The global cost of the crisis

Based on IMF forecasts, I calculate the global cost of the crisis as foregone (because of the pandemic) global economic activity up until 2024. The cost amounts to USD 23,600bn, a quarter of global output in 2019. The cumulative output loss is almost twice as large in developing and emerging economies as in advanced economies, though China is an outlier. The calculation represents a lower bound on the final cost, as economic activity might not recover until 2024. Also, the calculation does not include health-induced costs, the inclusion of which would further increase the cost.

As the final (at least for the time being) part of my analyses of the cost of the crisis (link and link), I present here my calculation of the global cost of the crisis.

I calculate the global cost of the crisis as the cumulative loss of global economic activity due to the crisis. This crisis is a health-induced crisis, though. In my previous posts, I calculated both the loss of economic activity and health-induced costs for Denmark. I am not aware of methods to estimate the size of health-induced costs on a global level. Hence, I restrict this analysis to the loss of global economic activity, but discuss health-induced costs.

Expected growth rates

I base my calculations on IMF forecasts for global economic activity. I take IMF’s forecasts from October 2019, when nobody expected the arrival of the pandemic, and compare them to the recent January 2021 IMF forecasts for global economic activity. Figure 1 summarizes IMF’s expectations:

Figure 1. Growth in global output during 2020 and the average annual growth rate during 2021-24. IMF forecasts from October 2019 and January 2021.

In October 2019, IMF expected global output to increase by 3.4% in real terms during 2020. There was no talk about a pandemic. Instead, the major risk mentioned was the uncertainty surrounding global trade and geopolitics (Trump and China).

2020 turned out to be so different. In their recent January 2021 WEO update, IMF now expects global output to contract by 3.5% in 2020. This means that the forecast error (= expectation in October 2019 – realized growth in 2020) amounts to 7%-point.

The pandemic and the resulting lockdowns caused this unprecedented recession. As a comparison, global output contracted by 0.1% “only” because of the financial crisis in 2009. In 2020, as mentioned, the contraction amounts to 3.5%. Globally, this recession has been so much more severe than the one following the financial crisis in 2009.

We do not have the final figures for 2020 yet. The 3.5% contraction for 2020 is a very good guess, though, given that we have the figures for the first three quarters of 2020 and higher frequency indicators for the last quarter, such as industrial production etc. Hence, I would be surprised if the final figure for 2020 turns out to be much different from this number, i.e. from a 3.5% reduction in global output.

Figure 1 also shows that global growth expectations for 2021-2024 have been revised upwards, compared to what was expected before the pandemic. In October 2019, IMF expected global output to increase by 3.6% per year on average during 2021-24. In January 2021, IMF expects the world economy to grow by 4.2% per annum during 2021-24. So, 2020 was worse than expected before the pandemic but 2021-24 is expected to be better. What do the revisions imply for the expected cumulative loss of economic activity during the 2020-2024 period?

The global loss of economic activity

Before the pandemic, in 2019, aggregate global output amounted to app. USD 87,500bn. In this figure, I calculate the path of global output expected before the pandemic (using growth rate expectations from October 2019) and contrast it with the path expected now, i.e. taking into account the actual 2020 development and current (January 2021) forecasts for global growth during 2021-24:

Figure 2. Paths of real global output, based on IMF forecasts from October 2019 and January 2021. Billions USD.
IMF WEOs and own calculations

In October 2019, the expectation was that global output would amount to app. USD 90,500bn in 2020 (in real terms). Instead, global output amounts to app. USD 84,500bn in 2020. In 2020, the world earned app. USD 6,000bn less because of the pandemic.

Figure 2 reveals that global output will most likely remain below the level expected before the pandemic during the next couple of years, in spite of the increase in expected growth over the next four years that Figure 1 illustrated.

In aggregate, during the 2020-2024 period, the difference between what was expected before the pandemic and what was realized in 2020 plus what is expected going forward from here amounts to USD 23,600bn. This is my estimate of the global cost of the pandemic. It is the value of foregone economic activity in 2020 plus the expected value of foregone economic activity up until 2024.

USD 23,600bn is a big number. To illustrate, it is 27% of 2019 global output. A quarter of one year’s global output has been lost due to the pandemic. It also (more or less) corresponds to the value of everything that is produced (GDP) in the still-largest economy in the world, the US, during one year. Or, more or less, one and a half times everything produced in China in a year. As a final illustration, it is app. 12 times Biden’s new stimulus package of USD 1,900bn.

Advanced vs. Developing and Emerging economies

The pandemic is global. The cost of the pandemic will not be shared equally among Advanced and Developing/Emerging economies, though.

This figure shows that the forecast error for 2020, i.e. the difference between expected 2020 growth and actual 2020 growth, was equally large for Advanced and Developing/Emerging economies. For both Advanced and Developing/Emerging economies, growth during 2020 turned out to be 6%-7%-points lower than expected before the pandemic:

Figure 3. Forecasts error for 2020 and forecast revisions for 2021-24 for output growth in Advanced and Developing/Emerging economies.

Advanced economies have provided substantial fiscal and monetary support to households and firms in 2020, as described elsewhere on this blog, helping to contain the contraction. Also, vaccines are expected to be widely available in Advanced economies during 2021, supporting the recovery as of summer 2021.

In contrast, it will take longer for vaccines to be rolled out in Developing/Emerging economies. Also, oil exporting and tourism-dependent Developing/Emerging economies have suffered particularly much, IMF mentions.

China differs from most other Developing/Emerging economies, as China has been successful in containing the pandemic after the initial outbreak in early 2020. Also, China has provided substantial fiscal and monetary support.

In total, this means that the cost of the pandemic will not be shared equally around the world. The output loss due to the pandemic will be 17% of 2019 GDP for Advanced economies, but will be almost twice as large, 31%, in Developing and Emerging economies. In Chinas, it will only be 7%:

The cost of the crisis in different parts of the world. Costs are the reductions in economic activity from 2020-2024 because of the pandemic, in relation to output in 2019.
IMF WEOs and own calculations.


There is considerable uncertainty surrounding the recovery. Will vaccines be rolled out according to plan, will we face new mutations that vaccines do not protect against, will consumers increase spending as much as we expect when restrictions as removed, etc.?

IMF presents up- and a downside forecasts that reflect this uncertainty. The calculations above are reasonably robust to the future risk scenarios. The reason is that the cost is mainly due to the contraction in 2020, and there is little uncertainty surrounding that by now.

Figure 4 presents the path of world output as expected before the pandemic and how it is expected to develop as of now, including up- and down-side scenarios:

Figure 4. Paths of real global output, based on IMF forecasts from October 2019 and January 2021. The January 2021 forecasts include an upside and a downside scenario. Billions USD.
IMF WEOs and own calculations.

The magnitude of the cost of the crisis is not much affected by this uncertainty. The cost of the crisis is reduced to 24% (from 27%) of 2019 global output in the positive scenario but increases to 29% of 2019 global output in the downside scenario.

Health-induced costs

This analysis calculates the cost of the crisis in terms of foregone output around the world. The crisis is a health-induced crisis, though. In my previous posts that estimated the costs of the crisis in Denmark , I factored in the economic value of the costs of premature mortality, health impairments, and mental health impairments.

A similar global calculation requires estimates of the value of a global statistical life, as well as estimates of the number of people experiencing severe health impairments because of the virus, as well as the number of people facing mental health impairments. I know of no good way to calculate these such on a global level. Hence, in this post, I calculate the part of the cost due to foregone economic activity. This is certainly an important number to know in itself.

To give one perspective on the health-induced costs, though, Cutler & Summers (link) estimate that the economic value of health-induced costs in the US is, largely, equal to the loss of economic activity for the US. They estimate the total US loss at USD 17 trillion and the loss of economic activity in the US to USD 7.5 trillion, i.e. the loss of US economic activity corresponds to app. 50% of the total US loss. I estimate that health-induced costs are significant for Denmark, too (link and link), but relatively smaller than those calculated by Cutler & Summers for the US.


The global recession caused by the pandemic has been unprecedented in terms of both its magnitude, its cause, as well as the policy response. Based on IMF forecasts for global economic activity up until 2024, I calculate the global cost of the crisis in terms of foregone economic activity around the world. The global cost of the crisis amounts to app. USD 23,600 bn. It is an unfathomable number. It is the value of the reduction in cumulative global output resulting from the crisis. It is income that would have been generated around the world was it not for the pandemic. It corresponds to a quarter of total economic output in 2019.

As I do not have good estimates of global health-induced costs, I restrict the analysis to foregone economic activity. On the one hand, this means that the analysis is not an estimation of the total cost of the crisis (loss of economic activity + health-induced costs). On the other hand, it is certainly important to know the value of the loss of economic activity in itself.

I base my calculation on IMF data. In October 2019, IMF presented forecasts up until 2024. Hence, my calculations run until 2024. As shown in some of the figures above, global economic activity will in 2024 most likely not have reached the level expected before the crisis. Hence, the eventual global cost of the crisis might end out being higher than the number presented in this analysis.

The cost of the second wave

Early December 2020, I presented my calculation of the expected cost of the corona crisis in Denmark, taking into account both economic and health-related costs (link). Since then, the situation has turned to the worse, and the expected cost of the crisis has increased by something like 50%. The calculation here is done for Denmark, but given similar types of waves in the US, the UK, and many other countries in Europa, I would expect similar types of consequences.

This graph shows the sad development in the number of people dying with corona in Denmark since March 11, 2020, on a daily basis.

Daily Covid-19 associated deaths in Denmark. Blue columns: March 11, 2020 through November 26, 2020. Red columns: November 27, 2020 through January 6, 2021.

The blue columns show the daily number of deaths up until November 26, 2020, when I did my initial calculation. The red columns show the numbers since then. From March through November, i.e. including the summer months when few people got infected, approximately 100 people died with corona per month. During the past six weeks, approximately 100 people died per week. During December, more people died with corona than during April.

Economic activity suffers

As a consequence of the dire situation, tough restrictions have been introduced: Max. five people are allowed to meet, people are working from home, “everything” (bars, restaurants, schools, universities, most retail shops, etc.) is closed, etc. Similar restrictions have been imtroduced in many other countries.

Because of the restrictions, economic activity suffers. Forecasts for economic activity have been revised.

In my calculation from early December, I used forecasts from the independent Danish Economic Councils (link). I used their “main scenario” from their autumn 2020 report. The Councils also published a negative scenario. Many economists, including the Council itself, and including myself by the way, now expect that this is a much more likely path for economic activity going forward.

Danish GDP forecasts.
Source: Danish Economic Councils

In their “main scenario”, the Councils expected GDP to fall by 3.6% in 2020, strongly rebound by 3.8% in 2021, and grow by 2.3% per year on average during 2021-2025. Now, a more likely path for Danish GDP is that it falls by 4.2% in 2020, grows by 1.5% in 2021 and by 2.5% per year on average during 2021-2025. In other words, due to the worsening of the situation since late November, the fall in economic activity during 2020 will probably be larger and, in particular, the rebound during 2021 will be smaller and delayed. Given that we start out 2021 in lockdowns, it is more reasonable to expect something like 1.5% growth in 2021, instead of 3.8%.

This figure collects the expected path of GDP without the coronacrisis (“Structural GDP w.o. corona”), the path expected in autumn, and the path that I and many other economists expect now (“Negative scenario”)

Danish GDP forecasts. DKK billion. Main and negative scenarios from Autumn 2020 projections vs. expected path of structural Danish GDP assuming no corona crisis.
Source: Danish Economic Councils

As is obvious from the graph, the expected reduction in GDP is just so much bigger now. More precisely, the expected loss in GDP due to corona (the reduction in GDP due to corona) is now app. DKK 400bn. This is basically two times the loss of economic activity expected just one month ago. This illustrates the costs of an additional wave of the coronacrisis.

