policies (as already appears to be the case), then the fundamental performance of these stocks is likely to be quite poor regardless of whether or not tech companies ultimately enjoy a relatively friendly regulatory environment under the Biden administration. Jonathan LaBerge, CFA Vice President The Bank Credit Analyst III. Indicators And Reference Charts BCA’s equity indicators highlight that the “easy” money from expectations of an eventual end to the pandemic have already been made. Our technical, valuation, and sentiment indicators are very extended, highlighting that investors should expect positive but modest returns from stocks over the coming 6-12 months. Our monetary indicator has aggressively retreated from its high last year, reflecting a meaningful recovery in government bond yields since last August. The indicator still remains above the boom/bust line, however, highlighting that monetary policy remains supportive for risky asset prices. Forward equity earnings are pricing in a substantial further rise in earnings per share, but for now there is no meaningful sign of waning forward earnings momentum. Net revisions remain very strong, and positive earnings surprises have risen to their highest levels on record. Within a global equity portfolio, global ex-US equities have underperformed alongside cyclical sectors, banks, and value stocks more generally. On a 12-month time horizon, we would recommend that investors position for the underperformance of financial assets that are negatively correlated with long-maturity government bond yields. But investors more focused on the near term, we would note the potential for further underperformance of cyclical sectors, value stocks, international equities, and most global ex-US currencies versus the US dollar – depending heavily on the evolution of the medical situation in the US and the subsequent response from policymakers. The US 10-Year Treasury yield has fallen sharply since mid-March. This decline was initially caused by waning growth momentum, but has since morphed into concern about the impact of the delta variant of SARS-COV-2 and the implications for US monetary policy. 10-year Treasury yields are well below the fair value implied by a mid-2023 rate hike scenario, underscoring that the recent decline in long-maturity yields is overdone. The extreme rise in some commodity prices over the past several months has eased. Lumber prices have normalized, whereas industrial metals have moved mostly sideways since late-April and agricultural prices remain 13% below their early-May high. We had previously argued that a breather in commodity prices was likely at some point over the coming several months, and we would expect further declines in some commodity prices as supply chains normalize, labor supply recovers, and Chinese demand for metals slows. US and global LEIs remain very elevated, but are starting to roll over. Our global LEI diffusion index has declined very significantly, but this likely reflects the outsized impact of a few emerging market countries (whose vaccination progress is still lagging). Still-strong leading and coincident indicators underscore that the global demand for goods is robust, and that output is below pre-pandemic levels in most economies because of very weak services spending. The latter will recover significantly at some point over the coming year, as social distancing and other pandemic control measures disappear. EQUITIES: Chart III-1US Equity Indicators US Equity Indicators US Equity Indicators Chart III-2Willingness To Pay For Risk Willingness To Pay For Risk Willingness To Pay For Risk Chart III-3US Equity Sentiment Indicators US Equity Sentiment Indicators US Equity Sentiment Indicators   Chart III-4US Stock Market Breadth US Stock Market Breadth US Stock Market Breadth Chart III-5US Stock Market Valuation US Stock Market Valuation US Stock Market Valuation Chart III-6US Earnings US Earnings US Earnings Chart III-7Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance Chart III-8Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance Global Stock Market And Earnings: Relative Performance   FIXED INCOME: Chart III-9US Treasurys And Valuations US Treasurys And Valuations US Treasurys And Valuations Chart III-10Yield Curve Slopes Yield Curve Slopes Yield Curve Slopes Chart III-11Selected US Bond Yields Selected US Bond Yields Selected US Bond Yields Chart III-1210-Year Treasury Yield Components 10-Year Treasury Yield Components 10-Year Treasury Yield Components Chart III-13US Corporate Bonds And Health Monitor US Corporate Bonds And Health Monitor US Corporate Bonds And Health Monitor Chart III-14Global Bonds: Developed Markets Global Bonds: Developed Markets Global Bonds: Developed Markets Chart III-15Global Bonds: Emerging Markets Global Bonds: Emerging Markets Global Bonds: Emerging Markets   CURRENCIES: Chart III-16US Dollar And PPP US Dollar And PPP US Dollar And PPP Chart III-17US Dollar And Indicator US Dollar And Indicator US Dollar And Indicator Chart III-18US Dollar Fundamentals US Dollar Fundamentals US Dollar Fundamentals Chart III-19Japanese Yen Technicals Japanese Yen Technicals Japanese Yen Technicals Chart III-20Euro Technicals Euro Technicals Euro Technicals Chart III-21Euro/Yen Technicals Euro/Yen Technicals Euro/Yen Technicals Chart III-22Euro/Pound Technicals Euro/Pound Technicals Euro/Pound Technicals   COMMODITIES: Chart III-23Broad Commodity Indicators Broad Commodity Indicators Broad Commodity Indicators Chart III-24Commodity Prices Commodity Prices Commodity Prices Chart III-25Commodity Prices Commodity Prices Commodity Prices Chart III-26Commodity Sentiment Commodity Sentiment Commodity Sentiment Chart III-27Speculative Positioning Speculative Positioning Speculative Positioning   ECONOMY: Chart III-28US And Global Macro Backdrop US And Global Macro Backdrop US And Global Macro Backdrop Chart III-29US Macro Snapshot US Macro Snapshot US Macro Snapshot Chart III-30US Growth Outlook US Growth Outlook US Growth Outlook Chart III-31US Cyclical Spending US Cyclical Spending US Cyclical Spending Chart III-32US Labor Market US Labor Market US Labor Market Chart III-33US Consumption US Consumption US Consumption Chart III-34US Housing US Housing US Housing Chart III-35US Debt And Deleveraging US Debt And Deleveraging US Debt And Deleveraging   Chart III-36US Financial Conditions US Financial Conditions US Financial Conditions Chart III-37Global Economic Snapshot: Europe Global Economic Snapshot: Europe Global Economic Snapshot: Europe Chart III-38Global Economic Snapshot: China Global Economic Snapshot: China Global Economic Snapshot: China   Jonathan LaBerge, CFA Vice President The Bank Credit Analyst Footnotes 1 Please see China Investment Strategy Weekly Report “Moderate Releveraging And Currency Stability: An Impossible Dream?” dated September 5, 2018, available at cis.bcaresearch.com 2 Grassroots Organizing in the Digital Age: Considering Values and Technology in Tea Party and Occupy Wall Street by Agarwal, Barthel, Rost, Borning, Bennett, and Johnson, Information, Communication & Society, 2014. 3 Please see The Bank Credit Analyst “July 2021,” dated June 24, 2021, available at bca.bcaresearch.com 4 “Are We Underestimating Short-Term Fiscal Multipliers?” IMF World Economic Outlook, October 2012 5 Please see US Political Strategy Outlook "Third Quarter Outlook 2021: Game Time," dated June 30, 2021, available at usps.bcaresearch.com 6 “Unfavorable Views of China Reach Historic Highs in Many Countries,” PEW Research Center, October 2020.

