Jonathan Swift on the Laffer Curve

But I will tell you a secret, which I learned many years ago from the commissioners of the customs in London; they said, when any commodity appeared to be taxed above a moderate rate, the consequence was, to lessen that branch of the revenue by one half; and one of those gentlemen pleasantly told me, that the mistake of parliaments, on such occasions, was owing to an errour of computing two and two to make four; whereas in the business of laying impositions, two and two never made more than one; which happens by lessening the import, and the strong temptation of running such goods as paid high duties, at least in this kingdom.

From An Answer to a Paper called a Memorial of the Poor Inhabitants, Tradesmen, and Labourers of Ireland, The Works of the Rev. Jonathan Swift, Volume 9

I found this reference in David Hume’s classic essay Of the Balance of Trade which is on the reading list of my International Economics class next Fall.

The radical transformation of the ECB

Students of my generation will remember what we learned about how the European Central Bank conducts monetary policy: The ECB makes one-week loans to commercial banks against top-rated collateral. This was called “Main Refinancing Operations”. The interest rate charged on these loans was known as the Main Refinancing Rate and was considered the key policy rate of the ECB, like the Fed Funds Rate in the United States. Then we learned something about Marginal Lending Facilities and Long-Term Refinancing Operations, but were told they were relatively unimportant.

This was indeed how the ECB operated – before 2009. Since then the ECB has changed its operations. It seems to me that the radical nature of this change has not been recognized enough by economists – let alone the general public.

Look at the chart below. It shows the assets held by the ECB system for purposes of monetary policy operations. The Main Refinancing Operations (the yellow area) have disappeared. In 2019 they constituted a mere 0.25% of the total monetary-policy related assets! The Long-Term Refinancing Operations (blue area) have replaced them and make up about 20% of the total now.

But the elephant in the room is the grey area that first appears in 2009 and then explodes after 2014. The ECB labels it “Securities held for monetary policy purposes”. What are those securities? Government bonds and a couple of corporate bonds as well. The ECB started to buy them with the “Securities Market Program” in 2009 and hugely expanded the purchases with the “Public Sector Purchase Program” in 2015. Last year, the ECB system held 2.6 trillion of assets in relationship with those programs. That is more than 80% of their total policy-related assets.

This means that the ECB of our old textbooks, the ECB that was envisioned by the founders of the euro, has ceased to exist. It has been replaced by an altogether different beast. The primary way in which the ECB conducts monetary policy these days consists in buying Eurozone government bonds in the open market.

This has made the ECB the single biggest lender to Eurozone governments. As I showed in my last post, 91% of all new government debt issued after 2010 is now being held by the ECB. It resembles a 3.2 trillion euro hedge fund, financed by short-term commercial bank deposits (aka “reserves”), holding a diversified portfolio of Eurozone government bonds. The equity owners of this fund are the Eurozone government themselves: they “own” the ECB, they are responsible for replenishing its equity if and when it is deemed necessary.

One implication of this radical transformation should be immediately obvious: Eurozone governments have in effect mutualized 91% of their post-2009 debt. Whenever a Eurozone government defaults on the bonds held by the ECB, the losses would be absorbed, eventually, by the other Eurozone governments.

I’m not saying that’s a bad thing or a good thing. I’m not saying it is illegal or legal. But nobody should delude themselves or others that this is not what has been happing.

How much monetary financing did the ECB provide to Eurozone governments?

Last week, the German constitutional court ruled that the large-scale purchases of government bonds by the ECB since 2015 fell outside the ECB’s legal competences. But the Court also held that this purchasing program did non violate the “prohibition of monetary financing of Member State budgets” in Art. 123 of the TFEU.

It’s unclear whether the ruling will have any effect in practice, because the Court allowed the ECB to continue its program provided they come up with some kind of explanation of what they are doing in the next few months (I’m simplifying).

I admit that I have no idea if the ECB transgressed its legal authorities. But I’m a little bit shocked by the second aspect of the ruling, the finding that the ECB did not provide monetary financing of Member States budgets.

Because the ECB clearly did.