Health-induced costs

Health-induced costs depend on the number of deaths, as described in my December blog post (link). In my December post, I assumed that the rate of deaths prevailing at the time of writing would continue for one more year. This was an assumption similar to the one used by Cutler & Summers for the US. This accumulated to 2100 expected deaths in total in Denmark. I argued that this was an aggressive assumption, given that I hoped vaccines were about to be rolled out, but it was the one used by Cutler & Summers, so I used that, too, such that I could compare costs for the US and Denmark.

Now, unfortunately, the assumption does not look that far off. During the past six weeks only, the number of deaths has increased by 75% in Denmark, from 800 to 1400. Given the severity of the current wave, and the slow roll-out of the vaccines, I do not feel comfortable changing my assumption with respect to the health-induced costs.

Updated expected cost of the crisis

This table collects my updated expected economic costs of the corona crisis in Denmark, together with my December expectation:

My estimate of the expected total cost of the crisis has increased from DKK 336bn to 536bn. This corresponds to 25% of pre-crisis Danish GDP, up from 16%. It also corresponds to app. DKK 90,000 per Dane (app. USD 15,000), compared with my best guess of DKK 60,000 per Dane in late November/early December (app. USD 10,000). In just six weeks, due to a terrible second wave of the corona crisis, the cost of the crisis has increased by something like 50% on a per-capita basis.


Obviously, there is a lot of uncertainty surrounding calculations such as these. The mere fact that six weeks of new data can change the economic outlook so much bear this out. Also, I use forecasts from the independent Danish Economic Councils. I expect all forecasters to agree that the economic outlook has turned to the worse, but exactly how much probably differs from forecaster to forecaster.

It is good with one number, DKK 536bn, as we then have something to remember and compare with. With so much uncertainty, on the other hand, we should not be surprised if the final cost will differ from this forecast. In general, whether we talk about forecasts of costs of crises, general economic forecasts of GDP, interest rates, exchange rates, or whatever, it is seldom that forecasts are spot on.

Forecasts such as these help us, though, to understand the magnitude of the crisis. The cost of the crisis (in Denmark) will be in the hundreds of billion Danish kroner. Of this, I am confident. Right now, I believe that something like DKK 500bn, or 25% of GDP, is a good estimate. This estimate might change, but I am reasonably confident that the cost of the crisis will end up being measured in double-digit percentages of GDP. This is the important take-away, not the exact number.

Given these high costs, we should do everything possible to try to reduce them. In particular, the faster vaccines are rolled out, the better. What is difficult to comprehend, I think, is that measures to expand vaccine production capacities were not taken in autumn, when everybody could see that the vaccines were on their way (e.g., by licensing production of vaccines to factories with spare production capacity, etc.). I thus cross my fingers that the rate of vaccination will increase significantly within a not too distant future, thereby helping to contain the otherwise very high cost of this crisis. A Danish newspaper had this cartoon yesterday, link, showing me arguing for the importance of a fast rollout of the vaccines.


It’s this time of the year. This post recalls events of 2020. It has been such an unusual year, so different from what we expected. Luckily, there seems to be light at the end of the very long and dark tunnel, and – I hope – that 2021 will be considerably more joyful than 2020.

2020 started out so well. The roaring twenties and all that. Wuhan was a city I had not heard of, corona a beer people tell me is best served ice-cold with a slice of lime (I do not drink beer, tough I do enjoy wine), and social distancing words we would only get to know too well. Today, we know that Wuhan is a Chinese city with more than eleven million inhabitants and a marketplace where it presumably all started, corona also means something terrible, and social interaction is an activity we have come to miss so dearly.

At the time of writing, app. 75 million cases of corona/COVID-19/SARS-CoV-2 have been confirmed globally and app. 1.7 million people have passed away because of corona. Most countries have been in lockdowns, many still are (again), and the social and economic costs of the crisis have been enormous.

I started this blog in April 2020. This had nothing to do with corona. I had wanted to set up a blog for some years (people ask me where I find time for this, and I really do not know, but seemingly I simply like writing economic stories and analyses). Starting the blog in April this year, however, naturally implied that many of the blog posts have dealt with various economic and financial aspects of the pandemic. In this post, I will review some of the learnings from 2020.

The worst recession on record. With the highest growth rate on record

The recession started in February 2020 in, e.g., the US. Initially, it was caused by a supply shock: lockdowns were imposed and firms could not sell their goods and services and households could not go shopping. In April, when the IMF released their Spring Outlook, they labelled it “The Great Lockdown”. This was a suitable label. The IMF also noted that “This is a crisis like no other” and that “many countries now face multiple crises—a health crisis, a financial crisis, and a collapse in commodity prices, which interact in complex ways”. As unemployment and bankruptcies increased, households and firm got nervous, and demand suffered, too.

The path of economic activity has been highly unusual. This graph shows the quarterly percentage changes in US real GDP since 1947:

Quarterly percentage changes in US real Gross Domestic Product. 2020 encircled.
Source: Fed St. Louis Database

2020 is very much an outlier. On average, from 1947 through 2020, real GDP has grown by 0.8% per quarter. Until 2020, quarterly growth had never exceeded 4%. Economic activity had never contracted by more than 2.6%. Then came the Great Lockdown. During the second quarter of this year, economic activity contracted by 9%. This is almost four times more than the otherwise worst contraction on record. In this sense, it was the worst recession ever.

It has also been the weirdest recession ever. During this recession, we have also witnessed the highest growth rate on record: economic activity expanded by 7.4% during Q3. This is twice as much as the otherwise highest growth rate on record.

This puzzling feature of the recession led me wondering what a recession really is (link). I expressed sympathy with members of the NBER Recession Dating Committee. They face a particularly difficult task this year. Should they conclude that we had one V in spring, with the recession ending in late April, and then a new V now, i.e. two separate Vs (VV), or that we have had one long recession with a double dip, i.e. a double-V (W)? Does it make sense to call it a recession when we experienced the fastest rate of growth in economic activity on record? If you conclude that the economy cannot be in recession when it expands at its fastest growth rate ever, then you must conclude that the recession ended during Q2. But, the NBER Recession Dating Committee has not called the end of the recession yet, i.e., officially, the recession is still ongoing.

You may ask why it is important to know whether the recession ended in April or whether it is still ongoing. The development in economic activity is what it is, whether we call it recession or not. It is important because a “recession” is such an important concept in economics. We inform the public, business leaders, students, and others about the characteristics and consequences of recessions. If a recession can contain the by-far strongest expansion of economic activity on record, we need to change our understanding of recessions.

The very unusual behavior of economic activity during Q2 and Q3 caused very unusual, and scary, developments in unemployment and related aspects of economic activity. This graph shows the monthly change in the number of unemployed in the US:

Monthly changes in the number of unemployed in the US. Millions. 2020 encircled.
Source: Fed St. Louis Database

During March, unemployment in the US increased by 16 million. Again, this was beyond comparison. Until March 2020, the number of unemployed had never increased by more than one million over one month. In March 2020, it increased by 16 million.

As the virus contracted during summer, unemployment fell. There has never been as fast a reduction in the number of unemployed as the one occurring during this summer. In May, the number of unemployed dropped by more than three million. Until May 2020, the number of unemployed had never fallen by more than one million over one month. In May 2020, it fell by more than 3 million. So, within a year, we have had the strongest-ever increase in unemployment, but also the largest-ever fall in unemployment. By far.

Such dramatic events happened all around the world. I documented this here (link) and here (link).

Inspired by these events, I did something admittedly nerdy. I calculated the probability that we would experience events such as these, given the historical data (link). I found the unconditional likelihood that we could see the increase in newly registered unemployed that we saw in spring to be 0.97 x 10^(-841). This is a zero followed by 841 zeroes and then 97. For all practical purposes, this is a zero-probability event. But it did happen. It was just very, very unusual.

The stock market

I use some of my time (a significant part, by the way) to try to understand the stock market. This has not been a straightforward task this year.

Today, the global stock market is 13% percent above its January 1 value, the US stock market is 18% higher, and the Danish stock market 29% higher (MSCI country indices). Given that we have been through the worst recession ever, and that the recession is not officially over yet, this is not what one would have expected prior to the events.

Then, on the other hand, in hindsight it is perhaps not so strange. The recession has been the worst on record, yes. But, we have also had the fastest growth in economic activity on record. I argued (link) that if we imagine that the recession ended in late April, when economic activity bottomed out, the behavior of the stock market fits perfectly well with the historical evidence on the behavior of the stock market.

Central banks have certainly played their role, too. When markets melted down in March, central banks intervened heavily. In contrast to the financial crisis of 2008, it was not banks that were in trouble this time, but firms. Firms could not sell their goods and services due to the lockdown. The limitless purchases of government bonds that central banks have become used to during and after crises thus probably did not do much good (evidence came out that central bank purchases of government bonds are less effective than we are often told, link ). What turned things around, instead, was the announcement on March 23 that the Fed would facilitate credit to firms (link). This was a new policy tool. It led to a complete turnaround of events. I produced this graph (I still think it is a supercool graph): 

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries (Left hand axis) as well as the SP500 inverted (Right hand axis). Both series normalized to one on January 2, 2020. Vertical line indicates March 23.
Source: Fed St. Louis Database

The graph shows how the stock market lined up with credit spreads. Firms were suffering, and their credit spreads started widening, in late February. The stock market suffered. The Fed announced it would provide credit to firms on March 23. Credit spreads tightened. The stock market cheered. The graph summarizes how the Fed saved credit and equity markets. And, strikingly, the Fed did so by merely announcing they would intervene. Up until today, the Fed has not intervened a lot. In this sense, it was a “Whatever it takes moment of the Fed”.

It should be mentioned that the Fed announced other initiatives on March 23, too, such as the Main Street Lending Program (link) and the Term Asset-Backed Securities Loan Facility (link). The Corporate Credit Facilities were the ones that directly targeted corporate bonds, though. Due to the nature of this crisis, the stock market lined up with credit spreads during this crisis, as the above graph reveals, emphasizing the importance of the announcement of the Corporate Credit Facilities.

Eurozone troubles, or rather no Eurozone troubles

The fact that we have not had to talk a lot about the risk of a Eurozone breakup since summer has been a positive surprise. In spring, there was talk about the risk of a Eurozone crisis. Like so often before, Italian sovereign yields rose relative to German sovereign yields. There was reason to be anxious. I argued that “Some kind of political solution at the EU level would be needed” (link).

This we got. The European Union agreed on a “Recovery and Resilience Facility” (link) that includes both loans and grants. EU has moved one inch closer towards a common fiscal policy. Who will pay is not clear, but EU has shown solidarity. I believe this is positive. At the same time, the European Central Bank continued its interventions and bought a lot of Italian debt. This has kept yields on sovereign bonds low. Here is the Italian-German yield spread during 2020:

Italian yield spread towards Germany. Ten-year government bonds. Daily data: January 2, 2020 – December 21, 2020.
Data source: Thomson Reuter Datastream via Eikon.

Italian yields have been falling continuously since summer, when the EU agreed on its recovery plan. It is positive that we have not had to discuss Eurozone troubles. We have had so many other troubles. Whether this means that we do not have to discuss Eurozone troubles again at some point, I am less sure. But, that is for another day.

Banks have been doing OK

The risk of a Eurozone breakup did not materialize. Another risk that did not materialize was the risk of systemic bank failures. This is positive as well, as economic activity suffers so much more when banks run into trouble and credit consequently does not flow to its productive uses.

During the worst days in March, stress in the banking system intensified. For instance, the spread on unsecure interbank lending increased relative to secure lending:

The TED spread. Difference between 3-Month USD LIBOR and 3-Month US Treasury Bills. Daily data: January 2, 2000 – December 14, 2020. 2020 encircled.
Data source: Fed St. Louis Database.

Stresses lasted only a few days, though. During the financial crisis in 2008, on the other hand, spreads remained elevated for much longer. This time, trust in the banking system was quickly reestablished.