Highlights The Auto and Components industry group is in the middle of a momentous transition to electric and autonomous-vehicle manufacturing thanks to technological advances in battery storage, AI, and radars. The entire EV cohort will benefit from government support for decarbonization, the preferences of millennials for green tech, and cutting-edge technological innovation. Further, the price of gas has recently nearly doubled, average US vehicles are more than 12 years old, while most US consumers came out of recession unscathed. Is this the time for consumers to upgrade to EVs? Legacy Automakers are to be primary beneficiaries of the theme: Higher earnings and greater economic visibility regarding EV transition should lead to further rerating of the industry group. These carmakers are also turning into Growth stocks as an expected surge in earnings is far in the future. Tesla has already had an amazing run. Even though it is 30% down from its peak, it remains expensive, and much of the growth expectations are already baked into the price. We recommend staying neutral on Tesla as it is a “cult” stock and a surge “to the moon” is not out of question. Ecosystem: The surge in EV capex and R&D spending will give a boost to the entire supply chain, which consists of chip manufacturers, battery and lidar R&D, part manufacturers, and charging networks. Many of these companies are still small. An ETF may be the best way to capture these names. Existing EV themed ETFs may not be perfect: Many have holdings that are way too broad and over-diversified, most invest outside of the US. Yet, these are the convenient vehicles to capture the theme and provide exposure to the entire EV cohort. Some of the best-known ETFs are ARKQ, DRIV, IDRV, and KARS. We believe that the EV/AV theme will outperform the US equity market over the 3-12 months horizon. Overweighting EV is also consistent with our call to rotate into Growth as higher rates and the pick up in inflation appear to be priced in. Feature Auto And Components Industry Delivers Historical Technological Advances The auto industry is undergoing a monumental shift towards electric vehicles (EV) and autonomous driving thanks to technological advances in battery storage, AI, and radars. Transition to EV is happening at a fast pace: According to IEA, the number of EVs on the road increased from about 17,000 vehicles in 2010 to 7.9 million in 2019. Autonomous vehicles (AV) are still in a testing stage, but most automakers promise to put them on the road within the next decade. LMC Automotive forecasts that that by 2031, EVs will reach 17 million units, while AVs will approach one million in 2025. Investors are cheering on this transition: The MSCI USA Auto and Components sector has outperformed MSCI USA by over 300% (408% vs. 90%) since the pandemic trough in March 2020. The EV-themed ETF DRIV outperformed by 95%. In this Special Report we provide an overview of the EV and AV industries and their emerging ecosystem. It is structured as following: First, we discuss the tailwind for transitioning towards EV. Second, we identify the key players in the EVs and AVs space. Third, we look at ways that investors can best get exposure to the EV theme and provide an investment outlook for the space. EV Tailwinds: Biden Administration Pushes Toward “Clean Tech”, Millennials Cheer The Biden administration’s push toward decarbonization of the economy will further accelerate transition towards EVs with a host of fiscal, infrastructure, and executive actions, such as tax credits, scrappage incentives, and government purchases. The White House’s $1.7-2.3 trillion infrastructure bill – which is highly likely to pass by the end of the year with green initiatives intact – includes a $15 billion buildout of 500,000 charging stations (there are currently only 27,000 in operation). Executive action by President Biden has also tightened fuel-economy standards. Individual states like California have committed to zero-emission standards by 2030. Add this to the emerging preferences of millennials for clean tech, and fully electric vehicles are expected to account for 33% of all US auto sales by 2030. Of course, there are EV adoption challenges: EV batteries remain expensive, adding approximately $10,000 to the price of a vehicle. Charging infrastructure is sparse, while EVs have relatively limited driving ranges and long charging times. But even these obstacles will be resolved sooner rather then later. According to Cathie Wood, CEO and CIO of the ARK (thematic) ETFs, EVs will approach sticker parity with gas-powered cars as soon as 2023. And there are a number of new entrants developing charging networks. Even driving ranges are increasing with Lucid promising 500 miles per charge (Chart 1). Key Players In The US Market Tesla: Enormous Potential But Competition Is Catching Up Tesla is a pioneer of battery electric vehicles (BEV), rewarded with sky-high valuations and deep pockets. Its stock had a spectacular run, rising ten-fold in two years, getting ahead of itself: It is down 30% from its January peak. So what is the bull case for Tesla that justifies the multiples, and may be considered a catalyst for future outperformance? After all, manufacturing of EVs is likely to become a highly competitive and low-margin business. Tesla has four unique advantages that constitute its competitive “moat”: An extensive supercharger network in the US and worldwide. Its push towards increased vertical integration into capabilities such as battery manufacturing and other key enabling technologies would allow it to maintain a technological edge over competition, as well as protect the company against any potential supply-chain disruptions. A mobility ecosystem, especially of data and network, turning the car into “mobile real estate”, powered by the cloud and fueled ultimately by thousands of exabytes of data. A host of auxiliary businesses: Energy, insurance, mobility/rideshare, network services and third-party battery supply. However, despite its tremendous long-term potential, Tesla has only recently become profitable (Chart 2). Further, we can’t discount a possibility that Tesla’s dominance may come to an end. Not only are Ford and GM gearing up their EV operations, but also European and Asian vehicle manufacturers such as VW, BMW, Hyundai, and Toyota present a significant competitive threat. Further, Chinese EVs, such as NIO, Geely, BYD, and XPEV, could erode Tesla’s market share in the Chinese market. Chart 1EV Will Reach Price Parity With ICE In 2023 EV Revolution EV Revolution Chart 2Tesla Has Only Recently Become Profitable Tesla Has Only Recently Become Profitable Tesla Has Only Recently Become Profitable Ford And GM Are Firmly Committed To EV Legacy automakers, such as Ford and GM, have no choice but to move aggressively into the EV space in order to survive the imminent regulatory push in Europe and the US to eliminate fossil-fuel cars. Also succeeding in the EV space is necessary to stave off competition from Tesla and other EV and legacy automakers (Chart 3). Recently, GM announced that it would accelerate its EV timeline and develop 30 new EV models by 2025, transitioning to 100% EV by 2035. It is targeting global EV sales of more than 1 million by 2025. On the heels of that announcement, Ford pledged to become all electric in Europe by 2030. The company anticipates that 40% of its global vehicle volume will be fully electric by 2030. Chart 3GM And Ford Need to Stave Off Competition From Tesla GM And Ford Need to Stave Off Competition From Tesla GM And Ford Need to Stave Off Competition From Tesla The transition to EV is a major endeavor for all legacy automakers but, if successful, they will reap significant rewards by means of higher sales and profits as EVs become increasingly more popular. They will also emerge as prime competitors of Tesla. Waymo (Alphabet) Alphabet’s Waymo launched its first autonomous ride-hailing network in Arizona but will need time and significant resources to scale nationally. The company is also