3/2/21: EU-US Trade Policies Dynamics

 

Evolutionary dynamics of the U.S.-EU trade policy changes via S&P Global:

3/2/21: Monetary Easing and Stock Market Valuation

There has been quite a puzzling development in recent years in the monetary policy universe. A decade plus of ultra low interest rates has been associated with rising, not falling, risk premium in investment markets. In other words, a dramatically lower cost of new and carried debt induced by lower interest rates - a driver for lower risk, is being offset by something else. What?

Laine, Olli-Matti paper "Monetary Policy and Stock Market Valuation" (September 18, 2020, Bank of Finland Research Discussion Paper No. 16/2020: https://ssrn.com/abstract=3764721) tries to explain. 

To start with, some theory - especially for my students in the Investment and Financial Systems courses. Per author, "the value of a stock is the present value of its expected future dividends... Hence, the changes in stock prices must be explained by 

• either changes in dividend expectations or 
• changes in discount rates. 

The discount rate, or (approximately) expected rate of return, can be thought as a sum of a risk-free rate and a risk premium. Theoretically, monetary policy should have an effect on stock prices through the risk-free rates. In addition, monetary policy should affect dividend expectations, for example, through the output or debt interest payments of firms. The effect on the risk premium (not to mention the term structure of risk premia), however, is less clear."

Looking at Eurostoxx50 index components, Laine shows "...that the average expected premium has increased considerably since the global financial crisis. This change is explained by the change in long-horizon expected premia. ... monetary policy easing has had a positive impact on the expected average premium."

Specifically (emphasis added): "a negative shock to the shadow rate is estimated to increase average expected premium persistently. Instead, the results show that monetary policy easing temporarily decreases short-term expected [risk] premia. This means that expansionary monetary policy steepens the slope of the term structure of risk premia."

This is not exactly new, as Bernanke and Kuttner (2005) observed that "expansionary monetary policy generates an immediate rise in equity prices followed by a period of lower-than-normal excess returns. ...However, Bernanke and Kuttner (2005) do not study the effect on the long-run excess returns. My results show that effect on long-horizon expected premia has a different sign. This effect on long-horizon premia seems to more than offset the effect on short-horizon premia."

Interestingly, "Contractionary monetary policy increases the short-term premia temporarily, but decreases long-horizon premia persistently. The effect on average expected premium is negative. Thus, monetary policy tightening actually makes stocks expensive relative to the expected stream of dividends. The results provide no evidence that expansionary monetary policy causes stock market bubbles..."

Here is (annotated by me) a chart showing evolution of implied and actual risk premia:

From theory perspective, therefore, monetary policy "can affect equity prices through the dividend expectations, expected risk-free rates or expected premia":

• "The effect of expansionary monetary policy on the dividend expectations is probably positive, because expansionary monetary policy can be expected to increase output and firms’ earnings.
• "Expansionary policy probably lowers the risk-free rates, but it is also possible that the effect is totally different. Central bank’s rate cut can increase risk-free rates, if people think that the rate cut eventually increases inflation. 
• "As for the expected premium, the sign of the effect is unclear. ... Gust and López-Salido (2014) show theoretically that expansionary monetary policy lowers the premium ... where asset and goods markets are segmented. When it comes to quantitative easing, ... investors who have sold their assets to the central bank rebalance their portfolios into riskier assets, which lowers their expected returns. ... Theoretically, it is also possible to argue that monetary policy easing actually increases the expected premium. If one assumes that there exists mispricing like Galí (2014) and Galí and Gambetti (2015), then the sign of the response is ambiguous. ... This means that monetary policy easing increases the expected premium implied by dividend discount model (see Galí and Gambetti, 2015, p. 250-252)."

So, onto the empirical results by Laine: 

1. "Interest rates have declined considerably since the global financial crisis, yet the expected average stock market return has remained quite stable at around 9 percent. This implies that expected average stock market premium has increased remarkably. This rise is mainly explained by the premia over a discounting horizon of four years.
2. "These results may seem unintuitive as the prices of stocks have risen, and ratios like price-to-earnings have been historically high. However, high price-to-earnings ratios do not necessarily mean that stocks are expensive, because the value of a stock is the present value of its expected future dividends.
3. "When it comes to the role of monetary policy, the results show that monetary policy easing decreases short-horizon required premia, but increases longer-horizon premia. 
4. "The effect on expected average premium is positive, i.e. expansionary monetary policy lowers the prices of stocks in relation to the expected dividend stream."

