International politics in quotes (2020)

And now for something completely different …… According to the Cambridge Dictionary, wisdom is the ability to use one’s knowledge and experience to make good decisions and judgments. Wisdom is often expressed in the form of quotes. Most of the quotes below can be linked to key concepts in International Relations Theory. Can you guess which?

War, Peace & Empire

“It was the rise of Athens and the fear that this inspired in Sparta that made war inevitable” (Thucydides)

“War is the realm of chance, passion and reason” 

(Carl von Clausewitz, adapted from original text)

“Der Krieg ist also ein Akt der Gewalt, um den Gegner zur Erfüllung unseres Willens zu zwingen“

(Carl von Clausewitz)

“Der Krieg ist eine bloße Fortsetzung der Politik mit anderen Mitteln“ 

(Carl von Clausewitz)

“The purpose of war is to make better peace” 

(Basil Liddell-Hart)

“La guerre! C’est une chose trop grave pour la confier à des militaires”” 

(Georges Clemenceau)

“That may be so, but it is also irrelevant” 

(North Vietnamese Col. Tu, responding to Col. Harry G Summer’s observation that North Vietnam had never beaten the US on the battlefield)

“La France a perdu une bataille. Mais la France n’a pas perdue la guerre”

(Charles de Gaulle)

“Il est plus facile de faire la guerre que la paix ” 

(Georges Clemenceau)

“Kein Plan überlebt die erste Feindberührung ”

(Helmuth von Moltke)

“The first casualty when war comes is truth” 

(Hiram Johnson)

“Carthago delenda est” 

(Cato the Censor)

“Auferre, trucidare, rapere, falsis nominbus imperium; atque, ubi solitudinem faciunt, pacem appelant” 

(Tacitus, quoting Calgacus) 

“Dulce et decorum est pro patria mori” 

(Horace) 

“…/ My friend, you would not tell with such high zest/ To children ardent for some desperate glory/ The old Lie: dulce et decorum est pro patria mori” 

(Wilfred Owen)

“You fight for your country, but you die for your comrades” 

(Soldiers’ motto)

“That’s more than we know” 

(Williams, the night before the Battle of Agincourt, responding to the disguised Henry V’s remark that one ‘could not die anywhere so contented as in the king’s company, his cause being just and his quarrel honorable’, William Shakespeare)

“Fear, honour and interest” 

(Athenian envoys citing the motives that led Athens to acquire an empire, Thucydides)

“We seem, as it were, to have conquered and peopled half the world in a fit of absence of mind” 

(John Robert Seeley)

“Divide et impera” 

(Philip of Macedonia, much practiced by imperial powers, typically rendered in Latin)

“George, the British Empire at present covers a quarter of the globe, while the German Empire consists of a small sausage factory in Tanganyika. I hardly think we can be entirely absolved of blame on the imperialistic front” 

(Captain Edmund Blackadder, responding to George’s suggestion that WWI started because of ‘the vile Hun and his villainous empire-building’)

International Politics & Power

“Macht bedeutet jede Chance, innerhalb einer sozialen Beziehung den eigenen Willen auch gegen den Widerstand anderer durchzusetzen, gleichviel worauf diese Chance beruht“ 

(Max Weber)

“Politik würde für uns also heißen: Streben nach Machtanteil oder nach Beeinflussung der Machtverteilung, sei es zwischen Staaten, sei es innerhalb eines Staates zwischen den Menschengruppen, die er umschließt“ 

(Max Weber)

"Politics: who gets what, when, how?"
(Harold Lasswell)

“International politics, like all politics, is a struggle for power” 

(Henry Morgenthau)

“Order always requires a subtle balance of restraint, force and legitimacy” 

(Henry Kissinger)

“Si vis pacem, para bellum” 

(Publius Flavius, Vegetius Renatus, also attributed to George Washington, Plato, Shi Ji)

“The Pope? How many divisions has he got” 

(Joseph Stalin, after allegedly being told that the Pope opposed a Soviet decision)

“The strong do as they please, the weak suffer as they must” 

(Thucydides, Delian Dialogue)

“He may be a son of bitch. But he is our son of a bitch” 

(FDR allegedly referring to Nicaragua’s president)

“Nicht durch Reden oder Majoritätsbeschlüsse werden die großen Fragen der Zeit entschieden, sondern durch Blut und Eisen” 

(Otto von Bismarck)