developing both local and long-haul AV networks to transport goods. So far the company has not been profitable, struggling to commercialize the product efficiently. New EV Players There is a host of newcomers into the EV/AV space in the US. Furthest down the path in the light-vehicle market are Lucid, Fisker, and Electrameccanica (Solo). Workhorse Group, and the controversial Nikola are most established in the truck space. There are also EV recreational vehicle makers such as Canoe and Green Power Motors. EV/Autonomous Vehicles Ecosystem There is a brand new ecosystem developing around EVs, with suppliers providing batteries, radars, and charging stations. The industry is highly fragmented, and most smaller suppliers on the cutting edge of technological innovation are too small to be part of any index just yet or are not even public yet. Batteries The recently IPO’d QuantumScape has developed a breakthrough technology for a battery that charges in just 15 minutes. The company has received significant investment from VW. Solid Power is its newest competitor, still privately owned. Romeo Power develops batteries for big trucks, buses, and construction equipment. And XL Fleet supports EV conversions for commercial vehicles. Lidars Companies like Luminar and Velodyne use Lidar technology to improve the 3-D “vision” of the self-driving cars. These ventures demand large investments into capex and R&D, but present significant future revenue opportunities to the winners. Waymo (Alphabet) relies on Lidar technology for its fleet of AV vehicles. Charging Networks There are also a few companies focused on developing private charging networks, overcoming the main obstacle on the path to EV adoption – the need for ubiquitous availability of charging stations: ChargePoint, EVBox and Volta. Chipmakers All these vehicles are powered by chips produced by Nvidia, Qualcomm, Micron, and other semiconductor manufacturers, and technological improvements taking place in this industry are literally exponential. It is not clear yet which of these entrants are here to stay and, in a way, the EV and AV industry should remind investors of biotech: Each of these companies requires only a small allocation as part of an EV basket in order to capture the 100-bagger future winners. Where Do You Find The EV/AV Theme In Equity Indices? EV Companies And Suppliers Are Spread Across A Multitude Of Sectors This may sound like a silly question. The answer is seemingly obvious: In the Auto and Components Industry Group. However, there is a whole host of companies that are part of the ecosystem that are neither in the S&P 500/MSCI USA nor in the Auto and Components industry group. Nvidia, Micron, and Qualcomm are chipmakers assigned to the Technology sector. Alphabet’s self-driving business unit, Waymo, sits within Communications Services. Velodyne (recently added to the Russell 2000), Luminar, Quantumscape, and XL Fleet are small caps. There are also a number of special purpose acquisition companies (SPACs) that are in the process of merging with EV companies (Lucid, Faraday, ChargePoint, etc.). Auto And Components Industry Group Is Dwarfed By Tesla Moreover, a key issue with Auto and Components GICS2 is that it is dominated by a few large companies: Ford, GM, and Tesla account for 90% of the segment by market cap. The rest is divided among several autoparts manufacturers. Moreover, despite generating sales equal to only a quarter of the sales of GM or Ford (in 2020 $31 billion vs $122 billion for GM and $116 billion for Ford), Tesla alone represents roughly 3/4 of the industry group by market cap, being five times larger than Chrysler and GM combined (Chart 4). In terms of market share, Ford and GM account for 6% and 9% of global auto sales respectively, while Tesla barely even registers on a radar at 0.8%. Tesla’s dominant position holds this industry group hostage to its price performance (Chart 5). Chart 4Tesla Dominates Auto & Components Industry Group EV Revolution EV Revolution Chart 5Performance Of Auto Industry Is Held Hostage By Tesla EV Revolution EV Revolution  Therefore, it is more effective to pursue the EV theme via a more balanced and diversified custom stock basket or ETF. Having said that, because of the size of the three largest automakers, we rely on MSCI USA Auto and Components industry group as a proxy for the EV/AV investment theme for analytical purposes. EV ETFs Are Mushrooming Recently there appeared a number of ETFs powered by EV/AV themes, cutting across GICS, such as ARKQ, IDRV, KARS, and DRIV. The ETFs BATT and LIT narrowly focus on EV batteries. These ETFs contain a wide range of companies cutting across industries (See Appendix for details) Excluding the broader-themed ARKQ (Autonomous Technology and Robotics), the DRIV ETF is the most widely traded. This ETF contains all the same companies as the MSCI USA Auto and Parts industry group, but also covers the entire EV/AV supply chain from miners to companies manufacturing opto-electronic components like IIVI. DRIV contains 77 names, and ranges from giants like Tesla and Microsoft to the tiny Plug Power. It is a global ETF and includes names like Nio, VW, and Toyota. Not a single name exceeds 4% weight. DRIV is 67% correlated with MSCI USA Auto and Components, and is generally less volatile, as it is more diversified across a variety of sectors (Table 1). Table 1EV/AV ETFs EV Revolution EV Revolution Key Revenue Drivers Reopening Trade And Global Growth Acceleration The Automobiles and Components industry group is a classic early cyclical, highly geared to economic growth, outperforming during the recovery stage of the business cycle. Global reopening has resulted in a sharp global growth acceleration and benefited US automakers’ sales at home and abroad. Indeed, total vehicle sales in the US have already exceeded pre-pandemic levels. The question is whether this surge may continue with a backdrop of a growth slowdown (albeit off high levels) and how fast supply-chain disruptions will be resolved. Consumers Are Flush With Cash Most vehicles are sold to consumers, whose sentiment and financial wellbeing are the key industry drivers. Ubiquitous vaccination and economy-wide reopening is increasing employment in the lower-paid cohorts most affected by lockdowns. Expiration of unemployment benefits and school reopening will see millions more returning to work this fall. Anticipating a surge in employment, consumer confidence has started to rebound, albeit off low levels. The most recent $1.9 trillion fiscal stimulus package with its $1,400 checks cut directly to consumers, bodes well for US auto sales. For many vehicles, this amount may be sufficient for a down-payment. Personal savings have increased by roughly $1.5 trillion from the January 2020 trough, and disposable income has increased by 6%. Coupled with low interest rates and an improvement in banks’ willingness to lend, US consumers are in an excellent shape to upgrade their vehicles (Charts 6 & 7). Chart 6Demand For Auto Loans Has Picked Up Demand For Auto Loans Has Picked Up Demand For Auto Loans Has Picked Up Chart 7Lending Standards for Auto Loans Eased Up Lending Standards for Auto Loans Eased Up Lending Standards for Auto Loans Eased Up However, plans to buy a new car have declined recently due to car shortages and a spike in prices. Supply Chain Disruptions Hurt Demand For Vehicles Pandemic has brought about unique challenges: Global pent-up demand and COVID-induced supply-chain disruptions led to a mismatch between supply and demand and resulted in sharp price acceleration across a wide range of goods. US automakers have been hit hard by the global chip shortage, resulting in plant shutdowns and lower output in some cases. Shortages of lithium, a key component of EV batteries, led to its price doubling. Transportation networks are also choked up, and delivery costs are up more than 30%. While these post-pandemic difficulties are transitory in nature, prices of vehicles spiked, making it the most volatile component of the latest CPI