2/2/21: Daylight Saving Time and Carbon Emissions

We usually associate reduction of carbon emissions with reduced consumption, as opposed to variation in timing of consumption, but this association is both too simplistic and also erroneous. Here is why: shifting more consumption activities toward periods of the day when energy generation mix is cleaner (e.g. daylight, when solar can be contributing more to the energy mix) can, quite literally, reduce overall emissions.

Right? Yep. Here is a nice piece of evidence from a natural experiment in Turkey. "In October 2016, Turkey chose to stay on DST all year round." This shifted a lot more consumption by the public from late afternoons to early mornings. As reported in Bircan, Cagatay and Wirsching, Elisa study "Daylight Saving All Year Round? Evidence from a National Experiment" (December, 2020, EBRD Working Paper No. 251, https://ssrn.com/abstract=3751336), overall levels of consumption did not change much, but "the policy has a strong intra-day distributional effect, increasing consumption in the early morning and reducing it in the late afternoon. This change in the load shape reduced generation by dirtier fossil fuel plants and increased it by cleaner renewable sources that can more easily satisfy peak load generation. Emissions from generation decreased as a result." 

Overall, the authors "find that staying on DST during winter months may have led to a reduction in CO2 emissions of between 1,500 and 8,200 tons per day. Hence, the policy change has an unforeseen but beneficial effect of reducing greenhouse gas (GHG) emissions, as generation by “cleaner” power plants substitutes generation from “dirtier” ones to satisfy changes in intra-day demand."

Incidentally, the study does not appear to have considered the effects of solar in their study that should have increased the CO2 abatement effects. It is unclear to me as to why...

2/2/21: The Disaster of Investing via Smartphones?

Some stuff I've been reading that (sometimes) falls into current newsflow: 

Kalda, Ankit and Loos, Benjamin and Previtero, Alessandro and Hackethal, Andreas paper, titled "Smart(Phone) Investing? A within Investor-Time Analysis of New Technologies and Trading Behavior"from January 2021 (NBER Working Paper No. w28363, https://ssrn.com/abstract=3772602) :

The authors tackle an interesting issue relating to the automated and low cost investing platforms (proliferating in this age of fintech). Per authors (emphasis is mine, throughout): "Technology has dramatically changed how retail investors trade, from placing orders using direct dial-up connections in the 1980s or Internet-based trading in the 1990s to the more recent rise of robo-advisers. With few exceptions, the introduction of these new technologies is generally associated with a decline in investor portfolio efficiency." In addition, "whether good or bad for investors, it is accepted that new technologies influence investor behavior". 

In this unique study, the authors used data that comes "from two large German retail banks that have introduced trading applications for mobile devices. For over 15,000 bank clients that have used these mobile apps in the years 2010-2017, we can observe all holdings and transactions, and, more important, the specific platform used for each trade (e.g., personal computer vs. smartphone). [As the result of having such a granular data over time] we can conduct all our main tests comparing trades done by the same investor in the same month across different platforms."

The authors present four sets of results:

1. "First, we study if the use of smartphones induces differences in the riskiness of trades. Comparing trades by the same investor in the same year-month, we find that the probability of purchasing risky assets increases in smartphone trades compared to non-smartphone ones. 
• "smartphone trades involve assets with higher volatility and more positive skewness. [Thus], smartphones increase the probability of buying lottery-type stocks by 67% of the unconditional mean for smartphone users."
2. "Second, we examine the effects of smartphones on the tendency to chase past returns. We find that smartphones increase the probability of buying assets in the top decile of the past performance distribution. Smartphones increase the probability of buying assets in the top 10 percent of past performance by 12.0 percentage points (or 70.6% of the unconditional mean)." In other words, smartphones trades involve severe and pervasive biases in investor decision making.
3. "Third, we investigate if investors selectively use smartphone to execute their risky, lottery-type, and trend-chasing trades. In this scenario, investors could simply substitute their trades from one device to another, without any real consequences for their overall portfolio efficiency. ....We find that, following the launch of smartphone apps, investors are—if anything—more likely to purchase risky and lottery-type assets and to chase hot investments also on non-smartphone platforms. ...this evidence potentially suggests that investors are learning to become overall more biased after their initial use of smartphones to trade."
4. "...smartphone effects are stronger during after-hours (i.e. following exchange closure). Institutional differences between trading on official exchanges and in after-hours markets do not drive this heterogeneity. Given that individuals are more likely to rely on the more intuitive system [System 1-type] later in the day (Kahneman,2011), stronger effects during after-hours are consistent with smartphones facilitating trades based more on [intuitive] system thinking."