The figure below shows the change in the aggregate outstanding debt of all Eurozone governments (blue line) as well as the change in the ECB’s holdings of such debt (red line) since 2010.

All Eurozone governments combined have issued 1.845 trillion euros* in new debt since 2010. During the same time, the ECB has increased its holdings of Eurozone government debt by 1.683 trillion euros. Ergo, the ECB has bought 91 cents of every euro of new debt issued by Eurozone governments. Notice also the clear break in the red curve in the year 2015. That’s exactly when the PSPP started.

How can this not be “monetary financing” of Eurozone governments?

*) All debt numbers here refer to face values, not market values, and are not adjusted for inflation.

Some descriptive COVID-19 regressions

Having raised the bar so incredibly high with my last post, I now want to bring it down again and show you some unsophisticated data analysis.

Every day now you can see people comparing countries on performance in this pandemic all over the place. How well ist Germany doing? How does the UK compare to France? And what about Sweden: should we have followed their hands-off approach?

All of these comparisons lack one fundamental ingredient: meaningful data. The data everybody is using (and which I will be using in a minute) is riddled with measurement issues. Most important among them is the issue of testing: who gets tested, how fast, how many gest tested – all of that varies from country to country and across time within a given country. Not even the death statistics are reliable as we learned only this week when the UK drastically corrected their number upwards.

But I thought to myself, what the heck. If everyone’s doing it, I might be forgiven for having some fun as well. And so, in between grading final exams, I pulled together some country-level data and ran some regressions.

It goes without saying that this analysis has some, shall we say, shortcomings. All I’m doing is using regressions to describe some patterns in the data. Although I did have some mental model when deciding which variables to include in my regressions, they were of the sort “I imagine X could have effect on COVID deaths” rather than any deep causal understanding of how the epidemic works (but, frankly, does anyone have that?)

So without further ado, here’s what I did. I took the data from the European Center for Disease Prevention and Control (ECDC), giving me daily new cases and new deaths for each country reporting those things, which I summed up until April 30th to get the cumulative cases and deaths. I then divided by population to get cases and deaths per capita. These are my dependent variables.

For my regressors I went on a wild hunt on the World Bank and OECD databases and downloaded everything that I thought would be interesting to regress on COVID-19. After some fooling around, I settled on the following two models:

Model 1: cumulative COVID-19 cases per capita (in logs)

The first variable (lrgdp_pc) here is PPP-adjusted GDP per capita (in logs). This is the single most important variable in “explaining” the number of cases: richer countries have more official cases. The relationship is 1:1, i.e. one percent more income is associated with one percent more cases. It is almost useless to speculate about the “causal channels” for this effect. If I were to guess, I’d say that rich countries got the virus earlier and perform more tests per capita and therefore detect more cases.

The second variable (pop65) is the share of population above the age of 65. We know that seniors are more susceptible to this disease, so any sensible model must take the age structure into account. It’s reassuring that the coefficient is positive and significant. I take this as a sanity check for my model.

The next two variables is population density (pop_dens) and share of urban population (urban). My “theory” here is that denser, more urban countries provide a more fertile environment for the virus to spread. Somewhat disappointingly population density seems to have no effect and urbanization only has a small one (a 1 percentage point higher urban share gives you 1.4% more cases per capita). And no, density and urbanization are not highly correlated (corr=0.17), glad that you’ve asked.

Lastly, I wanted to check if more open countries are more exposed. I tried to capture that with the trade share (exports plus imports divided by GDP). The answer seems to be a clear no. Being more open to international trade is not associated with more infections. In an alternative specification I checked if imports from China had a positive effect and was disappointed.

I direct your attention to the fact that the R-squared of this regression is 68.5%. I have seen papers published in decent journals with much worse goodness of fit given the sample size and number of regressors. Just saying.

Model 2: cumulative COVID-19 deaths per capita

Turning to coronavirus deaths, the first important “explanatory” variable is the number of cases (lcases_pc). Again, this is nothing more than a sanity check.

I then add all the variables from the previous model to see if they have an effect on deaths over and above the effect they have through the number of cases. Unsurprisingly, an older population has the expected positive effect on deaths: raising the share of old people by 1 percentage point raises deaths per capita by 11% (in addition to the effect through cases).