reading, with prices in May rising 16% YoY (Chart 8). Higher price tags and half-empty car lots at dealerships are dampening consumers’ intentions to upgrade their vehicles, despite their present financial wellbeing (Chart 9). Chart 8Prices Of Cars Surged Prices Of Cars Surged Prices Of Cars Surged Chart 9Supply Disruption Dampened Demand For Vehicles Supply Disruption Dampened Demand For Vehicles Supply Disruption Dampened Demand For Vehicles According to IHS Markit, the average age of vehicles on US roadways rose to a record 12.1 years last year, as lofty prices and improved quality prompted owners to hold on to their cars for longer. The average price for a new vehicle is $38,000, which is expensive for most Americans. However, there are early signs that supply disruptions are starting to dissipate: Production of motor vehicles rose 6.7% in May compared with a 5.7% decrease a month earlier. Once vehicle prices stabilize, or even correct, sales are likely to rebound. EV also enjoy a unique tailwind: The price of gasoline has doubled since the beginning of the year, making electric vehicles a more attractive proposition than gas-guzzling alternatives. Weaker Dollar Boosts Foreign Sales USD has weakened by 8% since the beginning of the pandemic. This bodes well for the US auto and parts manufacturers who derive about 1/3 of revenues from outside the US. A weaker USD not only stimulates demand by making vehicles cheaper for foreign buyers but will also benefit manufacturers' income statements via a currency-translation effect (Chart 10). Chart 10Weaker Dollar Boosts Foreign Sales Weaker Dollar Boosts Foreign Sales Weaker Dollar Boosts Foreign Sales Profitability Of Automakers Belt-tightening Of 2020 Is Unsustainable Margin compression has been a problem for the industry group for a while as a race to enhance existing vehicles and transition to EV has been weighing on profitability (Chart 11). However, in 2020, despite a dip in sales volume, US automakers were able to successfully manage margins, by reducing R&D expenses, capex, and labor costs, and by halting increases in dividends and buybacks, and enjoying lower prices of industrial metals. Maintaining this new lean cost structure is hardly sustainable. Chart 11Margins Are Under Pressure Margins Are Under Pressure Margins Are Under Pressure R&D And Capex Will Rise As Technological Innovation Demands Capital Outlays R&D and capex are likely to increase for the entire group. Legacy automakers are forced to operate on two distinct timelines by managing and investing in the immediate conventional vehicle production cycle, while concurrently preparing for the longer-term transition to a world of vehicle electrification and autonomous driving. Development of EVs requires deep pockets and substantial investments into both capex and R&D, which have been steadily rising (Charts 12 & 13). Chart 12R&D Expense Is Bound To Increase… R&D Expense Is Bound To Increase… R&D Expense Is Bound To Increase… Chart 13… As Is Capex EV Revolution EV Revolution Case in point, GM has recently announced a $35 billion investment into EV and AV, an increase of 75% from its initial pledge, an amount exceeding its gas and diesel investment. Not to be outdone, Ford has copied the move, pledging $30 billion on EV vehicle development, including battery development, by 2025. This is an increase of more than 35% over the $22 billion previously pledged. Clearly, commitment to EV siphons resources away from other businesses, and put pressures on automakers to keep up with competitors. Yet the market applauded these announcements by bidding up shares of both companies, implicitly saying that EV spending will lead to better future cashflows. Thus transition to EV moves auto stocks from the Value into the Growth camp, making the group more sensitive to interest rates. Runaway Cost Of Raw Materials Is Stabilizing Metals such as steel, iron, and aluminum comprise over 75% of the content of the car. The price of metals is particularly important to EV manufacturers as the body of an EV contains five times more steel than regular vehicles. In 2020 gross margin benefited from a dip in prices of industrial metals. However, the recent economic recovery has led to a rebound in the prices of commodities, with the GSCI Industrial Metals Index rising by more than 70% off the bottom and reaching 2010 levels (Chart 14). There are early signs that prices are stabilizing: The price of steel is down by 20%, copper by 13%, and aluminum by 6%, from their respective peaks (Chart 15). Chart 14Price Of Industrial Metals Have Spiked... Price Of Industrial Metals Have Spiked... Price Of Industrial Metals Have Spiked... Chart 15...But There Are Early Signs Of Correction ...But There Are Early Signs Of Correction ...But There Are Early Signs Of Correction High Operating Leverage Of Auto Manufacturers Amplified Earnings Growth Automakers and suppliers have high fixed-cost manufacturing facilities. As a result, their operating leverage is high, i.e., increases in sales are translated into even greater increases in profits. As 2021 sales are expected to rise, earnings will also continue to rebound, reaching or even exceeding pre-pandemic levels. Looking ahead, we expect earnings growth to decelerate as sales are likely to normalize while EV transitioning costs will continue to rise (Chart 16). However, eventually, EV investment will translate into higher sales volumes: Once new technology infrastructure is in place, the long-term profitability of the industry group will improve. Chart 16Earnings Are Rebounding To Pre-pandemic Levels Earnings Are Rebounding To Pre-pandemic Levels Earnings Are Rebounding To Pre-pandemic Levels Valuations: Significant Dispersion Within Industry Group The auto and parts industry has been underperforming the market since February 2020, with valuations coming down significantly. Looking under the hood, we observe a pronounced bifurcation between Tesla and other stocks (Table 2). Table 2Tesla Is Still Expensive, Ford and GM Are Cheap EV Revolution EV Revolution Tesla trades at an eye-watering 596x earnings (which is an improvement from 1,300x back in January) and 16.3x sales multiple. The company has enormous long-term potential, but over the short term it needs to grow into its valuations, as it has effectively “borrowed” returns from the future. Yet investors need to keep in mind that Tesla is a cult stock, and has a strong retail following: Continuation of an irrational speculative bubble is within the realm of possibility. Therefore, a neutral allocation to Tesla will be prudent. Legacy automakers and suppliers are still cheap despite a strong run off their market lows. Forward 12-month PE is in the single/low-double digit range. Low valuations indicate that there is still an overhang of uncertainty over the economic recovery and potential profitability of legacy car manufacturers and suppliers, along with lingering doubts about the success of the group in the EV space. However, there is a lot of room for long-term rerating once there is greater visibility (Chart 17). Chart 17With Tesla Down 30% From Peak, Industry Group Looks Cheaper With Tesla Down 30% From Peak, Industry Group Looks Cheaper With Tesla Down 30% From Peak, Industry Group Looks Cheaper Investment Outlook We have a positive 3-12-month outlook for the investment performance of the EV theme: The entire EV cohort will benefit from government support for decarbonization, the preference of millennials for green tech, and cutting-edge technological innovation. American vehicles are getting old, and consumers have financial resources to purchase new cars. Supply disruptions are gradually dissipating. Gasoline is getting expensive, but EV/ICE parity is near. Investing in automakers and suppliers, which are turning into growth companies with longer duration of cash flows, is also aligned with our thesis of rotating into Growth as rates have stabilized and the pick up in inflation has been priced in. Legacy Automakers are to be primary beneficiaries of the theme. Both Ford