Foreign Policy, Diplomacy & Alliances

“It is our policy to steer clear of permanent alliances with any proportion of the foreign world“

(George Washington’s warning about entangling alliances in his farewell address)

“We have no eternal allies and we have no perpetual enemies. Our interests are eternal and perpetual and those interests it is our duty to follow” 
(Lord Palmerston)

“Les traités, vous voyez, sont comme les jeunes filles et les roses: ça dure ce que ça dure!” (Charles de Gaulle)

“Keep the Russians out, the Germans down and the boys in” 

(Lord Ismay, NATO secretary general on the purpose of NATO)

“Der Mensch kann den Strom der Zeit nicht schaffen und nicht lenken, sondern nur auf ihm fahren und steuern, um mit mehr oder weniger Erfahrung und Geschick den Schiffbruch zu vermeiden“ 

(Otto von Bismarck)

“Speak softly and carry a big stick” 

(Teddy Roosevelt)

“Der Balkan ist mir nicht die Knochen eines einzigen pommerschen Grenadiers wert” 

(Otto von Bismarck)

“We have no dog in this fight” 

(Secretary of State James Baker, referring to the Balkans in 1994)

“Wenn man sagt, dass man einer Sache grundsätzlich zustimmt, bedeutet das, dass man nicht die geringste Absicht hat, sie in der Praxis durchzuführen“ 

(Otto von Bismarck)

“La parole a été donnée a l’homme pour dissimuler sa pensée” 

(Prince de Talleyrand)

“I wonder what he meant by that” 

(variably attributed to Klemens von Metternich and Talleyrand, upon hearing that the Turkish ambassador to the Congress of Vienna had died)

Foreign Policy & Cognition

“The Schleswig-Holstein question is so complicated, only three men in Europe have ever understood it. One was Prince Albert, who is dead. The second is a German professor, who has gone mad. I am the third and I have forgotten all about it” 
(Lord Palmerston)

“It is a riddle wrapped in a mystery inside an enigma” 

(Winston Churchill, referring to the USSR)

“A quarrel in a faraway country, between people of whom we know nothing” (Neville Chamberlain, during the Sudeten crisis)

“The whole problem with the world is that fools and fanatics are always so certain of themselves, but wiser people so full of doubts” 
(Bertrand Russell)

“Well, Lyndon, they may be every bit as intelligent as you say, but I’d feel a whole lot better if just one of them had run for sheriff once” 

(House Speaker Sam Rayburn after LBJ extolled the brilliance of the members of JFK’s cabinet)

“The Best and the Brightest” 

(Title of David Halberstam’s book about the group of apparently brilliant people and their decisions that led to the full-blown involvement of the US in Vietnam)

Biases & Bayes (2020)

Cognitive biases are systematic deviations from the standards of rational thinking and decision-making. Cognitive biases influence individual as well as collective decision-making (Sunstein & Thaler 2008, Kahneman 2012). Human cognition, perception and memory are adaptive and have been shaped by evolutionary pressures (Leakey 1991, Maslin & Lewis 2018). Biases (and heuristics) may not always lead to optimal decisions in terms of rationality. But, on average, they appear to translate into ‘good enough’ decisions and indeed are often more cost-efficient than more complex decision-making rules and procedures (Klein 1999). This is why intuition (biases, heuristics) sometimes fall short in important ways. Humans find it difficult to grasp the phenomenon of exponential growth and humans typically lend too much weight recent events and the near future than what a rational calculus would suggest. The existence of biases and heuristics simply confirms other research that demonstrated that human behaviour deviates from perfectly rational behaviour. Experiments have shown that individuals act less than rationally in a (game-theoretical) non-repeated dictator game. Completely self-interested behaviour is in fact the exception rather than the rule (Ariely 2009).

Cognitive biases and fallacies are ubiquitous. Biases often get divided into individual and social (and sometimes memory biases). Individual and social biases involve a flawed understanding of statistics and probability calculus, or their complete disregard. Cognitive biases lead people to hold mistaken (non-rational) beliefs and commit fallacies. Some fallacies can be squarely attributed to biases (e.g. base rate fallacy, conjunction fallacy), while other fallacies are perhaps simply due to a limited understanding of statistics and probability (e.g. birthday paradox, Simpson paradox, prosecutor’s and defence attorney’s fallacy). In all cases, human ‘intuition’ leads people to hold mistaken beliefs and/ or biased views and often leads them to make non-rational choices. For example, many people ignore the survivorship bias in data (e.g. Wald’s problem) or the they commit a conjunction fallacy (e.g. Linda problem). Training in probability and inferential statistics can help people correct for such biases (King, Keohane & Verba 1994). 