As an interesting aside, it is worth noting that the above results have nothing to do with the demographic biases or the potential lack of trading experience by smartphone-using investors. As noted by the authors: "German investors that adopt smartphone trading are, on average, 45 years old with nine years of experience investing with the banks."

Another aside is that authors also tested if the adverse effects of smartphones-based trading can be attributed to the first / early usage of these devices. It turns out not: "The effects of smartphones are stable from the first quarter of usage up to quarter nine or afterwards. The effects on volatility and skewness of trades, and probability of purchasing past winners are also stable over time."

To conclude: "Collectively, our evidence suggests that investors make more intuitive (system 1-type) decisions while using smartphones. This tendency leads to increased risk-taking, gambling-like activity, and more trend chasing. Previous studies have linked these trading behaviors to lower portfolio efficiency and performance. Therefore, the convenience of smartphone trading might come at a cost for many retail investors."

Ouch! Then again, this is fitting well with what we are observing happening in the markets these days: amplified herding, trend chasing, lottery-like speculative swings in investment capital flows, recency effects of overbidding for previously outperforming stocks and so on. 

1/2/21: The Unbearable Lightness of Winning?

My recent article for The Currency on some tight corners to be navigated by the Biden-Harris Administration as the Democrats grapple with controlling the two branches of the State: https://thecurrency.news/articles/33887/biden-harris-and-the-unbearable-lightness-of-winning/ 

6/1/21: BRIC: Composite Economic Indicators: 4Q 2020

• Manufacturing PMIs for BRIC economies for 4Q 2020 are discussed here: https://trueeconomics.blogspot.com/2021/01/4121-bric-manufacturing-pmis-4q-2020.html.
• Services PMIs are covered here: https://trueeconomics.blogspot.com/2021/01/6121-bric-services-pmis-4q-2020.html.

Now, Composite PMIs:

• Brazil Composite PMI rose from 51.6 in 3Q 2020 to 54.4 in 4Q 2020, marking second consecutive quarter of > 50.0 readings. Average 4 quarters PMI stands at 46.2, suggesting that Brazil's economy has not, yet, recovered fully from the Covid19 pandemic impact. Nonetheless, statistically, both 3Q and 4Q readings are signaling economic expansion and 4Q growth in Brazil's economy appears to be faster-paced than global (global composite PMI was at 53.3 in 4Q 2020).
• Russia Composite PMI is in a contraction territory, with 4Q 2020 reading of 47.7, down from 55.9 in 3Q 2020. Over the course of 2020, Russia Composite PMI averaged 46.0, the second weakest in the BRICs group. At 47.7, 4Q 2020 PMI is exactly in line with 1Q 2020 PMI.
• India Composite PMI rose from 45.9 in 3Q 2020 to 56.4 in 4Q 2020, signaling rapid bounce back in the economy, that, nonetheless continues to suffer from the pandemic-induced economic crisis. Full year 2020, Composite PMI average is at 44.3, by a distance, the lowest in the BRICs group. 
• China Composite PMI rose from 54.7 in 3Q 2020 to 56.3 in 4Q 2020, marking third consecutive quarter of economic growth, with full year PMI averaging 51.4, suggesting that the Chinese economy has now recovered fully from the Covid19 pandemic impact. 

Overall, three out of four BRIC economies posted 4Q 2020 Composite PMI above Global Composite PMI: Brazil, India and China, with Russia being the only BRIC economy posting both sub-Global and sub-50 Composite PMI reading at the end of 2020. Only one BRIC economy has, so far, signaled full recovery from the Covid19 crisis shock: China, with all other BRICs still recovering from the pandemic.

Given that both BRIC Manufacturing Sector Activity Index (54.9 in 4Q 2020) and BRIC Services Sector Activity Index (54.8 in 4Q 2020) are above Global Manufacturing (53.5) and Services (52.3) PMIs, BRIC economies as a group have supported global economic growth to the upside in 4Q 2020. In contrast, BRIC Manufacturing Activity Index outperformed Global Manufacturing PMI in 3Q 2020 (53.0 to 51.6), while BRIC Services Activity Index (51.0) underperformed Global Services PMI (51.4). 