and GM are relatively inexpensive. Higher earnings and improved visibility on the success of EV transition should lead to further rerating. Tesla is also a quintessential growth company. However, unlike legacy automakers, it has already had an amazing run. Even though it is down from its peak, it remains expensive, and much of the positive expectations are already baked into price. We recommend staying neutral on Tesla as it is a “cult” stock and a surge “to the moon” is not out of the question. Ecosystem Surge in EV capex and R&D spending will have positive spill-over effect on EV ecosystem suppliers. These are small cap stocks and creating a well-diversified basket of names in battery, radar, chips and software will help capture returns of the long-term winners. Existing EV-themed ETFs may not be perfect: Many have holdings that are way too broad and over diversified, most invest outside of the US. Yet, these are convenient vehicles to capture the theme and provide exposure to the entire EV value chain, including emerging industry players. Bottom Line: The auto industry is undergoing a major technological disruption. This process is expensive and perilous yet presents an enormous future earnings growth opportunity. The ingredients for success are in place: Proliferation of new technologies, government support, changing consumer preferences, and surging US economy. This tide will lift all boats: Legacy and EV-only auto manufacturers and suppliers as well as EV ecosystem players. We are bullish on the sector on a 3-12 months investment horizon.   Irene Tunkel Chief Strategist, US Equity Strategy irene.tunkel@bcaresearch.com   Appendix Table A1EV/AV ETF Summary EV Revolution EV Revolution Recommended Allocation EV Revolution EV Revolution

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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 2 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 4 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 6 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 8 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 10 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 14 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 16 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? Slide 18 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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 The Impact Of AI: Will We Be Able To Work In The Future? The Impact Of AI: Will We Be Able To Work In The Future? 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

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 Robots Are Getting Cheaper Robots 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 Global Robot Usage Global Robot Usage Chart 3Global Robot Usage By Industry (2016) The Impact Of Robots On Inflation The Impact Of Robots On Inflation 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) Stock Of Robots By Country (I) Stock Of Robots By Country (I) Chart 5Stock Of Robots By Country (II) (2016) The Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 U.S. Investment In Robots U.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 Productivity Collapsed Despite Automation Productivity 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 The Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 The Impact Of Robots On Inflation The Impact Of Robots On Inflation Chart 10U.S.: Unit Labor Costs Vs. Robot Density The Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 The Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 Impact Of Robots On Inflation The Impact Of Robots On Inflation 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 U.S. Capex Shortfall Partly To Blame For Poor Productivity U.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 The Impact Of Robots On Inflation The Impact Of Robots On Inflation Chart 16Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood Of Robots? Japan: 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.

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 Robots Are Getting Cheaper Robots 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 Global Robot Usage Global Robot Usage Chart II-3Global Robot Usage By Industry (2016) February 2018 February 2018 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) Stock Of Robots By Country (I) Stock Of Robots By Country (I) Chart II-5Stock Of Robots By Country (II) (2016) February 2018 February 2018 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 U.S. Investment in Robots U.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 Productivity Collapsed Despite Automation Productivity 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 February 2018 February 2018 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 February 2018 February 2018 Chart II-10U.S.: Unit Labor Costs Vs. Robot Density February 2018 February 2018 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 February 2018 February 2018 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 February 2018 February 2018 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 February 2018 February 2018 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 U.S. Capex Shortfall Partly To Blame For Poor Productivity U.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 February 2018 February 2018 Chart II-16Japan: Where Is The Flood Of Robots? Japan: Where Is The Flood OF Robots? Japan: 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.

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 E-Commerce: Steady Increase In Market Share E-Commerce: Steady Increase In Market Share Chart 2Amazon Dominates Did Amazon Kill The Phillips Curve? Did Amazon Kill The Phillips Curve? 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 Did Amazon Kill The Phillips Curve? Did Amazon Kill The Phillips Curve? 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 Did Amazon Kill The Phillips Curve? Did Amazon Kill The Phillips Curve? 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 Did Amazon Kill The Phillips Curve? Did Amazon Kill The Phillips Curve? 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 Did Amazon Kill The Phillips Curve? Did Amazon Kill The Phillips Curve? Chart 4Incompatible With A Supply Shock Incompatible With A Supply Shock Incompatible 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 Retail Margin Squeeze Only In Department Stores Retail 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 Online Price Index Online Price Index Chart 7Electronic Shopping Price Index Electronic Shopping Price Index Electronic 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 BCA E-Commerce Proxy Price Index BCA E-Commerce Proxy Price Index Chart 9BCA E-Commerce Adjusted Proxy Price Index BCA E-Commerce Adjusted Proxy Price Index BCA 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).

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 E-Commerce: Steady Increase In Market Share E-Commerce: Steady Increase In Market Share Chart II-2Amazon Dominates September 2017 September 2017 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) September 2017 September 2017 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) September 2017 September 2017 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 September 2017 September 2017 (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? September 2017 September 2017 Chart II-4Incompatible With A Supply Shock Incompatible With A Supply Shock Incompatible 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 Retail Margin Squeeze Only In Department Stores Retail 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 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 Online Price Index Online Price Index Chart II-7Electronic Shopping Price Index Electronic Shopping Price Index Electronic 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 BCA E-Commerce Proxy Price Index BCA E-Commerce Proxy Price Index Chart II-9BCA E-Commerce Adjusted Proxy Price Index BCA E-Commerce Adjusted Proxy Price Index BCA 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).