Biases often make it more difficult - and often impossible - to reach rational consensus. Finding consensus may be difficult due to different preferences, interests or values. Jonathan Haidt (2013) has shown that Republicans and Democrats tend to subscribe to different sets of values. The commitment to these values informs the intellectual arguments used in the defence of these values (not vice versa). But even if the defence rested solely on the intellectual merit of its arguments, different premises/ values would lead to different conclusions. In other words, different premises combined with deductive rules lead to different conclusions. (The conclusions are true, if the premises are true.) Values (like tastes) do therefore not lend themselves to rational debate. Or at least they do not lend themselves to rational consensus. De gustibus non disputandum. In practice, of course, disagreement about values and morality rarely involve formal deductive inference. Such discussions are more often about asserting one’s view and expressing affective opposition to the other person’s view.

In principle, it should be easier to find consensus with respect to empirical questions, in spite of cognitive biases. This is because in principle such questions concern the problem of how empirical evidence bears on the question (hypothesis) and should be more amenable to agreement in light of the evidence. Like in a deductive inferential situation, one may start with a different view (hypothesis). But as evidence accumulates, one is forced to update once initial belief. Naturally, empirical questions do need to overcome cognitive and affective biases. “It is difficult to get a man to understand something, when his salary depends on his not understanding it”, Upton Sinclair once remarked. This may explain why (most) investment bankers believe in low taxes and in trickle-down economics even though there is no empirical evidence to support the latter. Agreement on whether low taxes are more desirable than high taxes is a normative issue, of course. But it should be possible to get to a certain degree of agreement about what relevant effects lower or higher or taxes have. In principle, this question should be amenable to approximate agreement. Such questions can only be settled in a empirical-inductive manner, in light of evidence that bears on the hypothesis.

Extra-epistemic factors do influence how willingly individuals engage in a critical evaluation of hypotheses. For example, the rally-around-the-flag is very common during wars and national emergencies (Mueller 1970). When uncertainty is high, the need for leadership, authority and sense-making helps people deal with anxiety. Stability and reassurance then becomes more important than critical engagement with claims made by the authorities. (It can also make for strange proverbial (?) bedfellows.) Authority in general can limit people’s inclination and ability to assess views and claims critically. Often people are swayed by non-epistemic factors. Investment bankers dress well for a reason and US presidents and CEOs are disproportionately tall. Unless nice suits or tallness is correlated with greater intellect, people behave irrationally in the sense of letting themselves be influenced by non-epistemic factors (Economist, September 27, 2017). Research has shown that Democrats/ Republicans are more likely to support/ oppose the very same legislative proposal depending on whether it has been proposed by Democrats/ Republicans. Framing matters, too.

People tend to overestimate their intellectual grasp of issues and think themselves smarter than experts. This is related to the so-called knowledge illusion: people believe they understand things, but they don’t (Fehrenbach & Sloman 2017). Overestimating one’s own knowledge underestimating the knowledge of experts is also called the Dunning-Kruger effect (Dunning & Kruger 1999). Remember Michael Gove’s remark that “people have had enough of experts”. (Gove later clarified that he only referred to economists (BBC, February 27, 2017).) Interestingly the way to redress this is to ask people to explain how something works instead of why they support or oppose it (e.g. social security). This tends to nudge people towards a more reasonable view of their own abilities and makes them more willing to engage in fact-based debate that is more likely to lead a degree of agreement. If, on the other hand, one confronts people with a contrary view on a – actually or potentially – value-laden issue, people’s position tends to harden (Economist, December 8, 2018). This may be related to group biases. Either way, none of this makes it more likely that people will reach agreement.

Research has shown that experts are on average better than laymen in terms of making the correct predictions and good decisions (Klein 1999). However, not everybody who is called an expert is an expert. Philip Tetlock (2005) distinguishes between hedgehogs and foxes. Hedgehogs claim expertise but are often fairly ideological individuals with pre-set views and an unwillingness to learn from their mistakes (Tetlock 1998). (They make for good television in an age of polarisation.) Foxes, on the other hand, know many little things, are willing to learn from their mistakes, seek out diverse information and adjust their probabilistic forecasts in light of new information. Foxes do in fact outperform hedgehogs prediction-wise (Tetlock 2015). Polymath John Maynard Keynes would not have been surprised: “When the facts change, I change my mind. What do you do, sir?” 