More surprising are the effects of population density and urbanization. It looks like, after controlling for the number of cases, being a denser, more urban country reduces the number of deaths. I suppose this can make sense: given the number of infections, living closer together and in cities means living closer to hospitals, which might improve the chances of getting timely and effective treatment. But this is getting dangerously close to over-interpretation of weak effect estimates (small, barely significant coefficients).

The last variable is the number of hospital beds per 1000 people. The estimated coefficient suggests that each additional bed per 1000 inhabitants lowers the number of deaths by about 15%. Austria has 7.37 beds per 1000 people, the European average is 5. So bringing all the countries of Europe to the level of Austria would cut the death rate by about 36%. That’s a big effect.

I also toyed around with various measures of health care spending (per capita or as a share of GDP). In all the regressions I checked, health spending had a positive effect, which I couldn’t make sense of. My best guess is that, conditional on hospital beds per capita, spending more on health is a sign that your health system is too expensive and inefficient which is associated both with more cases and more deaths. But it’s still kind of a head scratcher.

Excess Cases and Deaths

OK. Having run these regressions and found some interesting patterns, what else can we learn from then?

One thing is that the regression model provides a benchmark to evaluate how individual countries are doing. Admittedly, this is risky business, given the poor data quality. But I’m putting it out there nevertheless.

Below, I’m plotting the excess cases and excess deaths per capita for a number of countries. Excess cases is the difference between the actual cases and the number of cases predicted by the model. Excess deaths are calculated analogously. (Attentive readers will realize that these are just the regression residuals.) The vertical axis shows cases and deaths per 100,000 people.

Three countries stand out in terms of excess cases: Italy, UK and US. Their case numbers are far higher than what one would expect on the basis of their country characteristics.

The “worst performers” among the selected countries in terms of excess deaths are France, Britain and Italy.

China and Korea have negative excess cases and no excess deaths. That is, these countries have fewer cases (and neither fewer nor more deaths) than the model predicts.

Notice that Sweden has similar excess cases as Germany and Austria, but far higher excess deaths. Make of that what you will.

(Data file and STATA code are available on request.)

The case for rational expectations in COVID-19 modeling

British biologist Carl Bergstrom recently gave an interview to the Guardian on the topic of “bullshit”. In it, the interviewer asked Bergstrom about shortcomings of existing epidemiological models as well as their use (and misuse) by political decision makers.

[Guardian] If you had the ability to arm every person with one tool – a statistical tool or scientific concept – to help them understand and contextualize scientific information as we look to the future of this pandemic, what would it be?

[Bergstrom] I would like people to understand that there are interactions between the models we make, the science we do and the way that we behave. The models that we make influence the decisions that we take individually and as a society, which then feed back into the models and the models often don’t treat that part explicitly. Once you put a model out there that then creates changes in behavior that pull you out of the domain that the model was trying to model in the first place. We have to be very attuned to that as we try to use the models for guiding policy.

In the context of the coronavirus, the problem was this: Early models such as the one by the Imperial College in London predicted between 1.1 and 2.2 millions of Americans could die from COVID-19, depending on the severity of mitigation efforts. This eye-popping number jolted the political decision makers (Trump, Congress, the Governors, etc.) into action, locking down schools and businesses and issuing stay-home orders. The media publicity around the study probably scared many people which made them take the social distancing measures much more seriously. All of this probably helped in slowing the spread of the disease such that the same researchers had to revise their predictions downward only weeks later.

That is, the publication of the initial predictions changed the behavior of people which rendered those predictions obsolete.

Bergstrom seems to say that the problem here is with the general public. They don’t understand that the models rely on behavioral assumptions which no longer hold once people learn about the models’ predictions and adjust their actions accordingly.

But, with apologies to Shakespeare: The fault, dear Bergstrom, is not in the general public, but in your models!

The problem with those epidemiological models (at least with the SIR-types of models) is that some of their key parameters (such as the reproduction rate R0, for instance) depend, in various ways, on people’s expectations about the future path of the disease. If you don’t take that into account, your predictions will be way off.