Dear Client, Over the next three weeks, much of BCA’s Geopolitical Strategy team will be traveling in Australia, New Zealand, and Asia. As such, we are taking this week off from publication and will return to our regular schedule next week. In lieu of our regular missive, we are sending you the following Special Report, penned by our colleagues in the BCA Technology Sector Strategy. The report, originally published on May 16, tackles “The Coming Robotics Revolution” in an innovative way that aligns with our own views. Clients often ask us what will be the political consequences of the revolution in artificial intelligence and robotics. Our answers are controversial because we strongly disagree with the conventional, Terminator-inspired, doom and gloom. Brian Piccioni and Paul Kantorovich agree with us, which is reassuring given that they understand the technology behind robotics far better than we do. I hope you enjoy the enclosed report and encourage you to seek out the insights of our Technology Sector Strategy. Kindest Regards, Marko Papic, Senior Vice President Chief Geopolitical Strategist Feature "The amount of technology coming at us in the next five years is probably more than we've seen in the last 50" Mark Franks, Director Of Global Automation at General Motors, Bloomberg News, April 2017 There is good reason to believe we are at the cusp of a Robot Revolution which will have a dramatic impact on our economy. Robots have been around for decades or centuries, depending on the definition. Past robots were either fixed in place, as in the case of factory robots, or supervised by operators that are near the robot, or connected through telemetry. In contrast, the robots that are coming will not be fixed in place, and will be able to perform their functions without a human operator. This opens up massive markets for robots in industry (cutting lawns, cleaning windows, delivering parcels, etc.) and, most significantly, consumer applications. Part 1: Robots - Industrial Revolution To Early 21st Century The term "robot" can have different meanings. The most basic definition is "a device that automatically performs complicated and often repetitive tasks,"1 a definition which encompasses a broad range of machines: from the Jacquard Loom,2 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. For much of history, most of the labor force was involved with the production of food: over 50% of the U.S. labor force was involved in agriculture until the late 1800s (Chart 1). Agriculture has benefitted immensely from automation as inventions such as the McCormick Reaper (a wheat cutting machine pulled by horses), the cotton gin, and other mechanical systems displaced human effort. Steam and then internal combustion-powered tractors, which can be viewed as "robotic horses," accelerated the process, as engines delivered much more power more cost effectively than mechanical devices (Chart 2). This massively improved productivity: within 20 years from 1830 to 1850, the labor to produce 100 bushels of wheat dropped from 250-300 to 75-90 hours, and by 1955 it only took 6 ½ hours of labor for a net reduction of 97.5% in 125 years.3 Chart 1Farm Workers Were Disrupted In The Late 19th Century The Coming Robotics Revolution The Coming Robotics Revolution Chart 2...And So Were Horses The Coming Robotics Revolution The Coming Robotics Revolution In other words there is nothing new about automation displacing workers while improving productivity, nor is a rapid displacement unprecedented. The industrial revolution was about replacing human craft labor with capital (i.e. 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. Automation Frightens People Although automation is nothing new, it has always engendered anxiety among workers. The anxiety boils down to concern for continued employment as well as fear of the technology itself. We discuss below why Artificial Intelligence (AI) does not present the sort of threat to humanity or even employment that seems to be the consensus view at the moment. Will Robots Become Self-Aware? We have covered the topic of Artificial Intelligence/Deep Learning as it relates to sentient/self-aware machines in some detail in our October 18, 2016 Special Report on Artificial Intelligence. In summary, most of the discussion surrounding AI is misinformation. Although AI uses algorithms called "artificial neural networks," which are extremely useful for solving certain classes of problems, these are nothing like biological neural networks. There is no reason whatsoever to believe AI technology in its current form can become sentient, or even meaningfully intelligent, and that will not change with increased computing power. Furthermore, whether or not AI can arise to the level of a threat, there is no current or imagined power source which could keep a rampaging robot active for more than a few hours. The Terminator would have been much less threatening if he required frequent recharging. Will Robots Make Human Workers Irrelevant? Automation in agriculture occurred rapidly enough to be felt by workers at the time - and yet there were no marauding hordes of unemployed hay cutters or cowboys. Improved productivity meant markets were opened which did not previously exist, and unemployed agricultural workers moved to factory work. Media coverage of automation tends to focus on the potential job losses without mentioning the fact that the economy and its workers adapt, and overall living standards generally improve (Chart 3). Technology has displaced entire classes of jobs very rapidly in the recent past, and many products such as smartphones would be extremely difficult to assemble if the work was done by hand. Box 1 provides several other examples. Yet as is usual for many things that have happened multiple times in the past, we are told "this time is different." Chart 3The Industrial Revolution Led To A Vast Improvement In Living Standards The Industrial Revolution Led To A Vast Improvement In Living Standards The Industrial Revolution Led To A Vast Improvement In Living Standards Box 1 Automation Displaced Entire Classes Of Jobs In The Recent Past, But Brought Enormous Benefits Before calculators and word processors were available, writing and mathematical calculations were done manually. Machines such as calculators and type writers enhanced productivity, eliminating many such jobs. Software applications such as Microsoft Word and Excel further accelerated this process. Not that long ago, welding was entirely a manual job but now most welding in factories is done by robots: you can usually tell a human weld on a mass produced product by its poor quality. Robots in the modern factory have freed up workers for other roles in the economy just as the massive loss of agricultural jobs in the 20th century did. Many modern electronic products such as smartphones would be extremely difficult to assemble if the work was done by hand, as the components are so small they require microscopes to manipulate. Even if it were possible to hand assemble a smartphone, it would take hours of manual labor to produce, and the quality would be very poor. The use of automation means that smartphones cost a few hundred dollars instead of a few thousand dollars and are affordable enough to be a mass market item. Some of the anxiety around automation-related job losses centers on the possibility that this time, robots will displace workers from the service and white-collar sectors. BCA's European Investment Strategy service has written about the potential for AI to replace jobs involving tasks that require specialized education and training, such as calculating credit scores or insurance premiums, or managing stock portfolios.4 Recent developments in AI (specifically deep learning algorithms) have allowed computers to solve pattern recognition problems that they could not previously solve. However, we do not believe AI in its current form poses a widespread risk to white collar employment for the following reasons: Both service-sector and white collar employees have been subject to replacement through automation already, and