Experts have a greater awareness of their cognitive biases, constraints and limitations. Good analysis requires an openness to new information as well as the continued questioning of assumptions, analytical frameworks and hypotheses as well as information. Good analysts are aware of cognitive, group and memory biases (CIA 1979). This awareness helps them avoid avoidable mistakes (King, Keohane & Verba 1994). Psychologically, this requires precisely what most people find uncomfortable: being comfortable dealing with cognitive incoherence and refusing cognitive closure. F. Scott Fitzgerald quipped that a great mind can hold two contradictory ideas in the mind at the same time. Good analysts do exactly that.

Scientists, whose training should attune them to the existence of biases, are also influenced by extra-epistemic factors in their evaluation of hypotheses and theories. In spite of their training in methodology and epistemology, scientists frequently remain wedded to so-called Kuhnian paradigms (or Lakatosian research programmes), theories and even individual hypotheses (Kuhn 1962, Lakatos 1978). Max Planck once quipped that science progressed one funeral at a time. This suggest that even scientists find it difficult to adjust their prior beliefs in light of new evidence. Extra-epistemic factors may be at work, including interests, biases, cognitive coherence. A brief look at Bayes’ theorem and holism suggests that this reluctant may not be indefensible, epistemologically speaking.

Could a Bayesian framework help foster a convergence of views and keep at bay biases? In a nutshell, Bayes theorem allows individuals to start out with different subjective beliefs (priors). The priors are linked to a conditional probability P (H/E), that is, the probability of the hypothesis given the evidence. To the extent that a piece of evidence raises the probability of the hypothesis, a rational agent will need to adjust P (H/E) upwards. If P (H/E) is not updated accordingly, an agent acts irrationally – and a diachronic Dutch book can be made against him. In principle, this allows two agents to start off with different views but end up with converging views as evidence accumulates.

There are some well-known problems with the subjective interpretation of Bayes' theorem. Convergence of beliefs is only possible if the priors are different from zero. This is not too much of a problem. Extremists and perhaps hedgehogs are likely to assign a zero probability to the priors. Most 'reasonable' people wouldn't. Priors only “wash out” in the long run. This is something that may keep philosophers up at night, but it is far from a decisive argument against Bayesian convergence. Even the likelihood of the evidence given the hypothesis P(E/H) that is used to help update P (H/E) is not too troublesome. Often the hypothesis entails the evidence. Surprisingly, the most problematic part of Bayes’ theorem is to do with the probability of the evidence P(E) = P (E/H) and P(E/-H), that is, the denominator. While P (E/H) is not too problematic, as just mentioned, the term P(E/-H) is a problem for -H is a catch-all hypothesis and may include any number of alternative hypothesis. This makes it difficult to assign probabilities to it. Then again, this is something philosophers (rightfully) quibble over. In practice, ‘reasonable’ people might be able to assign reasonably similar probabilities to P(E). Granted, unreasonable people won’t – and this is the point. At first sight, Bayes' theorem suggests a way to update one’s views in light of new evidence and a way for epistemic consensus to emerge among individuals in spite of very different priors. It turns out that this will only work if individuals are ‘reasonable’ to be begin with in the sense of not assigning off-the-charts probabilities to P(E) as well as not assigning zero probabilities to either of the priors. 

It is not clear that Bayes’ theorem can deliver convergence and consensus, even in principle. Scientists do not simply or quickly drop their hypothesis just because a piece of evidence bears negatively on it. (Popper’s fallibilism is too extreme.) Scientists often adjust other parts of their theory to account for such an ‘anomaly’. This is exactly what non-scientists do. They hold on to their prior beliefs (hypotheses) in spite of contrary evidence. They do so by changing the probabilities they assign to various parts of the theorem after the evidence is in. This would allow one to make a diachronic Dutch book and it does not feel legitimate to change posterior probability by changing P(E/H) or P(E). However, scientists (or philosophers of science) can invoke Quinean holism and the underdetermination of the theory by the evidence. Holism says that hypotheses can never be disconfirmed individually, but only as part of a wider theory or web of belief. Auxiliary hypotheses and background assumptions can always be adjusted to salvage the hypothesis. This also causes problems for Bayesian confirmation. To the extent that background assumptions are adjusted, the probabilities plugged into Bayes theorem may change. Again this looks illegitimate, hence the diachronic Dutch book argument. But this is often how salvaging one’s prior belief works, whether one is a scientist or a non-scientist. Aside from the problems related to Bayes’ theorem discussed above, this weakens the useful of Bayes' theorem as a tool to generate consensus. At the very least, it does so in practice. Subjective Bayesians naturally regard such a move as inadmissable. Holism weakens the usefulness of Bayes’ theorem by weakening the very constraints that - in the Bayesian view - were supposed to rein in biases and force a convergence of posterior beliefs in spite of potentially wildly different subjective prior beliefs.