I think I am allowed to claim that this was one of the predictions I got reasonably right. In autumn 2019, when nobody knew about the upcoming crisis, I wrote a policy paper on the Nordic financial sector. It was presented in December 2019 and finally published in June this year (link). I argued that banks are safer today, compared to 2008. Some doubted my conclusion and said, “just wait until the next crisis, then you will see that banks are not safer today”. Well, few months later we had the worst recession ever. Luckily, though, we have not had bank-rescue packages and we have not had to bail out banks. Banks have been withering the storm. In some instances, banks have even been part of the solution by showing flexibility towards troubled firms. I am not saying everything is perfect, but I am saying that the situation has been very different from the situation in 2008. On a personal note, this made me happy, too, as it would have been somewhat embarrassing if banks had failed at the same time I published an analysis arguing that the banking sector is safer. This, luckily, did not happen. Instead, the banking system turned out to be far more resilient than in 2008, as I predicted.

You may add that the Fed rescued markets during spring, as mentioned above, and thereby rescued firms and subsequently banks. True, but there was certainly also tons of rescue packages in autumn 2008. Banks nevertheless failed in large numbers in 2008. They did not this time around. Perhaps, thus, we did learn something from the financial crisis of 2008, and have gotten some things right. This would be no small achievement.

US election and Brexit

There have been other events, for instance the US election and Brexit negotiations. In normal years, such events would potentially have been among the most important events for markets and the economy. This year, the pandemic has certainly been more important. I did manage to write a post on the US election and the stock market, though (link). I discussed evidence that stock markets perform better under Democratic presidents. Only time will tell whether the same will happen under Biden.  

I did not manage to find space to discuss Brexit, but we got a trade agreement on Dec. 24 (link). Hopefully, the EU and UK can now move on.

The cost of the crisis

It is impossible to summarize the pandemic in one number or one word. Hence, I will not attempt to do so. But, I did present a calculation of the expected cost of the crisis in Denmark (link). I arrived at DKK 336bn, or app. USD 10,000 per Dane. This calculation generated some attention in Denmark.

One can discuss every single assumption one needs to make when calculating the expected cost of a crisis: What is the value of a statistical life? What is the value of a statistical life of those who pass away due to COVID-19, i.e. who are typically above 80? What is the past loss as well as the expected future loss in economic activity due to the crisis? Does it make sense to present one number when there is so much uncertainty? And so on. These are all fair points, but if we want to have a meaningful discussion of the impact of the crisis, we have to start somewhere.

In my calculation, I closely followed the assumptions of Cutler & Summers, such that US numbers and Danish numbers can be compared. This allowed me, for instance, to conclude that the cost of the crisis in Denmark, most likely, will be much lower than the cost of the crisis in the US.


I must admit I find it difficult to end this last post of 2020 on a happy note. Right now, at the time of writing, the situation is bad in the country I live, Denmark, and in many other countries in Europe and around the world. Numbers of new cases and deaths have been rising recently, or are on the rise again, and more and more restrictions and lockdowns are being imposed. Days are grey and short. The crisis has already been tremendously costly and it is clearly not over yet.

Nevertheless, I will try to end the post on a positive note. It gives me hope that several countries have started vaccinating people, and it seems to be working well. Finally, the EU also starts vaccinating people now. This has taken way too long, however, given the severity of the crisis and the fact that other countries started weeks ago. And, yes, every day counts. If it is correct, though, and I deliberately write if, that the EU has failed when it comes to the approval process and purchase of vaccines, as the normally well-informed and serious magazine Der Spiegel claims (link), it is a scandal. Biden aims to vaccinate 100m Americans within his first 100 days in office (link), close to a third of the US population. As things look now, it seems unlikely that we will be able to achieve the same in Europe. Christmas is all around us, though, so let us hope that somehow things will develop in the right direction.

Therefore, let me focus on the bright side. With the jabs, the situation will most likely start to improve within a not too distant future. I will try to convince myself that I see weak light at the end of the long and dark tunnel, even when we probably have to wait many months before things really calm down. Days are at least getting longer. I will focus on this, then.

With this, which is meant to be a positive message, let me thank you all for reading this blog and for sending me many encouraging mails with feedback. Please keep on doing so – it is highly appreciated.

I conclude by expressing hope that next year will be considerably more joyful than the one we leave behind.

Happy New Year!

The cost of this crisis

The corona crisis has caused a loss of economic activity. To calculate the total economic cost of this health-induced crisis, we must factor in health-related costs. Based on calculations for the US by Cutler & Summers, this post calculates the economic cost of the crisis in Denmark. Many uncertainties surround such calculations, and I discuss those. It seems a robust conclusion, though, that the cost of the crisis in Denmark will be considerably smaller than the cost of the crisis in the US. The broader implication of this result is that there is considerable variation across countries in the economic cost of the pandemic.

Economic activity suffered dramatically during spring, and it will take time before economic activity has recovered to its pre-crisis growth trend. In addition to the loss of economic activity, we must take into account health-related costs, if we want to estimate the total economic cost of the crisis. This is no easy task. Inevitably, it will be based on a number of assumptions. In a recent Journal of the American Medical Association article, Harvard University Professors David M. Cutler and Lawrence H. Summers (link, link) present a calculation of the total economic cost of the corona crisis in the US. Their calculation takes into account both the cost of lost economic activity as well as the economic value of premature mortality, the economic value of health impairments, and the economic value of mental health impairments.

Cutler & Summers (CS) find huge economic costs of the corona crisis in the US. They estimate the economic cost at USD 16 trillion. This is 90% of US GDP. This exceeds the cost of the financial crisis in the US by a wide margin, Cutler & Summers argue.

This post presents the first calculations of the cost of the crisis in Denmark. I follow the procedure of Cutler & Summers, but use Danish data. Knowing results from other countries, we will be able to better judge whether results for the US are globally representative.

I stress that these are first calculations and that uncertainties surround them. But, if we want to have a meaningful discussion of the cost of this crisis, we have to make assumptions, and then discuss their robustness. I do so.

Lost GDP

The first component of the calculation of the economic cost of the crisis is loss of economic activity. CS look at forecasts for US Gross Domestic Product from the Congressional Budget Office right before the crisis and compare it with the latest forecasts. The difference is the loss of economic activity. CS calculate a loss of USD 7,600bn. This corresponds to 35% of US pre-crisis GDP.

In Denmark, the Danish Economic Councils publish independent forecasts for Danish GDP. In their latest autumn 2020 report, the Councils publish figures for the expected development in structural Danish GDP, had there been no corona crisis. They also estimate expected GDP as of now. The results are here:

Danish GDP forecasts. DKK billions. Autumn 2020 forecast (“GDP”) vs. expected path of structural Danish GDP assuming no corona crisis.
Source: Danish Economic Councils

The difference between now-expected GDP developments and expected GDP developments without the corona crisis is the loss of Danish economic activity due to the crisis. This amounts to DKK 214bn (app. USD 40bn) for the years between 2020 and 2024, when Danish GDP is assumed to have recovered to its without-corona crisis structural trend. This loss of economic activity corresponds to approximately ten percent of Danish 2019 GDP.

One reason why the loss of economic activity in Denmark is expected to be considerably smaller than the loss in the US (10% in Denmark vs. 35% for the US) is that the Congressional Budget Office (CBO) expects the loss of economic output to persist in the US. CS reproduce this figure from the CBO:

US GDP forecasts. July 2020 forecast vs. January 2020 forecast.
Source: Cutler & Summers (2020).

Even ten years out, in 2030, US GDP is expected to be lower than its pre-crisis expected growth path. This is different in Denmark. In Denmark, the most recent forecast indicates that economic activity will have recovered in 2024. This reduces the loss of GDP in Denmark, relative to the US.

Cost of premature mortality

Deaths add to the economic cost of the crisis. CS note that 190,000 Americans had passed away due to Covid-19 by late September 2020. This is 0.06% of the US population.

In late September, when CS wrote their report, 5,000 Americans passed away per week as a result of Covid-19. CS expect this to continue for another year, i.e. an additional 260,000 deaths. CS estimate excess non-Covid-19 deaths at 40% of Covid-19 deaths. This means 1.4 x 450,000 = 625,000 American deaths.

In the US, a statistical life is estimated at USD 10m. CS reduce this by 30% to be conservative. All in all, this results in an economic costs of USD 4.4 trillion due to premature mortality. This is 20% of US GDP.

In Denmark, 816 have passed away as a result of COVID-19, at the time of writing this post (early December). In late September (to compare with the CS US figures), 650 had passed away. This is 0.01% of the Danish population.

This figure shows daily deaths due to Covid-19 in Denmark since March 2020:

Daily Covid-19 associated deaths in Denmark.

During the last couple of weeks, around 25 Danes have passed away because of Covid-19. Assuming, like CS, that this will continue for another year, this accumulates to 1,300 additional deaths. Assuming 40% excess non-Covid-19 deaths, like CS, results in 2,900 deaths over the next year.

In Denmark, a statistical life is estimated at DKK 34m (link). This corresponds to app. USD 5m. In other words, the value of a statistical life in Denmark is only half the value of a statistical life of an American. To be conservative, like CS, I use 70% of this. This gives an economic cost resulting from premature deaths of DKK 79bn. This is around 4% of Danish GDP. I.e., again, the loss in Denmark seems to be considerably smaller than the comparable loss in the US (4% of GDP vs. 20% of GDP). This is because fewer Danes are expected to pass away because of Covid-19 and because the value of a statistical life is assumed to be considerably smaller in Denmark.

Health impairments

Some of those surviving Covid-19 will face significant long-term health complications. CS mention that there are approximately 7 times as many survivors from severe or critical Covid-19 diseases as there are Covid-19 deaths, and that a third of these will experience long-term complications. CS assume that the cost of this is 35% of a statistical life and that it lasts for one year. This adds USD 2.6 trillion, or 12% of US GDP.

In Denmark, using the procedure of CS, expect 2,100 deaths, as mentioned above. This means 4,900 individuals with long-term health impairments over the next year. Assuming that the complications last for one year, and assuming an economic cost of complications of 35% of a statistical life, this means an economic loss of DKK 41bn, or 2% of GDP. Notice, like CS, I use the conservative value of 70% of a statistical life, i.e. 35% of 0.7 x 34m.

Mental health impairments

Many people get anxious or fell depressed during the pandemic. This could be because people need to isolate at home, and thus face loneliness, because of fear of losing your job, i.e. economic insecurity, fear of contracting the virus, etc. CS report that 40% of American adults have reported symptoms of depression or anxiety during the corona crisis. Normally, CS report, 11% of Americans report these symptoms. CS report that previous studies evaluate the one-year cost of depression and anxiousness at USD 20,000 per case. This amounts to an additional cost of USD 1.6 trillion, or 7.5% of US GDP, for the corona crisis.

In Denmark, we do not have official data on the number of people feeling anxious or depressed during the corona recession. What we do have, on the other hand, is the comprehensive health report of the Danish population from 2015 that also estimates the economic costs of a number of diseases in Denmark (link).

This report mentions that 136,000 Danes suffer from anxiousness and 91,000 from depression (in 2012). In total, around 4% of the Danish population. The report calculates an aggregate cost of lost economic activity of DKK 8.6bn for anxiousness and DKK 3bn for depression, primarily as a result of early retirements. These costs relate to 2012. Since then, inflation has been 7% in Denmark, i.e. the 2020 value is DKK 12.4bn (app. USD 2bn), assuming the same number of depressed and anxious individuals. CS assume an almost fourfold increase in the number of individuals facing depression and anxiousness because of the corona crisis. Given that this number is associated with a large degree of uncertainty in Denmark, and because one might hope that many people will recover, as this is a temporary crisis, such that not all of them will enter into early retirement, I double this number (in contrast to CS who assume a fourfold increase). I.e., I assume that the economic cost of mental health impairment amounts to DKK 24bn (app. USD 4bn), or app. 1% of Danish GDP.

Total costs

In total, taking into account the loss of economic output as well as health costs, reflecting both premature deaths, long-term health impairments, and mental health impairments, the economic cost of the pandemic in Denmark amounts to DKK 336bn, or 16% of Danish GDP:

This corresponds to approximately DKK 60,000 per Dane (app. USD 10,000). For a family of four, this means that the economic cost of the crisis is app. DKK 240,000 (app. USD 40,000).