6/1/21: BRIC: Services PMIs: 4Q 2020

 

BRIC's manufacturing PMIs for 4Q 2020 were covered here: https://trueeconomics.blogspot.com/2021/01/4121-bric-manufacturing-pmis-4q-2020.html. Now, to Services PMIs:

• Brazil Services PMI rose from 47.5 in 3Q 2020 to 51.4 in 4Q 2020, with aggregate 2020 levels of activity still significantly below 2019 levels. At 51.4, the index is barely statistically above 50.0 (95% confidence bound is 51.3). However, the latest quarterly reading is the first nominally above 50.0 after three consecutive quarters of sub-50 readings. 
• Russia Services PMI crashed in 4Q 2020 from 56.8 in 3Q to 47.7. Statistically, Russian services sector is contracting and it is contracting rapidly. In the entire 2020, there were three quarters of deeply sub-50 readings against one quarter of above 50.0 expansion. Services sector reading is basically identical to 47.6 recorded in Manufacturing sector, which means that in 4Q 2020 there was no 'comfort zone' in the Russian economy in terms of growth.
• India Services PMI rose significantly in 4Q 2020 compared to 3Q 2020, from 41.9 to 53.4.  However, this growth is unlikely to bring India's services activity anywhere near pre-Covid19 levels. 
• China Services PMI rose for the third consecutive quarter in 4Q 2020. In 2Q 2020, China's Services PMI was at 52.6, which increased to 54.3 in 3Q 2020 and to 57.0 in 4Q 2020. Nonetheless, it is still doubtful that Chinese services activities have fully recovered from the pandemic as of the end of 2020.
• Overall, BRIC Services Activity Index based on PMIs and respective GDP shares in the global economy rose for the second quarter in a row from 51.0 in 3Q 2020 to 54.8 in 4Q 2020. This marks some recovery from the Covid19 pandemic impact, although this recovery remains incomplete. BRICs have - as a group - outperformed Global Services PMI which rose from 51.4 in 3Q 2020 to 52.3 in 4Q 2020.

5/1/21: Ireland PMIs: 4Q 2020

Ireland's economic activity improved significantly in December, and the improvements were marked across all three sectors:

• Ireland's Manufacturing PMI rose 52.2 in November to 57.2 in December, marking the third consecutive month of > 50 readings, the second consecutive month of indicator being statistically above 50.0 line. The last three months average (53.23) is on 2Q 2020 average (53.30) and this is pretty encouraging, given the weakness in the indicator over 1H 2020. 
• Ireland's Services PMI also rose in December, reaching 50.1 from recessionary 45.4 in November. 4Q average is still weak at 47.9 (contractionary) after being effectively stagnant at 50.03 over 3Q 2020. Monthly increase in December, however, is a brighter spot.
• Ireland's Construction sector PMI (data through mid-December) is at 53.5, which is strong compared to month prior (48.6) and the first time the index is above 50 line since July 2020. 
• Official Composite PMI that accounts only for two sectors of activity (Manufacturing and Services) is now at 53.4, having broken above the 50.0 line for the first time since August 2020.

As you know,  I calculate my own index of economic activity based on all three sectors PMIs and using relative weights of each sector in Irish Gross Value Added, based on the latest National Accounts data. This is plotted against Markit's Composite PMI in the following chart:

Just as Composite PMI, my index of economic activity also rose in December (to 52.9) from 48.2 in November. This marks the first month of above-50 readings after 3 consecutive months of contraction. Nonetheless, 4Q 2020 index is at 50.03 - signaling zero growth q/q and this stands contrasted to 3Q 2020 reading of 51.2 (statistically zero growth, nominally, weak positive growth).

5/1/21: U.S. Labor Markets Update: America's Scariest Charts

Continued unemployment claims (based on seasonally-adjusted data) are continuing to decline, as the latest data through mid-December 2020 shows, yet, even with these news, the latest data print puts continued claims for unemployment at the levels comparable with late 2009. 

So here is the chart showing overall levels of continued unemployment claims in the U.S.:

And here is one of my "Scariest Charts", showing index of continued unemployment claims across all modern recessions:

Given current rates of continued unemployment claims declines, 

• Over the last 4 weeks, average weekly decrease in continued unemployment claims stood at 77,000
• Current levels are 3,570,000 higher than pre-Covid low.
• Which means that it would take roughly 46 weeks at the current 4-weeks average rate of decrease to eliminate surplus unemployment generated by the Covid19 pandemic. Which is pretty much the same distance to point of regaining pre-Covid19 levels of unemployment claims as well.