The concomitant risks of Germany's exploding net IIP position (2020)

Germany has seen a massive increase of its net international assets due to large and persistent current account surpluses. Current account surpluses have consistently exceeded 4% of GDP since 2004 and have occasionally exceed a stunning 8% of GDP. As a share of GDP, Germany’s net foreign creditor position is equal to Japan’s and larger than China’s. Germany’s gross and net position may not be particular large compared to that of some small, open economies, especially those with large international banking sectors (Hong Kong, Singapore, Switzerland, Luxembourg). But among the G-20, Germany and Japan do stand out in terms of their net position. The sharp rise in surpluses began in the early 2000s, roughly coinciding with the introduction of the euro and domestic labour market reform. In spite of the uncertainty due to the current global health crisis, Germany will continue to run surpluses for the foreseeable future and add to its net external creditor position.

Source: IMF

Gross assets and gross liabilities have also risen. Gross external assets reached 280% of GDP in 2019, up from 145% in 2000. Gross external liabilities increased to 212% of GDP last year, up from just over 140% two decades ago. Being a large creditor is obviously less fraught with risk than being a debtor. Even though gross liabilities have surged, they are not a major source significant risk, either. With the exception of the general government, all sectors are net foreign creditors. If the balance sheet of the general government and the Bundesbank are consolidated, the net foreign debt position is only half as large. More importantly, the German government’s external debt is actually a sign of financial strength. Foreigners are keen to own German government. Extraordinarily low bond yields are partly a reflection of this. Moreover, sound macroeconomic management, a track record economic stability and (until recently) a strong commitment to a balanced budget translate into minimal risk. Last but not least, around 2/3 of total Germany liabilities consist of either equity-type instruments (FDI, portfolio equity) or long-term bonds. The liability side of the balance sheet is not a problem. As far as foreign assets are concerned, they are relatively well-diversified. Geographically, foreign asset holdings are concentrated in the euro area (50-55%) with the UK and the US making up another (combined) 20% or so of total holdings. In short, however one slices and dices it, the risk attaching to Germany’s foreign liabilities is extremely low and, structurally speaking, the risk attaching to its foreign assets appears manageable.

Source: Bundesbank

Germany’s external surpluses and foreign asset accumulation have been criticised on the grounds of generating only modest financial returns (Jaeger & Mayer 2012). (They have also been criticised as “beggar-thy-neighbour” policies, but this is another matter).  There may be something to it or there may not. A lot depends on how returns are measured, over what time period and what the returns are compared to. Furthermore, financial returns only measure the financial benefits attaching to foreign assets. Other more difficult-to-calculate benefits may attach to foreign assets such as supply chain diversification (in the case of FDI) or asset diversification (in the case of portfolio investment). Should German returns be compared to the returns earned by Japan (or China)? Should German returns be compared to comparable domestic investments? Should returns take into account (unrealized) valuation changes at the time they are measured? In a global environment characterised by extra-low interest rates, even originally modest financial returns are beginning to look attractive.

Nonetheless, it is worth asking whether the continued accumulation of net foreign assets is a sensible thing to do. Of course, one might always argue the external surpluses are simply the outcome of private market transactions. That’s largely true. But if economist learnt one thing in the past couple of decades, it is that market-driven outcomes can lead to major systemic risk and instability. German economists and government official usually point out that it is not the role of public policy to second-guess the desirability of large surpluses and increasing net assets. Undoubtedly, Germany’s external position can be explained by any combination of the following: (1) macroeconomic policy mix, (2) demographic changes, (3) ‘cultural’ propensity to save, (4) attractive investment opportunities abroad. While accumulating net foreign assets sounds like a good problem to have, the global financial crisis in 2008 also demonstrated that doing so is not without risk in spite asset and geographic diversification. German banks suffered considerable losses on their holdings of CDOs. Today Germany’s balance sheet would take a significant hit in case a major Target-2 debtor in euro system were to ditch the euro. 