A number of implications follow from these calculations:

  • A cost to society of 16% of GDP is an enormous cost.
  • With this in mind – that a 16%-GDP cost is enormous – it also follows immediately, on the other hand, that the cost of the pandemic in Denmark is considerably smaller than the cost of the pandemic in the US. CS find, as mentioned, that the cost of the pandemic in the US will amount to 90% of US GDP. For Denmark, I find it will be 16% of Danish GDP. This implies that there is considerable variation across countries in the expected cost of the pandemic. Or, in other words, that it would be a mistake to assume that the cost of 90% GDP found for the US is a globally representative figure.
  • There are several reasons why the cost of the pandemic is considerably lower in Denmark:
    • Danish GDP is expected to recover faster than US GDP. Danish GDP is expected to have recovered in 2024. On the other hand, US GDP is not expected to recover (reach its pre-crisis growth trend) within the next ten years. This implies a larger negative effect of the pandemic on the US economy.
    • Fewer individuals have passed away in Denmark due to the pandemic. In late September, 0.06% of the US population had passed away. In Denmark, “only” 0.01% had passed away. This reduces the cost of premature mortality in Denmark.
    • The value of a statistical life is lower in Denmark. According to CS, in the US, it is USD 10m. In Denmark, it is USD 5m. This also reduces the cost of premature mortality in Denmark.
    • The numbers indicate that fewer people in Denmark will face health impairments, compared to the US. This also reduces the economic costs in Denmark, compared to the US.
  • CS find that the cost of the pandemic exceeds the cost of the financial crisis by a large margin. In 2012-2013, I chaired the official committee investigating the causes and consequences of the financial crisis in Denmark (link). We found that the financial crisis – at the time of writing the report in 2013 – had caused a DKK 200bn reduction in GDP. In 2008, when entering the financial crisis, Danish GDP was DKK 1,800bn, i.e., the accumulated loss up until 2013 amounted to 11% of pre-financial crisis GDP. Given that Danish GDP had not recovered to its pre-crisis trend in 2013, the final loss of the financial crisis is larger. It seems that the economic cost of the financial crisis and this pandemic will be, more or less, equal.

Uncertainties and assumptions

Many uncertainties surround the calculations presented above. In this section, I discuss some of them and their consequences. Notice as a starter, though, that by relying on the assumptions of CS above, I am able to compare results for Denmark with results for the US. If using other assumptions than CS, this is less straightforward. This – that it is easier to compare across countries when using the same underlying assumptions – is an argument for using the CS assumptions. Nevertheless, to see how sensitive results are, let us discuss what happens if using other assumptions.

Will Danish GDP have recovered in 2024, as the Danish Economic Councils expect? If not, the cost will be higher. If sooner, the cost will be lower.

Will 25 Danes pass away per week over the next year? This seems like a high number these days, given that we expect vaccines to be ready within the next few weeks. If “only” 650 (i.e. half the assumed number in the base-line calculations) additional Danes pass away, the cost of premature mortality will be reduced from DKK 71bn to DKK 49bn, and, all other numbers equal, the total cost will be reduced to DKK 300bn, or 14% of GDP. I.e., the baseline calculation is reasonably robust towards this adjustment.

The cost of mental health impairments in Denmark is associated with considerable uncertainty in this calculation, as we do not have official numbers for how Danes have been affected by depression and anxiousness during this pandemic. The overall baseline calculation is reasonably robust towards this number, though. For instance, I double the 2012 number in the baseline calculation. If I multiply with three, i.e. assume even more people are affected by mental health challenges, the total cost increases from 16% of GDP to 17% of GDP.

Finally, I, like CS, assume that the cost of premature deaths is 70% of the value of a statistical life. More than half – 52% – of Covid-19 related deaths in Denmark have been above 80 years old. Even when I, like CS, mark down the value of a statistical life by 30%, the number might still seem high. Reducing this further would of course lower the total cost of the pandemic marginally.

Conclusions and policy implications

This post presents first calculations of the expected economic cost of the Covid-19 pandemic in Denmark. Taking into account the direct effects on GDP, as well as the economic value of premature deaths and health consequences, I expect the economic cost of the pandemic to be 16% of Danish pre-crisis GDP. The calculation is based on a number of assumptions, but the overall figure seems robust to reasonably variations in these assumptions.

Harvard Professors Cutler & Summers expect that the cost of the pandemic in the US amounts to 90% of US GDP. The cost in Denmark will most likely be considerably smaller. Only a fifth of the cost in the US. This is due to a faster economic recovery in Denmark, a lower number of deaths in Denmark, a lower value of a statistical life in Denmark, and lower associated health costs. This large difference between the US and Denmark implies that there is considerable cross-country variation in the cost of the pandemic.

Cutler & Summers argue that the cost of this pandemic will exceed the cost of the financial crisis in the US. In Denmark, it seems that the cost of this pandemic will correspond, more or less, to the cost of the financial crisis.

Even when the cost of the pandemic in Denmark is considerably lower than the cost in the US, the cost is still enormous. 16% of GDP is a very large figure. For a family of four, it corresponds to DKK 240,000 (app. USD 40,000).

When the cost is high, it becomes even more important to roll out vaccines as fast as possible, as soon as health authorities have approved them. Call in retired doctors, call in retired nurses, use all available working personnel, and pay them all handsomely to work 24/7 in order to vaccinate as many as possible as quickly as possible, from a health perspective and an economic perspective. This will help contain the already very high cost of this pandemic.

VV or W: When did (or does) this recession end?

This corona recession started in February 2020. Officially, it is still ongoing. But, perhaps, it has in fact already ended. This might seem confusing but it helps explaining the performance of financial markets during this “recession”.

In my soon-to-be-released book From Main Street to Wall Street (link and link), I – among many other things – carefully examine the historical relation between the business cycle and financial markets. I verify that stock markets typically perform considerably better during expansions than recessions. In the book, I examine and explain why this is so. I also explain that this is not a bulletproof finding. It is not always so. Sometimes stock markets do fine during recessions. Is this recession one of them?

This recession

In the US, the Business Cycle Dating Committee (link) determines peaks and troughs in US economic activity. On June 8, 2020, the Committee announced that:

“The committee has determined that a peak in monthly economic activity occurred in the US economy in February 2020. The peak marks the end of the expansion that began in June 2009 and the beginning of a recession. The expansion lasted 128 months, the longest in the history of US business cycles dating back to 1854.”

This means that this recession started sometime in February 2020. It also means that the expansion preceding this recession became the longest on record.

The Committee has not declared the end of this recession yet. Officially, the US economy is still contracting, with the caveat that the Committee determines business-cycle turning points with a lag. E.g., it was in June only that the Committee concluded that the recession had started in February.

How has the stock market performed during this recession? As readers of this blog know, it has performed well since March 23 when the Fed had is “Whatever it takes moment” (link). During this recession, the US stock market has returned 10% up until today (total return on the MSCI USA index). This is particularly noteworthy in light of the fact that this recession has been unusually severe (link and link).

Perhaps the recession ended in April

This corona recession is special. Usually, recessions are caused by some economic imbalances that need to be corrected, such as an overvalued housing market or an implosion of the financial sector. This is not the case here. This recession was caused by the sudden arrival of a virus. The sudden arrival of the corona virus caused a sudden shutdown of economic activity.

The Business Cycle Dating Committee has traditionally relied on aggregate economic data when determining turnings points in the business cycle. Aggregate data, such as industrial production, GDP, etc., are available at the monthly or quarterly frequency. Given the underlying cause of this recession, which led to the closure of business activities from one day to the next, we need to look at higher-frequency data when searching for turnings points in economic activity.

One interesting new higher-frequency indicator is the Weekly Economic Index developed by the New York Fed (link). It summarizes information in ten weekly indicators of real economic activity. It is designed to track the four-quarter growth rate of real GDP. As the St. Louis Fed notices (link) ”This series is potentially a useful indicator to watch because James Stock (one of the authors of the study developing the index) is a member of the BCDC (Business Cycle Dating Committee).”

The interpretation of the index is that it is “scaled to the four-quarter GDP growth rate; for example, if the WEI reads -2 percent and the current level of the WEI persists for an entire quarter, one would expect, on average, GDP that quarter to be 2 percent lower than a year previously.” The Weekly Economic Index is available since January 2008:

FRED Graph
Weekly Economic Index for the US economy.
Fed St. Louis Database.

The index tracks the financial crisis well. In particular, as the figure shows, it bottoms out in Q2 2009 when the recession ended.

The indicator also tracks the beginning of this recession well. It fell like a stone from a rock when the recession started in February.

The interesting point here is that the indicator reaches its bottom during the last week of April 2020. I.e., if a recession ends when this indicator bottoms out, as in 2009, this recession ended in late April.

The idea that this recession ended in late April would also be consistent with the observation that US real GDP dropped dramatically from the first to the second quarter of 2020, and rebounded strongly from the second to the third quarter:

Quarterly percentage changes in US real Gross Domestic Product.
Source: Fed St. Louis Database

What about international evidence? I am not aware of a well-designed index that tracks, e.g., Eurozone economic activity at the weekly frequency. So, as an alternative when looking at data from other countries than the US, let us consider industrial production. Industrial production is a traditional business-cycle indicator. It is available at the monthly frequency:

Indices of industrial production, normalized to one in January 2019, for the US and the Eurozone.
Source: Fed St. Louis Database and Thomson Reuter Datastream via Eikon.

For the US, industrial production bottomed out in April, like for the weekly indicator above. The collapse in Eurozone industrial production is much larger than in the US, but the timing is the same. Also for the Eurozone, industrial production bottoms out in April. So, perhaps this recession really ended in April.

The stock market and the recession

Typically, stock markets tank during the early phase of a recession, and starts rebounding when the end of the recession is in sight, i.e. before the actual end of the recession. Furthermore, the stock market normally does well during the early phases of expansions.

This graph splits the cumulative return on the US stock market into a mid-February (start of recession) to late April (assumed end of recession) period, in red, and a period thereafter, in green:

Total return index for MSCI USA, normalized to one mid-February 2020. Daily data. Red line indicates assumed recession. Green line indicates assumed early phase of expansion.
Source: Thomson Reuter Datastream via Eikon.

If we decide that this recession ended in late April, instead of assuming that the recession is still ongoing, the behavior of the stock market makes perfect sense. In this case, the stock market lost 14% during the recession. Since May, i.e. since the assumed end of the recession, the stock market has gained 27%.

Zooming in on the correlation between the Weekly Economic Indicator and the cumulative return on the US stock market, it follows that the stock market bottomed out before economic indicators started improving. This again emphasizes that the reason for the turnaround in the stock market in March was the “Whatever it takes moment of the Fed on March 23” (link). Since late April, however, the stock market and economic conditions have moved in tandem, i.e. economic conditions have improved and so has the stock market:

Total return index for MSCI USA and Weekly Economic Index. Weekly data
Source: Fed St. Louis Database and Thomson Reuter Datastream via Eikon.

Did the recession really end in April, then?

We do not know. It is, as mentioned, the NBER Business Cycle Dating Committee that determines peaks and troughs in the US economy. They have not called the end of the recession yet. Hence, officially, the US economy is still contracting.

I am happy that I am not a member of the NBER Business Cycle Dating Committee. This time, it must be particularly difficult to decide whether the US economy (as well as the European economy, by the way) is really out of the recession or not. As we all know, numbers of new cases are on the rise again in the US and have been doing so for a while in Europe. This will hurt economic activity going forward. So, should one conclude that there was a recession from February through April, or should one conclude that the recession is still ongoing. This is no easy question.

VV (two single Vs) or W?