Meanwhile, some bad news from the most recent data on new unemployment claims:

In December 2020, new unemployment claims rose, not fallen, on 4 months cumulative basis due to a large increase in non-seasonally adjusted new claims in the first week of the month. How bad are things? Most recent data point ranks 33rd highest new unemployment claims weekly count in the entire history of the series (since July 1967). However, excluding other weeks of Covid19 pandemic, or, put differently, contextualizing current levels to pre-Covid19 history, the latest levels of new unemployment claims would have ranked as 5th highest in history.

4/1/21: BRIC: Manufacturing PMIs 4Q 2020

Latest data for BRIC Manufacturing PMIs indicates three countries outperforming global rate of recovery in manufacturing sector, against one country (Russia) remaining in contraction territory and well below global growth mark.

On a quarterly basis,

• Brazil's Manufacturing PMI stood at 64.1 in 4Q 2020, up on 62.6 in 3Q 2020, marking the second highest and the highest reading on record. The contraction in 2Q 2020 (with PMI at 42.0) was sharp, but not as sharp as in 1Q 2009. By these comparatives, GFC-related contraction of 2008-2009 resulted in 4 quarters average reading of 45.1 and saw three consecutive sub-50 readings. The Covid-19 related contraction was stretched only across one quarter, with 4 quarters average of 54.8 in 2020. It is, genuinely, hard to reconcile these numbers with reality of the Covid-19 crisis.
• Russia Manufacturing PMI slipped to 47.6 in 4Q 2020 from 49.5 in 3Q 2020, marking sixth consecutive quarter of sub-50 readings. Statistically, Russian Manufacturing posted no growth (> 50 readings) in seven consecutive quarters. Over 2020 as a whole, Russian PMIs averaged abysmal 46.0, compared to the GFC and the Great Recession average of 2008-2009 of 44.7.
• India Manufacturing PMI was at 57.2 in 4Q 2020, up on 51.6 in 3Q 2020, and averaging 49.5 for the year as a whole. During the GFC and the Great Recession period, India's PMI averaged at 51.1. Unlike Brazil, India is yet to recover to pre-Covid-19 levels of activity.
• China Manufacturing PMI finished 2020 with a reading of 53.9, averaging 51.1 over 2020 as a whole, with overall PMIs performance suggesting that Chinese industrial producers have recovered from the Covid-19 pandemic by the end of 2020. China's Covid-19 experience has been more benign than the country contraction during the GFC and the Great Recession (46.9 average).

Global Manufacturing PMI stood at 53.5 in 4Q 2020 and an average of 49.3 over 2020 as a whole, against BRIC's Manufacturing Index (weighted by relative global GDP shares of the four economies) at 54.9 in 4Q 2020 and 50.5 for 2020 as a whole. In other words, BRICs have supported global growth to the upside during the Covid-19 pandemic. 

3/1/21: Covid19 update: Sweden vs Nordics

 

As before, let's conclude the latest update of the Covid19 trends data with analysis covering comparatives between Sweden and other Nordics. 

Sweden is commonly used as a shining example of 'saving the economy' by not 'panicking' into severe mobility restrictions. This argument is commonly used by the folks who tend to believe in sinister Big State conspiracies around other countries' responses to the pandemic.

Sweden started the pandemic by openly pursuing the strategy targeting 'herd immunity'. In this, the country approach to the pandemic containment was similar to that of the Netherlands. However, unlike Sweden, the Netherlands quickly reversed this approach and switched to the more common policy response of imposing severe mobility restrictions.

When it comes to the Nordic countries, there has been both some significant heterogeneity in Covid19 policies responses and some shared experiences. To reflect some of these, I look at three Nordics groupings to compare these with Sweden:

• Nordic 1 group comprising Sweden's immediate neighbors of Norway and Finland. This is the 'closest' group to Sweden as the three countries share relatively open borders and, in normal times, have no mobility restrictions between them. All three countries are physically remote from the rest of Europe, with far less mobility across borders to third countries than, say, Belgium or the Netherlands.
• Nordic 2 group adds Iceland and Estonia to the first group. Iceland is, obviously, an island nation that is also relatively well isolated in physical terms, making its border controls more effective. Estonia is a country that is not physically isolated, but shares less physical land-based borders with the rest of the EU (ex-Finland). Both, N1 and N2 groups are, therefore, characterized as those countries which can impose more effective control of their borders for the purpose of isolating during the pandemic.
• Nordic 3 group adds two key countries that have much less capacity to isolate from the Continental EU states: Denmark and the Netherlands. 

