4/2/21: The Impact of the Business Closures on Covid-19 Infection Rates

 In a recent post, I covered the impact of the failure at the Federal level to implement more robust measures on rents and tenure security for households (see: https://trueeconomics.blogspot.com/2021/02/3221-cost-of-trumps-failures-to-act-on.html). Another interesting aspect of the U.S. experience during the pandemic relates to the policies concerning the closure of essential vs non-essential businesses. A recent (January 2021) study by Song, Hummy and McKenna, Ryan and Chen, Angela T. and David, Guy and Smith-McLallen, Aaron, titled: "The Impact of the Non-Essential Business Closure Policy on Covid-19 Infection Rates" (NBER Working Paper No. w28374: https://ssrn.com/abstract=3772613) looked at the implications of this specific policy response to the Covid-19 pandemic.

Per authors, durig the pandemic, "many localities instituted non-essential business closure orders, keeping individuals categorized as essential workers at the frontlines while sending their non-essential counterparts home". The authors examined "the extent to which being designated as an essential or non-essential worker impacts one’s risk of being Covid-positive following the non-essential business closure order". The study used data for the State of Pennsylvania, accounting for the intra-household transmission risk experienced by the workers' cohabiting family members and roommates. 

The study estimated that:

• "... workers designated as essential have a 55% higher likelihood of being positive for Covid-19 than those classified as non-essential; in other words, non-essential workers experience a protective effect. 
• "While members of the health care and social assistance sub-sector contribute significantly to this overall effect, it is not completely driven by them. 
• "We also find evidence of intra-household transmission that differs in intensity by essential status. Dependents cohabiting with an essential worker have a 17% higher likelihood of being Covid-positive compared to those cohabiting with a non-essential worker. Roommates cohabiting with an essential worker experience a 38% increase in likelihood of being Covid-positive. 
• Overall, "analysis of households with a Covid-positive member suggests that intrahousehold transmission is an important mechanism."

In summary: "Our findings suggest that essential workers and their cohabitants (whether dependents or other primary policyholders sharing the same address) are at substantially higher risk of being positive for Covid-19 than are non-essential workers and their cohabitants. Conversely, non-essential workers and their cohabitants experience a protective effect against the risk of Covid-19 infection as a result of the nonessential business closure policy." 

And the kicker: "the designation of some workers as essential and others as non-essential during the pandemic has increased the health risk profile of some jobs while reducing it for others, all while other underlying aspects of these jobs (e.g., monetary compensation) remain minimally affected." In other words, the essential workers carry risk without carrying associated risk premium in their compensation (monetary or non-monetary).

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 6

 Having covered some core stats relating to the U.S. labor markets in previous 5 posts:

1. Continued Unemployment Claims (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest.html);
2. Labor force participation rate and Employment-to-Population ratio (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_4.html); 
3. Non-farms payrolls (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_16.html); 
4. New (initial) unemployment claims data through January 30, 2021 (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_57.html); and
5. Average duration of unemployment (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_41.html),

in this last post, we will focus on the overall employment index for the current recessionary cycle:

Currently, into month 10 data of the recession (December 2020), and employment index is reading close to the conditions in the recession of 1945, but better than the recession of 1953. We are still trending worse than any recession in modern period (post-Gold Standard), and that is quite an achievement (in negative terms). Dynamically, improvements in employment conditions have been flattening out from month 5 of the recession through month 8 and index improvements have slowed down to almost nil in months 9 and 10. Unless there is a significant reversal in this trend, by the end of 2021 we are likely to be around the same labor markets conditions as at the same time during the Great Recession. 

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 5

 The first four posts on the state of the U.S. labor markets have covered:

1. Continued Unemployment Claims (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest.html);
2. Labor force participation rate and Employment-to-Population ratio (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_4.html); 
3. Non-farms payrolls (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_16.html); and
4. New (initial) unemployment claims data through January 30, 2021 (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_57.html)

In this post, let's take a look at the latest data on average duration of unemployment through December 2020:

As the chart above clearly shows, current average duration of unemployment spell is already higher than the peak of any prior recession other than the Great Recession. However, the duration remains relatively benign when we control for the business cycle (red line and the chart next).

Dynamically, it is hard to imagine average duration of unemployment to be staying around its current levels. Something to watch in months to come as an indicator of the direction of structural (as opposed to cyclical) unemployment. 

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 4

 The first three posts on the state of the U.S. labor markets have covered:

1. Continued Unemployment Claims (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest.html);
2. Labor force participation rate and Employment-to-Population ratio (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_4.html); and
3. Non-farms payrolls (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_16.html)

In this post, let's take a look at new unemployment claims data through the week of January 30, 2021:

The data confirms the worrying trends cited in reference to continued unemployment claims. In the last week of January 2021, based on preliminary estimates published today, initial unemployment claims stood at 816,247 - a decline of just 23,525 on prior week reading. The 4-weeks cumulative initial unemployment claims are at 3,744,581, which only 103.433 down on prior 4 weeks period. Net, over the last 5 weeks, the reduction in initial unemployment claims stands at a miserly 19,725. 

Despite little media coverage, the U.S. labor markets remain stricken by the pandemic effects on economic activity. If we strip out data for the pandemic period-to-date, the latest weekly reading for initial unemployment claim ranks as the 10th highest in the history of the series. 