the economy has adapted: ATMs are robot bank tellers, self-checkout lanes are robot checkout kiosks, and "smart" gas and electric meters that can be read remotely replace human meter readers. The legal profession has been transformed by Google searches and the accounting business by accounting software. These tools allow certain clients to avoid the use of a lawyer or accountant altogether (for example in setting up a corporation or doing bookkeeping), or allow a firm to employ less skilled workers for the task. We can offer numerous other examples of white collar jobs which have been fully or partially automated over the past couple decades. In addition, recall that AI produces high probability answers which turn out to be wrong, and it requires a lot of subject specific training. Both of these are intrinsic to the implementation of the algorithm. In contrast, humans generally are much better at assigning confidence to decisions and train very rapidly because they have cross-expertise AI lacks. An implementation of AI has to meet BOTH of the following conditions to be successful: There has to be a lot of subject-specific data available A high probability assigned to a wrong answer is either inconsequential or can be easily overruled by a human It is also important to note that although AI may reduce the demand for accountants, insurance agents, credit analysts and other skilled professionals, these are exactly the sort of people that can handle retraining. Part 2: What Makes Upcoming Robots Revolutionary Upcoming robots will be different because they will not be confined to the factory floor. We believe this is a key transition point, and that the next 20 years or so will see as dramatic a change from robotics as was caused by the Internet. Factory robots have improved immensely due to cheaper and more capable control and vision systems. Early robots performed very specific operations under carefully controlled conditions -an assembly robot which encountered a misaligned component would simply install it that way, resulting in a defective product. Eventually vision systems were developed which allowed robots to adjust to varying conditions. As camera and computing costs continue to decline, vision systems are becoming more elaborate and useful, as they gather and process more information to make increasingly complex decisions. 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. Mobile robots will likely rely on AI to make many decisions. In order to be cost effective, for many years AI will likely be hosted in cloud data centers. This is especially the case for consumer robots, which will have to be highly capable and yet cost effective. We discuss the implications for cloud services providers in more detail in Part 3: Investment Implications. We May Be Entering A 'Virtuous Cycle' In Robotics Improvements to one domain of robotic applications can be generally applied to others. Robotics technology is concurrently moving forward on many fronts ranging from the aforementioned vacuum cleaners, lawnmowers, and logistics robots, to medical orderlies,5 farm tractors,6 mining equipment,7 transport trucks,8 and cargo ships.9 Despite enormous differences in cost and value added, all of these applications are solving essentially the same problem. As with any other technological revolution, advances between different fields in robotics will be adapted, borrowed, extended and enhanced. This, in turn, creates opportunities for ever more applications, creating a virtuous cycle (Diagram 1). Diagram 1Robotics Will Enter Into A Virtuous Cycle The Coming Robotics Revolution The Coming Robotics Revolution There are few tasks which cannot be automated, but there is a definite cost-benefit tradeoff for each one. For example, a golf course may consider spending $25,000 for a robotic lawnmower, however costs were closer to $70 - $90,000 in 2015,10 and installed cost is even higher.11 Because the incremental cost of the machines is comprised of electronics, which will drop in price rapidly, it is probably a matter of another 2 or 3 years before the price moves to the point where mass adoption by groundskeepers begins. The same improvements to industrial lawnmowers will lead to more useable, albeit still pricy, consumer models which will probably enter mass market adoption 5 to 10 years from now. The same argument can be made for almost any manual chore ranging from cleaning the carpet to delivering parcels. We predict the virtuous cycle for robots will span several decades. As the cost of automation drops, better solutions will be developed, resulting in 'early retirement' of dated but otherwise fully functional robotic systems. This is the opposite of the Feature Saturation phenomenon currently present in the smartphone and PC industries - though feature saturation will eventually hit robots as well. A Self-Driving Car Is A Robot The most important robotics technology, from a macroeconomic perspective, is the rapidly advancing field of Autonomous Vehicles (AVs). The automobile industry is a significant part of the global economy, so changes in this industry will have profound implications. We covered AVs in detail in our April 8, 2016 Special Report. Due to technical and legal obstacles that must be overcome, a vehicle which can safely travel from point to point on major roads and city streets without driver intervention is probably 20 years away, +/- 5 years. The macro impact, however, will occur much sooner than that, due to the technologies developed on the way to full AVs. Vehicles are already offering features such as forward collision warning, autobrake, lane departure warning, lane departure prevention, adaptive headlights, and blind spot detection.12 Although we have only touched the surface, robotics are being applied across many industries, making even seemingly modest advances significant when measured in aggregate, as small changes in one industry are quickly adapted by other industries. It is noteworthy that this transition will likely occur during a period where demographic shifts, in particular in the most developed economies, signal the potential for labor shortages, or at least increasing cost of labor (Chart 4).13, 14 Robots may be showing up in the nick of time to improve both the economy and quality of life in the developed world. Chart 4Advances In Robotics Will Counter Adverse##br## Demographic Trends Advances In Robotics Will Counter Adverse Demographic Trends Advances In Robotics Will Counter Adverse Demographic Trends Part 3: Investment Implications The semiconductor industry has stagnated as the PC and smartphone markets entered a largely replacement-driven era (Chart 5). Although it may not be evident until the virtuous cycle is fully engaged, robotics represents another up-leg in demand for semiconductors and therefore should result in a significant improvement to industry growth rates. There is little opportunity for startup semiconductor companies nowadays due to the high costs of developing a new chip. Well positioned, established, semiconductor companies will be the primary beneficiaries of the robotics revolution. Large firms that attempt to fit their existing product offering into the industry (e.g. by remaining PC or mobile-phone centric) will fall behind. Winners System on a Chip (SoC) Vendors: Robotics hardware will more likely be implemented as "System on a Chip" (SoC) as this provides the greatest functionality with lowest cost and power consumption. SoCs generally consist of a variety of Intellectual Property (IP) "cores" which may be licensed from third parties. Typically, IP cores consist of a microprocessor and various specialized subsystems, depending on the application. Robotics SoCs are likely to include Digital Signal Processing (DSP) or Image Processing cores to process sensor data. SoC vendors who target or encourage robot development, such as Overweight-rated Texas Instruments, are likely to be favored by early movers in the space.15 We believe it is a matter of time before Graphics Processors (GPUs) currently used in AI/Deep Learning are replaced by processors