So, here are the updated charts, in which I adjust all three groups to normalize cases and deaths numbers to Sweden's population scale:

As of the end of 2020, cumulative excess deaths in Sweden compared to other Nordics, adjusting for differences in population sizes are:

• 7,545 more deaths in Sweden than in Nordics 1 group of Finland and Norway;
• 7,359 more deaths in Sweden than in Nordics 2 group of Finland, Norway, Estonia and Iceland; and
• 3,808 more deaths in Sweden than in Nordics 3 group of Finland, Norway, Estonia, Iceland, Denmark and the Netherlands.

Put differently, between 3,800 and 7,545 more deaths took place in Sweden than in its relatively comparable European neighbors, primarily because Swedish Government prioritized economic well-being over public health.

2/1/21: Covid19 update: U.S. vs EU27

In previous posts, I covered Covid-19 updates for the last week of 2020 for:

• Worldwide trends: https://trueeconomics.blogspot.com/2021/01/2121-covid19-update-worldwide-numbers.html;
• Europe and the EU27: https://trueeconomics.blogspot.com/2021/01/2121-covid19-update-europe.html; 
• Countries with more than 250,000 cases: https://trueeconomics.blogspot.com/2021/01/2121-covid19-update-countries-with-250k.html; and
• BRIICS: https://trueeconomics.blogspot.com/2021/01/2121-covid19-update-briics.html.

In this post, let's take a look at the latest data for the U.S. compared to the EU27.

Weekly counts of new cases and deaths, illustrated above, suggest that:

• Since the start of the pandemic, the U.S. has experienced three waves, against the EU27's two of the pandemic. The EU27's 2nd wave appears to have crested in week 45, while the U.S.' current wave continued to rise through week 51 of 2020. Week 52 data is hard to interpret, as it represents poorer quality of data due to the holidays season.
• Over the last 8 weeks, US new cases exceeded those in the EU27 by 337,233.
• The EU27's 2nd wave appears to have crested in week 48 in terms of deaths, while the U.S.' current wave continued to rise through week 51. Once again, we should ignore, for now, week 52 data.
• Over the last 8 weeks, US new deaths continued to run below those in the EU27. On population-adjusted basis, US deaths cumulated over the last 8 weeks are 33,622 lower than those in the EU27. Over the entire pandemic period, US deaths currently exceed those in the EU27 by 69,416 on population-adjusted basis.

The last point is worth considering more closely:

• Since the start of Wave 2 in the EU27 (Wave 3 in the U.S.), EU27 deaths per capita have been converging with those in the U.S.
• At the start of the EU27 Wave 2, U.S. excess total deaths per capita exceeded those in the EU27 by 87%. Latest excess is 26% and it was 28% in week 51.
• Adjusting for differences in population, U.S. excess deaths relative to the EU27 fell from the Wave 1 maximum of 103,038 to 69,389 today. 
• Adjusting for differences in population, U.S. excess deaths relative to Europe fell from the Wave 1 maximum of 122,441 to 117,690 today. 
• Adjusting for age differences and population size differences, the U.S. pandemic is associated with 135,343 excess deaths compared to the EU27.

Despite the big negatives, mortality rates have declined for the later waves of the pandemic in both the EU27 and the U.S.:

Note: the above chart is not adjusted for demographics differences between the U.S. and the EU27, which means that part of the amelioration in mortality rates in the U.S. relative to the EU27 is down to these differences.

Lastly, rates of change in cases and deaths, both, suggest that the pandemic Wave 2 (in the EU27) and wave 3 (in the U.S.) are still at risk of re-accelerating as new data arrives and as we intergate more accurate figures for Week 52 of 2020:

Finally, a summary table for comparatives:

The table above clearly shows the reality of the pandemic impact differences between the EU27 and the U.S. to-date. Through week 52 of 2020, the U.S. performance is consistently worse than that of the EU27 in all metrics, but one: mortality rate per 1,000 positive cases. This only difference is most likely accounted for by the factor exogenous to the pandemic policy responses in the two countries, being down primarily to younger demographics of the U.S. population.