Did the recession start in February and stop in April? Are we now entering a new recession due to a rising number of corona cases and a subsequent slowdown in economic activity? Or, are we still in the recession that started in February? One way to try to judge this is via NowCasts, i.e. daily forecasts of what growth in economic activity will be the current quarter. This is related to, but different from, the Weekly Index above. It is an estimate of the growth rate of GDP during the current quarter, but based upon data available at a higher frequency. Here is the NowCast from the New York Fed:

NowCasts of US quarterly growth in real GDP. Daily forecasts of growth during the current quarter.
Source: New York Fed

It has a clear V-shape during the second quarter. During April/May/June, incoming data indicated that the fall in GDP during Q2 would be enormous, as it turned out to be (see figure above with actual GDP growth). Incoming data during Q3 indicated that Q3 growth would be high, as it turned out to be. Q4 growth is expected to be considerably lower than Q3 growth, i.e. a new V, but growth is still expected to be positive.

Why is it important whether the recession lasted from February through April only (one V), whether we left the recession in April/May but enter a new now (two Vs, i.e. VV), or whether we are still in the recession, but economic growth was high during Q3 but expected to be low during Q4 and possibly Q1 2021, i.e. W? The reason is that it influences how we should think of recessions and financial markets during recessions.


Historically, stock markets have suffered during recessions. This recession started in February. Since then, the US stock market has gained 10%, seemingly in contrast to its usual behavior during recessions. Economic activity bottomed out in late April, though. The US stock market lost 14% from February through April. Since then, it has returned more than 25%. So, whether you conclude that the stock market has suffered during this recession or not depends on your favorite definition of when the recession ended, if at all until today.

To get the official answer, we have to wait for the NBER Business Cycle Dating Committee.

For the future path of the stock market, the question will be how the number of new infections develop and their impact on economic activity.

For the history books, i.e. for the conclusion of whether the stock market performed surprisingly well during this recession or not, the question is whether the recession ended in April or hasn’t ended yet. Did we have one V from February through April, do we get a new V now, so this ends up being VV, or are we still in the recession, i.e. W?

If Biden wins

If history is any guide, it will be four good years on the stock market if Biden wins on November 3. Historically, stocks have performed so much better under Democratic presidents. The question is whether history will be a guide also this time around.

Are Democratic or Republican presidents better for the stock market? To evaluate, let us recall the performance of the US stock market under Trump – a Republican – and compare it to its performance under Obama – a Democrat and Trump’s predecessor. Afterwards, let us look at the full history of the stock market under Democratic and Republican presidents. Finally, let us discuss what it implies for this election and the next four years on the US stock market.

Obama vs. Trump

This graphs shows the cumulative return to USD 1 invested in respective January 2009 (Obama 1st term), January 2013 (Obama 2nd term), and January 2017 (Trump). I show real returns, i.e. returns after inflation:

Cumulative real returns to US large-cap stocks under Obama and Trump. Own calculations.
Data source:

A couple of months remain, but during most of Trump’s presidency, the stock market has performed worse than under Obama. The exceptions are the first few months of Obama’s first term and the beginning of this year, right before the corona crisis.

In numbers, one US dollar invested in the US stock market in January 2009, when Barack Obama – a Democratic president – was inaugurated, had turned into 2.68 USD in December 2016 (when Obama’s second term ended) in real terms, given reinvestments of dividends. This is a total accumulated real return of 168 percent over eight years, or an average annual return of 13%.

One US dollar invested in the US stock market in January 2017, when Donald Trump – a Republican president – was inaugurated, had turned into 1.46 USD in September this year (in real terms). This is a total accumulated return of 46 percent over four years, or an annual average real return of 10% (there are still three months to go of Trump’s presidency, so the numbers might change a little in the end).

Some extraordinary events influenced the stock market under both Obama and Trump.

Early 2009, the stock market was still suffering from the financial crisis of 2008. Obama was inaugurated in January 2009. A few months later (in March 2009), the stock market turned around. Following March 2009, many great years on the stock market followed.

This year, 2020, has seen the fastest bear market in history (in March; link), caused by the corona crisis, hurting the stock market’s performance during Trump’s presidency.

The financial crisis of 2009 and the corona crisis were very unusual events. Perhaps the stock market’s performance under Trump and Obama was unusual.

It turns out that the picture painted above – that the stock market performs better under Democratic presidents – is robust. In fact, the US stock market has historically performed much better under Democrats.

The Presidential Puzzle

Pedro Santa-Clara and Rossen Valkanov published in 2003 a paper (link) entitled ”The Presidential Puzzle”. They documented an intriguing stylized fact: Over the 1927-1999 period, the excess return on the US stock market has been nine percentage points higher under Democratic than Republican presidencies. The average excess returns under Democratic presidencies, they showed, was 11 percent versus 2 percent only under Republican presidents. A nine-percentage point difference is enormous. For instance, it exceeds the excess return on the stock market in general, i.e. the return stocks provide over and above the return on risk-free assets.

Santa-Clara and Valkanov took great care in investigating potential reasons for this stylized fact. The most obvious explanation, financial economists would suggest, is that this is simply a compensation for risk, i.e. that risks have been higher under Democratic presidents. It turns out that this is not the case. In the end, their conclusion was:

”There is no difference in the riskiness of the stock market across presidencies that could justify a risk premium. The difference in returns through the political cycle is therefore a puzzle. ”

Pastor and Veronesi (link) update the results of Santa-Clara and Valkanov. They add app. twenty years of data (until 2015, i.e. their sample runs from 1927 through 2015). Pastor and Veronesi find even stronger results. In their extended sample, they find that the average return under Democratic presidents is eleven percentage points higher than the average return under Republican presidents.

Pastor and Veronesi have this figure with average annual excess returns under each president:

Average excess returns during individual presidencies.
Source: Pastor and Veronesi (2020).

The figure shows that the only Democratic presidency that delivered returns significantly below the average return over the whole period (the dotted line) was Roosevelt’s second term (1937-41). In contrast, a number of Republican presidents have delivered subpar returns (Nixon, first Reagan term, Bush Jr.). My small calculation in the beginning of this post, that returns have been higher under Obama than Trump, strengthens this conclusion.

Pastor and Veronesi show that this finding is robust during subsamples, for instance if excluding the large negative and positive returns during the 1930s. They also show that it is particularly the first year of the presidency that drives these differences. During the first year in office, Democrat presidents have experienced a 37%-points (!) larger return than Republican presidents have:

Difference between average excess returns in the returns during the presidents’ early years in office.
Source: Pastor and Veronesi (2020).

(If you are interested, here is a detailed account of the stock market’s performance under every single president since Truman: link).

There is some discussion whether this finding (that the stock market does better under leftwing presidents) holds internationally. Pastor and Veronesi show in the (114 pages…) appendix to their article that it holds internationally while others claim that it does not (link). We leave this aside here.

Democratic presidents are elected when times are bad

Pastor and Veronesi develop an interesting explanation of this intriguing pattern of the data. They argue that the reason for the difference between the stock market’s performance under Democratic and Republican presidents has nothing to do with economic policies during presidencies but everything to do with the economic situation when new presidents are elected.

Pastor and Veronesi argue that Democratic presidents are elected at points in time when risk aversion is high. Remember, for instance, the election of Obama in autumn 2008. This was right in the middle of the financial crisis. The worst financial and economic crisis since the 1930s. Everybody was afraid and uncertain how things would evolve. Risk aversion was high. When risk aversion is high, investors demand high compensation for taking on risks in the stock market. Subsequently, investors get compensated by high average returns.

So, the story of Pastor and Veronesi is that Democratic presidents get elected when times are bad. When times are bad, voters have a tendency to vote for left-wing candidates, as voters demand more social insurance during hard times. An important implication of this theory is that Democrat presidents do not cause higher stock returns (and Republican presidents do not cause lower stock returns). Instead, Democratic presidents are elected when the economy is suffering and risk aversion is high, causing high expected returns and subsequent high average returns.

Implications for this election

If (and I write if) it is correct that Democratic presidents get elected when risk aversion is high, and that good returns subsequently follow, what does this imply for this election and the next four years on the stock market?

Pastor and Veronesi suggest a number of proxies for risk aversion. To illustrate, let us look at two of them. The first is unemployment. The idea is straightforward: when unemployment is high, people are afraid of taking on risk on the stock market. Risk aversion is high and so are expected returns.

Here is unemployment in the US since 1947:

US unemployment rate.
Source: FRED.

Unemployment is currently high, at 7.9% in September (latest figure at the time of writing). This is more than two percentage points above the historical average rate of unemployment of 5.8%. Unemployment is coming down, though, and fast. Also, there are still two weeks to go until the election. Nothing is for sure. But, as it looks now, this indicates high risk aversion, and thus high expected returns.

Another measure of risk aversion is the habit-persistence idea of Campbell and Cochrane (link). If your consumption is low today, in relation to your past consumption, you feel that times are bad. If you are used to go on holiday a couple of times per year, but now you don’t dare because you are afraid of losing your job, you also become afraid of taking on risk in the stock market. Your risk aversion is high. You require high expected returns if you should be convinced to nevertheless invest in stocks.

Let me present a simple illustration here, inspired by Atanasov, Moeller, and Priestley: link. I take consumption (real per capita) and divide by habit. I approximate habit by the average of the past three years of consumption (this is a blog, so this should be OK), i.e. the habit ratio is here: C(t)/(AVG[C(t-1)+(C(t-2)+…..+C(t-12]), where C(t) is consumption in period t and AVG is the average. I do this calculation for every quarter since 1950. The result is the following time series:

Consumption habit ratio. Own calculations.
Data source: FRED.

When an entry in the figure is above one, consumption during that quarter is higher than its past three-year average, and times are good. When the habit-ratio is low, times are bad. The habit ratio (current consumption to habit) thus has a tendency to drop around recessions (1980, 2008, etc.), as, during recessions, people cut consumption in relation to past their consumption.

The lower is the habit ratio, the higher is risk aversion and thus expected returns. Currently, due to the enormous drop in consumption resulting from the corona-crisis, the habit-ratio is historically low. Times are bad. Consumption is low. Biden should win (according to this theory). Risk aversion is high. Expected returns should be high. The next four years on the stock market should be good.

There is uncertainty, of course

Many things can happen until November 3 and the next four years. And, for sure, this has been a really weird campaign. Nothing is for sure.

Some reservations:

My figure with the habit-ratio is based on quarterly data (consumption is quarterly). The last entry is Q2 2020. Since Q2, the stock market has been sprinting ahead, implying that the potential for future returns is, all else equal, lower now than in Q2.

Also, financial markets have been pricing in an enormous amount of event risk surrounding this election. The uncertainty surrounding this election is high. Protection against event risk is expensive (link, link, and link).

At the same time, the stock market has been doing really well since March 23 (link). This is unlike recessions in general. During normal recessions, the stock market tanks. This stock market rebound since spring, everything else equal, reduces the potential for future returns as of now.

On the other hand, at least right now, the polls seem to offer some support for the story. We are in a big downturn (corona crisis). In downturns, people have a tendency to vote left-wing. As we all know, Biden leads the polls at the time of writing:

Poll average. October 16, 2020.


The stock market has performed worse under Trump than under Obama. This is not an outlier. Historically, the US stock market has performed much better under Democratic presidents.

One explanation why the stock market performs well under Democratic presidents is that Democratic presidents get elected during bad times, when risk aversion is high.

Currently, we are in the midst of a terrible situation (corona crisis). Supporting this theory, Biden leads the polls. If history is any guide, this indicates good years ahead on the stock market. Not necessarily because Biden – if he wins – implements policies that support the stock market (perhaps he does, but this is not the point of the theory), but because risk aversion is high right now, at the time of the election.

Of course, so many things can happen. This certainly has been a strange (and, at least for many Europeans, myself included, a very weird and scary) campaign to follow from the sideline. The performance of the stock market during this recession has also been weird. It is important to recognize that the stock market rebound since March reduces the potential for future returns as of now. And, in general, so many things can happen during the next four years. Perhaps the situation will thus play out differently this time around. We will know how the election turns out in two weeks, and we will know how the stock market performs during the next presidency in four years and three months. It will be interesting to follow.