specifically designed for AI, which will be cheaper and more power efficient.16 This is one of the reasons for our Underweight rating on Nvidia. Semiconductor Foundries, Mixed Signal and Automotive Semiconductor Vendors: This environment will favor the merchant semiconductor foundries which manufacture most SoCs. In addition, firms with "mixed signal" expertise will experience increased demand for motor controls, sensor interfaces, etc. As robotics features are added to automobiles, demand for automotive semiconductors should outpace that in other sectors. A significant degree of commonality in the parts and systems used in advanced automobiles will be used in other mobile robots, so "automotive" semiconductor demand should significantly outpace automobile sales. Sensor Vendors: Robots need a variety of sensors, depending on the application. Unlike factory floor robots which can make do with cameras, mobile robots will require advanced radar, ultrasound, laser scanning and other sensor types in order to provide redundancy and cope with weather and other related issues. Important sensors on prototype AVs are currently made in low volumes and are extremely expensive. Due to the number of sensors involved, we believe there is significant opportunity for companies offering aggressive cost reduction in sensor technology. Wireless Equipment and Service Providers: Most robotic systems will include some degree of wireless connectivity and participate in the "Internet of Things" (IoT). This will present challenges and opportunities for wireless equipment and service providers,17, 18 as networks will have to adapt to increased upload bandwidth (from robot to carrier) as well as novel billing schemes. Coverage will also have to be expanded to accommodate AVs as it is non-existent or spotty in large stretches of North American roadways. Not being able to check Facebook between two cities is one thing, losing your robot driver is much more serious. Our recent downgrade of Cisco to Underweight19 may appear inconsistent with the analysis above. However, the company's valuation is extremely elevated and revenues are declining (Chart 6). Any benefit Cisco will derive from investment into wireless infrastructure is several years out, and open-source hardware initiatives are gaining momentum.20 For that reason, we see the risks as outweighing the opportunities at the moment for the company. Chart 5Long Replacement Cycles Mean Slower ##br##Semiconductor Sales Long Replacement Cycles Mean Slower Semiconductor Sales Long Replacement Cycles Mean Slower Semiconductor Sales Chart 6Cisco's Stock Price Is Close To Tech Bubble##br## Levels Despite Declining Revenue Cisco's Stock Price Is Close To Tech Bubble Levels Despite Declining Revenue Cisco's Stock Price Is Close To Tech Bubble Levels Despite Declining Revenue Cloud Service Providers: Most robots will be on line and some will likely use cloud services to offload computational effort and minimize cost. A relatively "dumb" robotic lawnmower which offloads control to a shared computational resource in the cloud would probably be cheaper than a much more capable fully autonomous system. This will increase demand for cloud services, however the challenge of declining margins (due to increased competition in the space) will offset cloud services revenue growth somewhat in the long term. On balance, Overweight-rated Microsoft and Alphabet/Google, as well as Amazon, stand to benefit. Chart 7Eastman Kodak Tried To Ignore The Shift ##br##To Digital Cameras Eastman Kodak Tried To Ignore The Shift To Digital Cameras Eastman Kodak Tried To Ignore The Shift To Digital Cameras Losers We believe companies who ignore the robotics revolution will find themselves at a significant competitive disadvantage. This is not unprecedented in the technology sector: Digital Equipment Corporation (DEC) and Kodak vanished because their business models could not accommodate an obvious shift in their core markets (Chart 7). Similarly Intel and Microsoft completely missed the smartphone revolution. As we noted in our April 8, 2016 Special Report on AVs, the frequency and severity of crashes will decrease dramatically which will lead to reduced insurance rates, fewer repairs, and less money spent on accident related healthcare and rehabilitation. The economic losses of automobile crashes were estimated $871 billion in the US in 201021 and even a modest reduction in the frequency and severity of collisions due to partial automation would have a significant economic impact. "Dumb" Auto Parts Manufacturers: Fewer collisions will result in fewer repairs to people or vehicles. Auto parts manufacturers will fall into two camps: those with significant expertise in robotics will prosper, while those without such expertise will fall behind as the demand for replacement components (fenders, bumpers, doors, windshields, etc.) will decline. AVs are also likely to include advanced diagnostic and service reminder systems which will result in more timely service, reducing wear and tear on internal components as well. The Auto Insurance Industry: While it is doubtful robotics will ever eliminate auto accidents, the rate might be reduced to such a level that the auto-insurance industry, worth $157 billion in the US alone,22 will be much smaller in 20 years than it is today. This will be offset to a degree by greater demands for product liability insurance for AVs and robots in general. Brian Piccioni, Vice President Technology Sector Strategy brianp@bcaresearch.com Paul Kantorovich, Research Analyst paulk@bcaresearch.com 1 http://www.merriam-webster.com/dictionary/robot 2 http://www.computersciencelab.com/ComputerHistory/HistoryPt2.htm 3 https://www.agclassroom.org/gan/timeline/farm_tech.htm 4 Please see European Investment Strategy Special Report, "Female Participation: Another Mega-Trend," dated April 6, 2017, available at eis.bcaresearch.com. 5 http://www.tomsguide.com/us/Forth-Valley-Royal-Robots-Serco-Medicine,news-7124.html 6 http://modernfarmer.com/2013/04/this-tractor-drives-itself/ 7 http://www.asirobots.com/mining/ 8 http://www.theaustralian.com.au/business/powering-australia/rio-rolls-out-the-robot-trucks/story-fnnnpqpy-1227090421535 9 http://www.bloomberg.com/news/articles/2014-02-25/rolls-royce-drone-ships-challenge-375-billion-industry-freight 10 http://techon.nikkeibp.co.jp/english/NEWS_EN/20141210/393619/ 11 http://www.golfcourseindustry.com/article/do-robotic-mowers-dream-of-electric-turf/ 12 http://www.iihs.org/iihs/topics/t/crash-avoidance-technologies/topicoverview 13 http://gbr.pepperdine.edu/2010/08/preparing-for-a-future-labor-shortage/ 14 http://www.imf.org/external/pubs/ft/fandd/2013/06/das.htm 15 http://www.ti.com/corp/docs/engineeringChange/robotics.html 16 Please see Technology Sector Strategy Weekly Report, "Google - AI And Cloud Strategy," dated April 25, 2017, available at tech.bcaresearch.com. 17 http://www.fiercemobileit.com/press-releases/gartner-says-internet-things-will-transform-data-center 18 http://www.computerworld.com/article/2886316/mobile-networks-prep-for-the-internet-of-things.html 19 Please see Technology Sector Strategy Weekly Report, "Networking Equipment Update ," dated March 28, 2017, available at tech.bcaresearch.com. 20 http://www.businessinsider.com/att-white-box-test-should-scare-cisco-juniper-2017-4 21 http://www.nhtsa.gov/About+NHTSA/Press+Releases/2014/NHTSA-study-shows-vehicle-crashes-have-$871-billion-impact-on-U.S.-economy,-society 22 http://www.bloomberg.c/bw/articles/2014-09-10/why-self-driving-cars-could-doom-the-auto-insurance-industry

The self-driving car, or Autonomous Vehicle (AV), will have a profound impact on a variety of industries. However, expectations for the timeframe of commercial AV availability are too optimistic. The greatest near-term impact is likely to be from advanced safety technologies developed on the path to full autonomy. In today's <i>Special Report</i>, we discuss our expectations for the timeframe of AV development, and the effect of advanced safety technologies on the Insurance, Health Care, Semiconductors, and Automotive industries.