And way off they were! Here’s the summary of a statistical evaluation of a model similar to the one used in the Imperial study:

In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state (Figure 1)

The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)

You might say that prediction is not the point with those models. Maybe their only purpose is to produce scary headlines to make people listen to the experts. But that is a weird proposition. If experts want the general public to take them more seriously, making wildly erroneous predictions seems like a bad strategy.

So how are we going to take people’s expectations into account in epidemiological models? Let’s see.

March: Imperial predicts 2 million deaths. Government imposes lockdown. People are scared and stay at home.

April: Imperial revises his model, now predicts 50,000 deaths. Government partially re-opens the economy. People cautiously start going out again.

May: Imperial revises his model, now predicts 200,000 deaths. Government re-imposes some lockdown measures. People are scared again.

June: Imperial revises his model, now predicts 75,000 deaths. Government opens up again. People relax again.

And so on until we have converged to a situation in which the number of deaths Imperial predicts is consistent with the government’s (and the people’s) expectations and actions.

Such a situation is what economists call a rational expectations equilibrium. I think that trying to model people’s expectations in a consistent way would improve the usefulness of epidemiological models. This is, of course, a tall order. But perhaps if economists, statisticians, and epidemiologists would put their heads together, we could move in this direction.

Die ökonomischen Konsequenzen des Coronavirus – Soll die EU für die Kredite ihrer Mitgliedsstaaten bürgen?

Jetzt heißen sie nicht mehr Coronabonds, sondern Recoverybonds. Die Idee ist aber eigentlich die gleiche. Die EU nimmt Kredite auf, um jetzt den von der Krise stärker betroffenen EU-Ländern Hilfsgelder zukommen zu lassen. Diese Kredite muss die EU irgendwann zurückzahlen. Also entweder stockt die EU in Zukunft ihr Budget auf, indem die Mitgliedsländer alle einen größeren Teil ihrer Steuereinnahmen an die EU abgeben, oder es wird irgendein Schlüssel ausgemacht, wie und wer diese Kredite später zurückzahlen wird. Gedanken dazu habe ich in einem vorigen Blogeintrag angestellt. Eventuell kann die EU diese Hilfsmittel auch ohne Kredite finanzieren, indem die EU-Länder sofort Geld an die EU überweisen. Die einzelnen Länder müssten dazu dann wohl Kredite aufnehmen, indem sie eigene Staatsanleihen ausgeben.

Egal, wie das im Detail gemacht wird, sieht es nun immer mehr so aus, dass die EU vorhat, eigentlich Transferzahlungen innerhalb der EU zu vermitteln. Das würde bedeuten, dass also, zum Beispiel bei Krisen wie der jetzigen, Hilfsmittel von weniger betroffenen zu stärker betroffenen EU-Ländern fließen sollen. Die sogenannte „no bail-out“ clause, die genau diese Transferzahlungen eigentlich verhindern soll, scheint zu wackeln. Ich möchte hier (nach Gesprächen, die ich mit Jörn Kleinert geführt habe) der Frage nachgehen, was dabei Probleme machen könnte.

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Estimating the proportion of Corona cases with a random sample

This short note makes one simple point. If you are interested in estimating the proportion of Corona infected people in some country or region, there is a simple and better (more precise) estimate than the one you obtain by computing the sample proportion. You can also read this in German here (and here).

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Die ökonomischen Konsequenzen des Coronovirus – ein genauerer Schätzer der Dunkelziffer

Es geht hier nochmal um die Dunkelziffer der Coronaviruskranken in Österreich und darum, wie man diese aus der von SORA durchgeführten Stichprobe schätzen kann. Wie schon in meinem vorigen Blogeintrag zu dem Thema erkläre ich hier wieder, wie man die zusätzliche Information, die man über die Coronaerkrankten hat, zu einem genaueren Schätzer nutzen kann. Der Vorteil in diesem Blogeintrag ist der, dass ich genauere Schätzungen bekomme, ohne neue Daten zu erheben. Ich nutze hier etwas mehr von der Information, die SORA in ihrem Bericht geliefert haben.