Expected returns, autumn 2020 updates

Today, October 1, the Council for Return Expectations publishes its updated expectations. We expect very low – negative – returns on safe assets over the next five and ten years. We expect an equity premium around 5-7 percent.

I chair the Council for Return Expectations (link). Twice a year, we update our expectations. Today, we publish our latest forecasts.

In a June post (link), I described the history of the council, how we operate, who we are, why we publish expected returns, what they are used for, and so on. Briefly, here, the Council consists of Torben M. Andersen (professor of economics, University of Aarhus; link), Peter Engberg Jensen (former CEO of Nykredit and current chairman of Financial Stabilitet; link ), and myself as Chairman. Based on inputs from Blackrock, J.P. Morgan, Mercer, and State Street (thanks!), we estimate expected returns, risks (standard deviations), and correlations between returns on ten different asset classes over the next five and ten years. We also publish expected returns on two assets classes (stocks and bonds) for investment horizons exceeding ten years.

The forecasts are important in Denmark. When Danish pension funds project how the retirement savings of their customers will most likely develop (with confidence bands), they base their projections on the Council’s return expectations. Similarly, when banks advice Danish investors on their non-retirement savings, they use the expected returns provided by the Council. I think it is fair to say that Denmark has been a front-runner in designing a system where banks and pension funds do not compete on expected returns, but all use a common set of return assumptions, determined by an independent Council.

The forecasts published today will be used for pension projections and projections for savings outside retirement accounts as of January 1, 2021. In spring 2021, we will publish updated expectations that will be used from July 1, 2021, and the following six months. And so on.

The timing is as follows. The expectations we publish today are based on market data from July 1, 2020. We use the period from July 1 until today (October 1) to determine our expectations. The expectations we publish today are then used by banks and pension companies as of January 1, 2021.

The numbers

We publish forecasts for expected returns on ten asset classes over the next five years, years 6-10, and over the next ten years. Returns are average annual returns in Euros/Danish kroner (the Danish kroner is fixed to the euro). These are our expectations:

Source: Council for Return Expectations

We expect an investment in safe assets (defined as a five-year duration portfolio of 30% Eurozone government bonds (minimum Triple-B), 20% Danish government bonds, and 50% Danish mortgage bonds – Danish mortgage bonds are triple-A rated, by the way) to lose money every year on average over the next ten years. This is noteworthy. On average, we expect such an investment to lose 0.1% per year over the next ten years. Over the next five years, we expect it to lose 1.2% per annum. The reason why we expect such low returns is that the underlying bond portfolio has a duration of five years, as mentioned, and we expect a normalization of interest rates, causing capital losses which drag down expected returns.

We expect lower returns from bond investments than we did in our June update (link). In June, we expected government and mortgage bonds to return a negative 0.3% per year over the next five years and a positive 0.3% per year over the next ten years. The reason why we expect lower returns now (-1.2% and -0.1% for the five and ten year periods, respectively) is that our forecasts today, as mentioned, are based on market data from July 1. On July 1, interest rates and credit spreads were lower than those used at our previous update. Our previous update was based on March 31 market data. In March, the corona virus had caused a hike in yields and a widening of credit spreads. Since then, yields and spreads have come down.

Global equities are expected to yield around 6% per year. This means that the equity premium is expected to be 5%-7%, depending on the investment horizon.

We expect low inflation. We expect Danish inflation to be 1.2% per annum over the next five years and 1.5% over the next ten years.

For investment horizons above ten years, we expect equities to return 6.5% per annum and bonds 3.5%. This is the same as we expected last year (we update long-run expectations once a year). Over the coming year, we will make a thorough evaluation of these long-run expectations.

We also publish standard deviations for all asset classes and for all horizons, as well as correlations and fees. Standard deviations are high for high-return assets; nothing comes for free. We thus publish many numbers. You can find all these numbers on the webpage of the Council: link.


We (The Council for Return Expectations) expect that Danish investors will lose money – even before fees and inflation – if investing in safe assets over the next ten years. You can expect positive returns on other asset classes, but then you need to take on risks.

Historically, the risk-free real interest rate has been around 2% per annum and the nominal interest rate around 4%. Investors would see their savings grow, even without taking on risk. This is not what investors should expect going forward. The fact that you cannot expect your portfolio to grow without taking on risk tells us a lot about the low-return world we are living in.

Quantitative Easing (QE) and biases in research

Do asset purchases by central banks raise economic output and inflation? An interesting new paper finds an affirmative answer, but also – and this is the main point – that the size of the effect depends on whom you ask. If you ask central bankers, they will tell you that the effect of QE is large. If you ask independent academic researchers, they will tell you it is considerably smaller. This difference indicates that central-bank research on this topic might be biased. It also indicates that Quantitative Easing is probably not as effective as we are told.

One of the defining characteristics of financial markets since the financial crisis in 2008 is the use and influence of “unconventional policy tools” by central banks. As monetary policy rates have been close to zero, central banks have been unable to stimulate the economy via even lower interest rates. Instead, central banks have started purchasing financial assets, mainly government bonds. These alternative policy tools are labelled “asset purchases by central banks”, or simply “Quantitative Easing (QE)”.

Quantitative Easing increases the demand for government bonds, thereby raising their price and bringing down their yields. When yields on government bonds fall, other yields in the economy, such as yields on mortgage bonds, fall, too. This should promote economic activity and raise inflation, central banks argue.

Quantitative Easing is not uncontroversial, though. In several of my posts (link, link, and link), I have argued that it raises other asset prices in the economy, such as stock prices. Some fear that this induces bubble-like behavior in asset prices. Also, QE might distort signals from asset prices, causing unclear signals from prices about the underlying state of the economy and financial markets. In addition, by raising other asset prices, Quantitative Easing might contribute to increasing inequality, as financial assets are typically held by the already wealthy. On the other hand, if QE helps promoting economic activity, it helps reducing unemployment among low-income groups, which should reduce inequality, central banks argue in return (link). In the end, then, to justify Quantitative Easing, it should have a sizeable impact on inflation and output, outweighing the potentially negative effects on other parts of the economy.

Many papers have analyzed the effects of quantitative easing. A brand-new paper (link) summarizes these analyses and asks the question whether results are more positive when central bank economists analyze QE. Given that central banks influence public opinion, the latter question is important when we evaluate the most significant policy intervention during the last decade.

There are two reasons why I think this paper is particularly interesting. First, it summarizes research on QE in a neat way. It concludes that QE is effective, but not as effective as we are often told. Second, it emphasizes the importance of independent academic research. As the faculty representative on the Board of Directors at Copenhagen Business School, stressing the importance of academic research, I find this to be an important conclusion, too.

To avoid any misunderstandings about my own view, let me stress two things before getting to the results.

First, I believe that targeted central bank intervention can be useful. In my last post, I describe one monetary policy intervention that clearly fulfilled its goal (link). During a crisis, if markets are malfunctioning, there can be good reasons for policy interventions. On the other hand, I am skeptical towards the view that the advantages of endless asset purchases by central banks outside crisis periods outweigh their disadvantages. This paper indicates that QE is less powerful than central bankers tell us, lending some support to this view.

Second, my point here is not to say that central bank research is suspicious in general. On the contrary, I strongly recommend central banks to invest in economic research. I believe that better decisions are taken when based on solid academic analyses. So, central banks should be encouraged to invest in research, but their own evaluations of their own actions are probably not unbiased.

The study

Brian Fabo, Martina Jancokova, Elisabeth Kempf, and Lubos Pastor (link) study 54 analyses, written/published between 2010 and 2018, of the effects of quantitative easing in the US, the UK, and the Eurozone. 57% of the papers have been published in peer-reviewed journals. 60% of the authors are affiliated with central banks.

Lubos Pastor and co-authors collect estimates of the effect of QE on economic output and inflation across the 54 studies and report the average (and median) effects. They also investigate whether the reported effects are different if a study is conducted by central bank researchers.

Bias in central bank research

Pastor et al. list five reasons why central bank research might be biased (directly taken from the paper, page 2, here):

  • ”First, the economist may worry that the nature of her findings could affect her employment status or rank. Is she less likely to get promoted if her findings dent the bank’s reputation? Could she get fired?”
  • ”Second, the economist may be unsure whether her research will see the light of day. Bank management could in principle block the release of studies that find the bank’s own policy to be ineffective, or to have undesirable side effects.”
  • ”Third, the economist may suffer from a confirmation bias (Nickerson (1998)). A central bank employee may believe a priori that the bank’s policies are effective, and she may select evidence supporting her prior.”
  • ”Fourth, the economist may care about the bank’s reputation.”
  • ”Finally, the economist may care about her own reputation if she is senior enough to have participated in the formation of the bank’s policy.”

The findings

The main findings of the paper are collected in this graph:

Source: Fabo, Jancokova, Kempf, and Pastor (2020)

Pastor et al. report that a QE-program at its peak, i.e. when a QE program has its maximum effect, on average (across the 54 studies) raises GDP by 1.57% and the price level in the economy by 1.42%, as indicated by the blue columns in the graph (”Average all studies”). This seems to be relatively large effects, I would say.

Do these effects depend on the affiliations of researchers? Pastor and co-authors find that central-bank affiliated researchers report significantly larger effects. If counterfactually changing the share of central bank researchers in a study from 0% to 100%, the peak effect on output is estimated to be 0.723%-points larger and the peak effect on the price level 1.279%-points larger. I illustrate this in the figure above as the “Effect of CB authors”. Compared to the overall average effect, the effect of central-bank authorship is large. For the price level, going from 0% to 100% central-bank authorship almost corresponds to the total average estimated effect of QE on price levels across all 54 papers . In other words, if you think central bank estimates might be biased, the ”true” average effect of QE programs is considerably smaller than the estimated overall average effect.

Pastor and coauthors report the average point estimate of all papers and the additional effect of central bank affiliation, as indicated in the figure above. They do not report the average estimate from papers written by central bank authors, respectively written by academic authors. I asked Lubos Pastor about this. In private email correspondence with Lubos and Elisabeth Kempf, they inform me that papers written by central bank authors (defined as papers with at least one central bank author) estimate a 1.752% peak effect on output. Papers written by academics (defined as papers with zero central bank authors) find a considerably smaller effect, 0.996%. For inflation, the peak effect on output is 1.791% for papers written by central bank affiliated authors. Academics estimate a much smaller effect, only 0.545%. In spite of massive asset purchases (we are literally talking trillions of dollars, euros, and yen), the average effect on inflation is small, at 0.5%, academics report. Less than a third of what central bank affiliated researchers report.

As academics, we are not only interested in the size of the coefficients/effects, but also whether effects are statistically different from zero. Pastor and coauthors report a striking finding here. While all papers written by central bank researchers find that QE has a statistically significant effect on output/inflation, only 50% of papers written by independent academics find significant effects.

Finally, Pastor and co-authors note that the German central bank (the Bundesbank) has been particularly skeptical towards ECB QE. So, what happens if you look at researchers affiliated with the German central bank? Bundesbank researchers find much smaller effects of QE. In fact, Bundesbank researchers find an even smaller effect of QE on economic output than independent academics. Again, this indicates that the preferences of an institution seem to influence the conclusions of its researchers.

The paper presents additional analyses, such as looking more closely at the mechanisms at play, i.e. career concerns, involvement of management in research, and so on. Read the paper if you want to know more about this.


I think the paper by Brian Fabo, Martina Jancokova, Elisabeth Kempf, and Lubos Pastor is interesting also because it summarizes what the average effect of QE is, based on a large number of studies. Across more than 50 papers, the average maximum effect of QE on GDP and the price level is around 1.5%. This is useful information in itself.

Some papers are written by central bank researchers and some by independent academics. It seems reasonable to hypothesize that central bankers might have a tendency to view their own policies in a more favorable light. This is what Lubos and co-authors find. They report that the effect of having central bankers as authors of an analysis is almost as large as the average reported effect of QE on the price level itself. In private email correspondence, they also tell me that the average effect, estimated in academic papers, of QE on the price level is only 0.5%, i.e. very small, almost negligible. This is of course a controversial result. I predict it will generate intensive debate.