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 3

 In two prior posts, I covered two of America's Scariest Charts:

1. Continued Unemployment Claims (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest.html) and 
2. Labor force participation rate and Employment-to-Population ratio (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest_4.html)

Here, let's take a look at non-farm payrolls that measure employment levels in the economy.

In December 202, employment growth stalled. In fact, non-farm payrolls fell 328,000 in the last month of 2020 to 143,777,000, or 9,400,000 below pre-pandemic peak. December was the first month of declines in employment since April 2020, but employment growth was relatively slow already in November when the U.S. economy added 603,000 jobs, the slowest pace of recovery after July for the entire period of recovery of May-November 2020.

This evidence further reinforces the argument that labor markets conditions in the U.S. remain abysmal, prompting American workers to slip out of the labor force. 

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 2

In the previous post, I covered the first set of data - Continued Unemployment Claims (https://trueeconomics.blogspot.com/2021/02/4221-us-labor-markets-americas-scariest.html) - that highlights the plight of American economy in the current crisis. Now, let's take a look at Labor Force Participation rate and Employment to Population ratio:

The chart and the table above highlight continued serious problems in the structure of the U.S. labor markets. While official continued unemployment claims are inching back toward some sort of a 'norm', much of so-called improvement in unemployment dynamics is actually accounted for by the dire state of labor force participation which is still trending below anything one might consider reasonable. Current labor force participation rate is 61.5 which is well below anything seen before the onset of the pandemic in March 2020. By a mile below. And in terms of historical perspectives, we have no modern recession (from 1980 onwards) that matches these lows of labor force participation. Structurally, this means that instead of gaining jobs, the unemployed simply roll off the cliff of unemployment assistance and drop out of the labor force, discouraged by the lack of meaningful decent jobs in the market. 

Employment to population ratio is a little better, but it is still stuck below pre-pandemic levels and is low compared to prior recessions' troughs. 

The conditions in the U.S. labor markets might be improving somewhat off the pandemic lows, but the situation overall remains dire. 

4/2/21: U.S. Labor Markets: America's Scariest Charts, Part 1

 

Updating my series of America's Scariest Charts, here is the latest reported data (through the week of January 23rd) on continued unemployment claims:

In absolute terms, official continued unemployment claims stood at 4,592,000 during the week of January 23, 2021, 193,000 down on week prior and 935,000 down on the month prior. The four weeks-average rate of decline in continued claims is at 120,000 per week, an improvement on 4-weeks average of 103,250 weekly rate of decline a week ago, but worse than 177,250 average rate of decline recorded a month ago.

Mapping the same series in comparison to other recessions:

The log scale ameliorates, visually, the extreme nature of unemployment dynamics during the current recession, which is now into its 46th week running. Compared to all prior recessionary episodes, current week 46 reading is still the worst of all post-WW2 recessions. 

Some recent research (reviewed here: https://trueeconomics.blogspot.com/2021/02/3221-cost-of-trumps-failures-to-act-on.html) suggests that U.S. policy errors in dealing with pandemic could have increased infection rates by 8.7-14.2 percent. Translating these potential effects into unemployment suggests that more robust public policy interventions at the Federal level could have, potentially, reduced current unemployment rolls by some 425,000-693,000.

3/2/21: The Cost of Trump's Failures to Act on Covid19: Case of Housing Market Interventions

 

COVID-19 pandemic has been associated with a range of deep and dramatic policy interventions, including rolling lockdowns, monetary and fiscal policies interventions, wide ranges of subsidies and supports, but also measures relating to addressing the risk to households and companies arising from the pre-pandemic financial commitments. 

One of the most, potentially, impactful measures has been adoption of a range of policy interventions that aimed to reduce the impact of income shocks on housing availability. In addition to targeting reduction of financial burden of the pandemic shocks on households, the measures also targeted the objective of lowering the risk of spread of the disease via promotion of housing stability.

A recent paper, by Jowers, Kay and Timmins, Christopher D. and Bhavsar, Nrupen and Hu, Qihui and Marshall, Julia, titled "Housing Precarity & the Covid-19 Pandemic: Impacts of Utility Disconnection and Eviction Moratoria on Infections and Deaths Across US Counties" (January 2021, NBER Working Paper No. w28394: https://ssrn.com/abstract=3772641) looked into the effectiveness of housing markets interventions in the latter context. 

Per authors, "housing precarity, which includes both the risk of eviction and utility disconnections or shut-offs, reduces a person’s ability to abide by social distancing orders and comply with hygiene recommendations."

The authors found that 

1. "...policies that limit evictions are found to reduce COVID-19 infections by 3.8% and reduce deaths by 11%.
2. "Moratoria on utility disconnections reduce COVID-19 infections by 4.4% and mortality rates by 7.4%."

"Had such policies been in place across all counties (i.e., adopted as federal policy) from early March 2020 through the end of November 2020, ... policies that limit evictions could have reduced COVID-19 infections by 14.2% and deaths by 40.7%. (emphasis is mine) [While], for moratoria on utility disconnections, COVID-19 infections rates could have been reduced by 8.7% and deaths by 14.8%."

These are genuinely huge numbers. Assuming the effects are non-additive, the lower end estimate of human losses to Covid19 pandemic due to the Trump Administration's failure to act coherently and resolutely in imposing similar policies to support households' tenancy in rental and mortgages markets across the U.S. is in the range of > 40 percent. If the effects are additive, the magnitude of the preventable deaths rises to well over 50 percent.

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.