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Die ökonomischen Konsequenzen des Coronavirus – Corona-Bonds

Viele EU Länder werden wohl aufgrund des Virus, des dadurch bedingten Rückgangs wirtschaftlicher Aktivität und die dadurch bedingten erhöhten Sozialversicherungsmaßnahmen neue Kredite aufnehmen müssen. Die Standardmethode wäre, das über Staatsanleihen zu machen. Es wird nun von EU-weiten sogenannten Corona-Bonds (also EU-Anleihen) gesprochen. Wie sollen die genau aussehen? Was bringen sie? Dieser Eintrag entspringt Gesprächen, die ich mit meinen Kollegen Jörn Kleinert und Richard Sturn hatte.

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6 1/2 Economic Principles for the Pandemic

The Coronavirus Pandemic has fundamentally changed our world. But it hasn’t changed the validity of fundamental economic principles.

I suggest six and a half economic principles which I think are important to bear in mind during these times. Most of them were touched on by Christoph Kuzmics in his excellent series of posts. But I thought it would be worthwhile to state them in a pointed, if slightly oversimplified, way:

  1. People still respond to incentives.

So, for instance, allowing small businesses to re-open earlier than large ones means there will be more small and fewer large businesses. Paying higher unemployment benefits means there will be more unemployed people. Requiring people to wear face masks when doing X, but not when doing Y, means people will do more X and less Y. 

2. World output still equals world income still equals world expenditure.

If you shut down X% of the world economy, the world will produce X% fewer goods and services, will have X% less income, and will spend X% less. The idea that we can somehow preserve everyone’s income and spending while shutting down the production of (most) goods runs into this basic adding-up constraint. The recession is the price we pay for the lockdown which at the moment is the only weapon we have to fight the pandemic (until we have a vaccine or medical treatments). Government transfers can change who gets to consume the goods, but they don’t change the amount of goods there are. (But also see principles 4 and 6 1/2!)

3. The price mechanism is still the best way of allocating scarce resources.

If the demand for toilet paper exceeds the supply at the current price, there are two options: either you let the price of toilet paper rise or you create a shortage. Allowing a higher price is by far the better option. A higher price gives producers of toilet paper an incentive to produce more of it and gives consumers an incentive to use it more carefully and economically. The same applies to face masks, ventilators, and yes, even to hospital beds.

4. Economic inequality is still best addressed by lump-sum transfers.

The pandemic will lead to more economic inequality, because the poor are hit much harder both by the disease itself (low income correlates with worse health conditions) and by the lockdown (most low-wage jobs can’t be done from home). The best way to address this is to give an unconditional transfer to all households (a.k.a. „basic income“) financed by a tax on something that is in fixed supply (at least in the short run): a once-off wealth tax for example. The second fundamental theorem of welfare economics still applies: we can achieve any desired allocation of scarce goods (including toilet paper, face masks and hospital beds) by lump-sum taxes and transfers while letting the market do its job.

5. The government budget constraint still exists.

Every euro the government spends needs to come from any of three sources: from taxes, from borrowing, or from printing money. But in the end, these are all just different forms of taxation. Government borrowing is delayed taxation: the government will need to pay back the debt with future taxes. Printing money is a tax on nominal wealth. 

6. Public goods problems still exist.

Enforcing the lockdown requires the threat (and sometimes use) of force. (That’s why it’s called enforcing). Staying at home is a prisoner-dilemma situation. If nobody is policing the lockdown, going out of the house is a dominant strategy (i.e. it is best irrespective of whether other people stay at home or go out). Social stereotyping of defectors (public shaming corona-party-goers, for instance) can go some way, but is also just another kind of force. Some civil liberties won’t be upheld during the lockdown.

6 1/2. Government spending still has a multiplier effect (but it is probably small).

If the government buys more goods, some otherwise unemployed workers will be employed making those goods. Those workers will themselves be able to buy more goods, creating further jobs for otherwise unemployed workers, and so on. However, the multiplier logic doesn’t work quite as well during the lockdown, because some workers simply cannot go to work. Government spending can help prop up demand in those sectors that aren’t shut down, but as long as many labor-intensive industries such as construction are closed, the multiplier will be only slightly higher than 1.