The fact that I discuss this paper here should not be taken to imply that I am skeptical towards central bank research in general. In fact, I am sure the quality of monetary policy decisions is improved when central bankers have access to the latest research. Also, I have no reason to believe that central bank research on other topics than monetary policy should be biased. But, when it comes to assessing their own actions, researchers in central banks might be subject to certain biases. It requires some guts to tell senior management that the trillions they have spent on quantitative easing probably has not been very effective. Instead, it might further your career if you paint a rosier picture. This is important to recognize.

I view the bottom line as follows: QE probably has some effect, but its effect is considerably smaller than we are told by central banks.

The Fed’s “Whatever it takes” moment. Or, how the Fed saved equity and credit markets

Facing a looming recession and financial market panics, the Fed intervened heavily in late-February/early-March, lowering the Fed Funds Rate to zero and expanding its balance sheet dramatically. In spite of this, markets kept on panicking. Then, suddenly, on March 23, everything changed. Stock markets started their rally. This was not because the Fed lowered the rate or expanded its asset purchases even further, nor because the economic data improved. What happened? The Fed made an announcement. Nothing else. It is a fascinating illustration of how expectations can change everything on financial markets.  

Much has been written about the massive interventions of the Fed during February and early March. In my previous post (link), I list Fed interventions as one of the reasons why the stock market is back to pre-crisis highs. In this post, I dig one step deeper and explain the fascinating story of how the Fed said something and thereby rescued markets.

Asset purchases and rate reductions did not save markets in February/March

Let us start by illustrating how the actual Fed interventions (interest rate changes and asset purchases) did not save markets in March. The Fed lowered the (lower range of the) Fed Funds Target Range to 1% from 1.5% on March 2 and then again to 0% less than two weeks later, on March 14. At the same time, it bought Treasuries and mortgage-backed securities to the tune of USD 600bn per week. These interventions succeeded in lowering yields on government and mortgage-backed bonds, but did not cheer up stock markets.

This graph shows how Treasury yields came down significantly in February/March, by basically 1.5%-point (from close to 2% to close to 0.5%; I show yields on 10-year Treasuries in this graph), as a result of reductions in the policy rate (the Fed Funds Rate) and asset purchases by the Fed.

SP500 and yield on 10-year Treasuries. Daily data.
Source: Fed St. Louis Database

The graph also shows that the SP500 continued falling throughout February/March. In other words, the massive interventions by the Fed in late-February/early-March (and these interventions really were massive – buying for USD 600 bn per week and lowering rates to zero is indeed a massive intervention) did not convince stock markets that the situation was under control. And, remember, these were not minor stock-market adjustments. It was the fastest bear market ever (link).

What turned the tide?

On March 23, the SP500 reached its low of 2237, a drop of 31% compared to its January 1 value. Since then, everything has been turned upside down and markets have been cheering, as the above graph makes clear.

What happened on March 23? The Fed had its finest hour. It did not do anything. It merely said something. A true “Whatever it takes” moment.

As you remember, a “Whatever it takes” moment refers to the July 26, 2012 speech by then ECB-president Mario Draghi (link). The speech was given at the peak of the Eurozone debt crisis. The debt crisis pushed yields on Italian and Spanish sovereign bonds to unsustainable levels. Italy was too big to fail, but also too big to save. The pressure on Italy was a pressure on the Eurozone construction. Mario Draghi explained the situation and said the by-now famous words:

But there is another message I want to tell you. Within our mandate, the ECB is ready to do whatever it takes to preserve the euro. And believe me, it will be enough.

Investors understood that the ECB would be ready to buy sovereign bonds to save the Euro. Markets calmed down. Italian and Spanish yields fell. And this – and this is the point here – without the ECB actually intervening, i.e. without the ECB buying Italian or Spanish bonds. The announcement that the ECB would intervene was enough to calm down investors.

Why is this relevant here? Because on March 23 the Fed sent out a press release, announcing that:

“…the Federal Reserve is using its full range of authorities to provide powerful support for the flow of credit to American families and businesses.”

And, then, as one of the new features announced the:

Establishment of two facilities to support credit to large employers – the Primary Market Corporate Credit  Facility (PMCCF) for new bond and loan issuance and the Secondary Market Corporate Credit Facility (SMCCF) to provide liquidity for outstanding corporate bonds.

This statement turned everything upside down. The important thing is that this turn of events happened without the Fed using any money at all. It was a “Whatever it takes” moment (though, perhaps not as Dirty-Harry dramatic as Mario Draghi’s “And believe me, it will be enough”): The Fed announced what they would do, investors believed the Fed, and markets started cheering. It is a prime example of how investor expectations influence financial markets.

What are the PMCCF and SMCCFs, why did the Fed announce them, and why were their effects so dramatic?

The lowering of the Fed Funds Rate and the purchasing of Treasuries succeeded in lowering yields on safe assets, such as Treasury bonds, as explained above, but did not lower yields on corporate bonds. In fact, during the turmoil in late-February/early-March, credit spreads (the spread between yields on corporate bonds and safe bonds) widened dramatically. When yields on corporate bonds rise, it becomes more expensive for corporations to finance their operations. And, when yields rise a lot, as in February/March, investors get nervous about the profitability and survival of firms.

This graphs shows how yields on both the least risky corporate bonds (Triple A) and speculative grade bonds (Single B) rose dramatically in relation to 3-month Treasury Bills, with yields on lower-rated bonds (Single-B) naturally rising more than yields on higher-rated (Triple-A) bonds.

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries as well as the difference between yields on ICE BofA Single-B US High Yield Index and 3-month Treasuries. Daily data.
Source: Fed St. Louis Database

The important point in the picture is that the spreads continued rising throughout late-February/March, in spite of the intensive interventions described above, i.e. in spite of Fed purchases of government and mortgage bonds. The Fed was happy that safe yields fell, but was concerned that credit spreads kept on rising. Volatility in corporate bonds markets also rose (link), making the whole thing even worse.

More or less all firms saw their funding costs increase, even when there were differences across firms in different industries, with firms in the Mining, Oil, and Gas, Arts and Entertainment, and Hotel and Restaurant sector hit the hardest, and firms in Retail and Utilities sectors less affected (link). The Fed became nervous because higher funding costs for firms affect firms negatively, causing them to cut jobs, reduce investments, etc.

The Fed decided to act. It announced on March 23 that it would launch two new programs, PMCCF and SMCCFs. The PMCCF is the ’Primary Market Corporate Credit Facility and the SMCCF the Secondary Market Corporate Credit Facility. Primary markets are where firms initially sell their newly issued bonds. The PMCCF should thus ease the issuance of newly issued corporate bonds, i.e. help firms raise funds. Secondary markets are where bonds are traded afterwards, i.e. the SMCCF should ease the trading (liquidity) of already existing corporate bonds.

The effect on equity and credit markets of the announcement of the PMCCF and SMCCFs was immediate and spectacular. Immediately after the announcement, credit spreads narrowed (see, e.g., link, link, and link).

Interestingly, stock markets reacted immediately, too. The stock market, thus, did not react to the lowering of the Fed Funds Rate and the extensive expansion of the Fed balance sheet in late-February/early-March, but reacted strongly to the announcement that the Fed would buy corporate bonds on March 23.

This (supercool, I think 🙂 ) graph shows developments on corporate bond and equity markets. The graph shows the spread between yields on AAA-rated corporate bonds and 3-month Treasury Bills and the stock market inverted, both normalized to one on January 1. The graph for the inverted stock market means that when the SP500 is at 1.45 on the y-axis on March 23, the stock price at January 1 was 45% higher than it was on March 23.

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries (Left hand axis) as well as the SP500 inverted (Right hand axis). Both series normalized to one on January 2, 2020. Vertical line indicates March 23.
Source: Fed St. Louis Database

The parallel movements in equity and credit markets are striking. Equity and credit markets moved in parallel during January, when stock markets rose and credit spreads narrowed, in late-February/early-March when credit spreads widened dramatically and stock markets fell like a stone, as well as after March 23, when both credit spreads and stock markets improved spectacularly. Since then, stock markets have continued to rise and credit spreads have continued to narrow.

The correlation between the two series is an astonishing 0.93 for the January 2 through May 31 period.

The announcement effect

The Fed has experience with and a mandate to buy mortgage-backed securities and Treasuries. It had no such experience when it comes to purchases of corporate bonds and corporate bond ETFs. This means that the Fed could not start buying corporate bonds on March 23, it needed an institutional set-up. It created an SPV with capital injections from the Treasury and leverage from the New York Fed, it asked a financial firm (Blackrock) to help them purchase the bonds, etc. These things take time. The Fed only started buying corporate bond ETFs in mid-May and corporate bonds in mid-June.

Given that the Fed only started buying bonds and ETFs in May/June, the spectacular turnaround on March 23 really was due to the announcement only.

In slightly more detail, it played out as follows. The March 23 announcement primarily dealt with higher-rated corporate bonds (Investment Grade), and spreads narrowed immediately. On April 9, the Fed announced that they would expand “the size and scope of the Primary and Secondary Market Corporate Credit Facilities (PMCCF and SMCCF)”, buying bonds that were Investment Grade on March 22 but had been downgraded since then as well as corporate bond ETF. High-yield spreads tightened even more (link). Today, the Fed explains that the programs allow the Fed to buy “investment grade U.S. companies or certain U.S. companies that were investment grade as of March 22, 2020, and remain rated at least BB-/Ba3 rated at the time of purchase, as well as U.S.-listed exchange-traded funds whose investment objective is to provide broad exposure to the market for U.S. corporate bonds.”

The actual purchases began in May/June, several months after the announcement. Interestingly, the purchases themselves have had a modest impact only. First, the amounts of corporate bonds and corporate bond ETFs bought pale in comparison with the amounts of mortgage-backed securities (MBS) and Treasuries bought. The Fed has bought MBS and Treasuries to the tune of USD 3,000bn this year. It has “only” bought corporate bonds and ETFs for around USD 12bn. The amount used to buy corporate bonds thus corresponds to less than 0.5% of the amount used to buy MBS and Treasuries. This graphs shows the daily purchases. Using the latest figures from August 10, the Fed has basically stopped buying corporates.

Daily Fed purchases of corporate bonds and corporate bond ETFs. Daily data.
Source: Fed webpage. Thanks to Fabrice Tourre for collecting and sharing the data.

So, the Fed has not bought a lot of corporate debt. It has, however, the power to buy a lot. The PMCCF and SMCCF set-up is such that the Treasury has committed to make an USD 75bn investment in the SPV that buys the assets (USD 50bn toward the PMCCF and USD 25bn toward the SMCCF). The New York Fed then has the ability to level this up by a factor of ten, i.e. the Fed can buy corporate debt for up to USD 750bn. This is a sizeable fraction of the total US corporate bond market. This, that the Fed can potentially buy a lot, helps making the program credible and thus helps explaining its powerful impact.


Given the success of the PMCCF and SMCCFs, commentators have started arguing that the Fed should be allowed to buy corporate bonds as part of its standard toolkit (link). This might be relevant, but purchases of corporate bonds by central banks raise a number of dilemmas:

  • Keeping zombie firms alive. By easing up stresses in corporate bond markets, the Fed calmed down markets. This was the intention of the PMCCF and SMCCF announcements and it worked. It eased access to funding for firms, and firms have raised a lot of cash as a consequence (link). It is positive that malfunctioning markets are stabilized, but if central bank intervention makes markets too cheerful it may allow firms that in principle should not receive funding to nevertheless get it. And, thereby, to keep firms alive for too long, and increase firm leverage too much. Basically, the fear is that programs such as the PMCCF and SMCCFs create too many zombie firms. My CBS colleague Fabrice Tourre and his co-author Nicolas Crouzet has an interesting paper that examines this (link). Fabrice and Nicolas find that when financial markets work perfectly (no disruptions), Fed intervention might be detrimental to economic growth. On the other hand, if markets are disrupted, Fed intervention might prevent a too large wave of liquidations. One thus needs to determine when markets are disrupted “enough” to rationalize interventions in credit markets. This is no straightforward task.
  • Distributional aspects. By buying some bonds but not others, the Fed exposes itself to the critique that it helps some firms at the advantage of other. To alleviate such criticism, the Fed has been very transparent and publishes a lot of information about the bonds it buys, the prices at which it buys the bonds, etc. (link). Nevertheless, it is easy to imagine that some firms at some point will start saying ‘Why did you buy the bonds of my competitor, but not my bonds’?
  • The Fed put. The more the Fed intervenes when troubles arise, the more investors get reassured that the Fed will also come to the rescue next time around. When the Fed saves markets, it is sometimes called the Fed exercises the “Fed Put”. The potential problem here is that if investors believe that the Fed will exercise the Fed put, investors will be tempted to take on even more risk. Those of us concerned about systemic risks get nervous.

So, in the end, the announcement of the PMCCF and SMCCFs was crucial during this crisis. There are, however, important dilemmas that need to be addressed when evaluating whether such programs should be part of the standard toolbox. I am not saying they should not. I am saying that one needs to be careful.


The Fed launched massive “traditional” interventions in late-February/early March, lowering the Fed Funds Rate and buying mortgage and government bonds. In spite of these very large interventions, equity and credit markets kept on tail spinning. When firms struggle, it hurts economic activity and employment. The Fed got nervous.

The Fed announced – and this is the whole point here; they only announced – that they would start buying corporate bonds. Markets turned upside down. Credit markets stabilized, credit spreads narrowed, corporate bond-market liquidity improved, and stock markets cheered. And, all these things without the Fed spending a single dime until several months after the fact. Even today, the Fed has spent very little (some might say that USD 12bn is a lot, but compared to asset purchases of USD 3,000bn, it pales).

It was a “Whatever it takes” moment. It illustrates how managing investor expectations can be crucial. Understanding this announcement is thus important for understanding the behavior of financial markets during this pandemic.

The weird stock market. Part II: Potential explanations

The behavior of the stock market during this recession raises three main questions: (i) why did stock markets fall so spectacularly during February/March, (ii) why did stocks rebound so spectacularly during April/May, and (iii) why is the stock market currently at its pre-crisis level? My previous post (link) presented the stylized facts and addressed stories that cannot explain the facts. In this post, I provide some potential explanations.

I my previous post (link), I mentioned that the rebound in the stock market since its low in March cannot be explained by (i) revisions to the economic outlook – in fact, economic forecasts were revised down during spring, (ii) a preference for just a few (FAANG) stocks, as the rebound has been international and broad based, even when large-cap stocks have done particularly well, (iii) a hypothesis that this recession is not particularly bad – in fact, at least in the short run, this recession is much worse than the 2008 recession, or (iv) the fact that markets also recovered after 2008 – markets always recover, i.e. this is not an explanation.

What can explain it, then? First, a warning: I do not want to raise expectations too much (sorry). I will not be able to offer a full explanation of these puzzles in an app. 1,900 words blog post. Also, as you will see, puzzles remain, even if we can explain some things. Finally, I have not seen a good explanation of these puzzling stylized facts (though Gavyn Davies’ piece in the FT is a good place to continue after reading this post; link). Hence, it would not be serious nor academic if I claimed that I could explain everything. What I can do, though, is to provide some hints at what might be relevant parts of an explanation.

Explanations that contain elements of truth

Earnings suffered more in 2008               

In the end, earnings and not economic activity (GDP) determine stock prices. In the long run, there is a strong relation between earnings and economic activity, which is why we focus on economic developments when we try to understand stock markets. The relation is not one-to-one, though, in particular in the short run. Hence, let us discuss earnings.

Earnings suffered dramatically in 2008. Often, we look at 12-month earnings, as quarterly earnings are volatile. 12-month reported earnings per share for the S&P500 came in at 6.86 in Q1 2009, which is app. 10% of 12-month reported earnings in Q1 2008. I.e., earnings fell by a mind-blowing 90% during the financial crisis of 2008.

Earnings fell in Q1 2020, too, but much less: From USD 139 per share in Q4 2019 to USD 116 (12 month reported earnings), i.e. by 17%. At the time of writing, around 90% of companies in the S&P500 have reported earnings for Q2 2020. Earnings (12-month reported) seem to be coming in at close to USD 96 per share, which is a drop of 17%, too, compared with Q1. Earnings have thus dropped by 17% during each of two consecutive quarters. But, and this is the main thing, these drops pale in comparison with autumn 2008. In autumn 2008, earnings dropped by 68% from Q3 to Q4 and then again by 54% from Q4 2008 to Q1 2009. This figure shows the quarterly percentage changes in 12-month reported earnings of the S&P500 in 2008 versus 2020. “0” on the x-axis is Q2 2020, respectively Q4 2008. The drop in earnings was just so much larger in 2008.

Quarterly changes in 12-month reported earnings per share, S&P500. “0” is Q2 2020, respectively Q4 2008. For Q3, 2020 and forward, these are expected earnings.

“1” on the x-axis in the figure is thus Q1 2009 and Q3 2020, i.e. firms’ expected earnings in Q3 2020 (and actual earnings in Q1 2009). If these expectations hold true, earnings are expected to do relatively fine going forward.

Earnings do fall from Q1 to Q2 this year. Earnings surprises matter, too, though, i.e. whether actual earnings are higher (or lower) than expected. At the time of writing, around 80% of companies have reported positive earnings surprises for Q2. This, that actual Q2 2020 earnings are better than expected, helps explaining why stock prices rise currently.

If investors primarily look at earnings, this helps explaining why stock prices fell considerably more in 2008 than they have done during this recession and why the stock-market recovery took longer in 2008. Earnings have simply not suffered so much this time around. For that reason, stock prices have not suffered so much this time around either.

Monetary and fiscal policies have been aggressive

Another factor that helps explaining why stock prices today are at pre-crisis levels, in spite of the severity of the recession, is that policy interventions have been aggressive. Immediately, at the onset of the crisis, monetary policy turned very expansionary. This can be illustrated by the weekly changes in the Fed balance sheet, as in this graph:

Weekly changes in the Fed balance sheet. USD mio.
Data source: Fed St. Louis Database

The figure plots the weekly changes in the Fed balance sheet due to purchases of financial assets, in millions of USD. The Fed balance sheet changes when the Fed buys/sells financial assets. During March this year, there were weeks when the Fed intervened to the tune of USD 600bn. This is an enormous amount of money. It is, by way of comparison, more than twice the weekly amounts spent during the financial crisis of 2008, the figure also shows.

At the same time, yields are very low. This forces investors to buy risky assets if they want some kind of return, supporting stocks.

In addition to very aggressive monetary policy, fiscal policy has also been very aggressive.

Why did stock prices fall during February/March?

OK, so earnings have not fallen as much as in 2008, earnings for Q2 2020 have surprised positively, monetary and fiscal policies have been very aggressive, central banks pump liquidity into the system, and interest rates are very low, implying that investors need to invest their money in risky assets if they want a positive expected return. These features help explaining why stock prices today are not much lower than in the beginning of the year. But, if this is the story, why did stock markets drop so dramatically in March?

Changes to expectations to earnings

Earnings have dropped in Q1 and Q2, even if considerably less than in 2008. Perhaps the stock-price drop in February/March was simply due to a downward revision in expected earnings. Alas, this is not the case. Landier & Thesmar (link) have an interesting analysis where they look at analysts’ expectations to the earnings of S&P500 companies during Q1 and Q2 2020. They find that analysts cut their expectations to earnings of S&P500 firms, but not so much that it can account for the drop in stock prices during February/March. In other words, the drop in stock prices in February/March cannot be explained by analysts reducing their earnings forecasts.

If the drop in expected earnings cannot account for the drop in stock prices in February/March, what happened then? Financial economists have a straightforward way of explaining this. Stock prices are discounted cash-flows. To understand stock price movements, we must understand cash-flow and discount rate movements. When the fall in expected earnings is not large enough to account for the drop in stock prices, discount rates must have increased in February/March. In other words, during February/March investors required a higher expected return if they should invest in stocks. This is also the conclusion in Landier & Thesmar and in Gormsen & Koijen (link). Basically, this is the conclusion in most empirical asset-pricing literature: stock prices move too much to be justified by movements in cash-flows. Discount rate variation is the reason, the literature concludes.

When stock prices move, and it cannot be because of cash-flow movements, it must be discount-rate movements. This is fine, but it is also somewhat tautological: If A = B + C, and A moves but B does not, then it must be because C moves.  A more difficult question is what makes discount rates move in the first place? I.e., what is the deeper economic explanation? Perhaps/probably risk aversion spiked, when investors in February suddenly realized the severity of the virus. Perhaps/probably some market participants faced funding constraints. And so on. In other words, I agree that discount rates most likely spiked during March, but I am not 100% sure why and I do not know if the increase in risk aversion is large enough to account for the increase in discount rates, and thus account for the drop in stock prices.

Lingering doubts

So, a potential reason why stock markets have done better than in 2008 is that earnings have not suffered as much as in 2008 and central banks have flooded the market with liquidity (and yields are at zero). The reason why we saw a massive fall in stock prices in February/March, and an unprecedented rebound during April/May/June, then probably is that discount rates increased during the early phase of the crisis, and then stabilized. This is a story that somehow makes sense.

Two things are still strange (to me at least), though.

First, if stock prices are at their right levels now, why did they have to fall so much in March? OK, because risk aversion increased and funding conditions tightened. But, if risk aversion increased in February because investors understood that a recession was looming, why would investors turn less risk averse three weeks later, when economic forecasts just kept on deteriorating? Probably the Fed and the Treasury (via monetary policy and fiscal policy) eliminated the spike in risk aversion via aggressive policy interventions, but it is still not 100% clear how to reconcile these features of the data.

Second, the behavior of earnings has been surprising. During recessions, earnings normally contract much more than economic activity. So, when economic activity in 2020 contracts much more than in, as an example, 2008, a reasonable hypothesis is that earnings in 2020 would contract much more than in 2008. This has not happened.

To some extent, this graph summarizes both the explanation of the relatively mild response of the stock market to this recession and the remaining puzzle:

Percentage changes in US real GDP and earnings of S^P 500 firms from Q3 to Q4 2008 and from Q1 to Q2 2020.

The figure shows how earnings dropped 68% from Q3 to Q4 2008 but seems to drop only 17% from Q1 to Q2 this year. This explains why the stock market has performed considerably better during this recession compared to its performance in 2008, for instance. On the other hand, the figure also shows that GDP dropped by 10% from Q1 to Q2 this year and only by 2% from Q3 to Q4 2008, i.e. the fall in economic activity is five times larger during this recession. It is puzzling that earnings drop so much less during this recession when economic activity contracts so much more.

So, the relatively modest contraction in earnings helps explaining the relatively fine performance of the stock market during this recession, but it is difficult to understand why earnings have behaved reasonably well. Perhaps this is because banks were suffering in 2008, but are able to help firms getting through this recession. Perhaps it is because firms have been able to adapt better this time. We do not really know. From an academic asset-pricing perspective, it is interesting that the stock market has behaved so differently this time around, compared to how it normally behaves. From an investor perspective, we can only hope that the stock market will keep on behaving in a different way than it usually does, as, otherwise, stock prices will fall going forward.


The facts that earnings have done better than in 2008 and that monetary policy has been very aggressive help us understand why the stock market has done well during this recession. Some puzzles still remain, though. For instance, why have earnings done so reasonably well, given the severity of the recession, and why did risk aversion increase so dramatically in February only to normalize few weeks later, in spite of the worsening recession. Perhaps the sensible answer is that this recession has been unusual in many dimensions, and some things will remain difficult to understand.