Might it not make sense for the public sector to start dissaving in order to fund increased domestic investment and slowly reduce external surpluses? If one is of the view that the rapid accumulation of foreign assets is accompanied by increasing credit risk, then it might make sense to adjust macroeconomic policy accordingly. Again, Germany is very attached to ordo-liberalism and it has been generally been reluctant to manage its macroeconomy actively – for a range of ideological, intellectual, historical and institutional reasons. The Keynesian experiment did not last long. However, this has never meant that the German government is not prepared to take forceful action if the circumstances call for it (1990, 2008, 2020).

Prior to the economic shock due to the epidemic, a good case could be made for the German government to fund productivity-enhancing infrastructure investment. And  the case remains a good one. Like many other countries, Germany was experiencing low productivity growth, underinvestment and limited wage increases in the context of full employment. It was possible to argue, and many leading German economists did so, that the German economy was in macroeconomic equilibrium. An expansionary, investment-oriented fiscal policy would have led to overheating and reduced the fiscal space that was best to be preserved to confront the next economic downturn. However, if external surpluses remain large in the aftermath of the global health crisis and German foreign assets continue to increase, including in countries faced with significant post-crisis economic and financial challenges, then there continues to be a solid for a fiscal policy geared towards public-sector-supported/ -funded investment. Such a policy would be countercyclical and help deal with the recession and it would help reduce external surpluses and slow external asset accumulation. Such a policy would be affordable given Germany’s solid fiscal and government debt position. It would help limit an increase in risk attached to further increases in foreign assets. It would help unlock productivity growth domestically and generate important that would benefits Germany's major trading partners. It therefore comes down to whether or not the German government can devise a policy that is successful in identifying sufficiently profitable investment projects and in channeling excess domestic savings into profitable, productivity-enhancing investments in a timely manner? If it can, it will be able to kill three birds (economic downturn, increasing external assets and domestic productivity) with one stone (investment-focussed fiscal policy).

Data, statistics, epidemics & public policy (2020)

Mortality risk – or more precisely the infection-fatality rate – is often difficult to estimate, especially at the beginning of a disease outbreak. In the case of the current covid epidemic, countries (and the media) report so-called case-fatality ratios (CFR), that is, deaths per diagnosed cases. Reported case-fatality rates suffer from several shortcomings. For a start, testing typically suffers from selection bias. Given limited testing capacity, often only people with mild-to-severe symptoms are tested. Even where large-scale testing is available, this approach will miss infected people with mild or no symptoms. This will bias CFR upward relative to actual risk of death from an infection. Counting fatalities is similarly fraught with difficulties. Some deaths attributable to covid will not be attributed to it because the deceased were not tested. Other deaths will be counted as covid-related because the person had covid even though the actual death was due to complications unrelated to covid. On balance, the CFR published by countries (and US states) are likely to be higher than the actual infection-fatality rate (IRS) given that the undercounting of infections is more significant than the miscounting of deaths. This is what ‘natural experiments’ and the limited number of instances where large-scale covid testing was conducted suggest. 

Case-fatality rates exaggerate the actual risk of dying from a disease due to an undercounting of actual cases. Initial CFRs for corona were put at 3% based on Chinese data. Regardless of whether the Chinese data suffer from misreporting (as alleged by the CIA amongst others), the three-percent figure overstates actual mortality risk. Like virtually all other countries, China focused its testing efforts on people with flu-like symptoms, thus undercounting asymptomatic and mild cases. ‘Natural experiments’ also point towards this conclusion. The testing of all the passengers and crew on a cruise ship revealed that half of all cases were asymptomatic and that the actual IFR was around 1%.  And even this probably overestimates the IFR in the wider population given that an important high-risk group (people > 70 years of age) was overrepresented. Similarly, complete and repeated testing of population of the Italian town of Vo showed that about half of all cases were asymptomatic. This has led several academic studies to infer that that the IFR may be closer to 0.5%. IFR estimates that are consistent with the available data range from 0.05% to 1.0%. By comparison, the estimated IFR of seasonal influenza in the US is 0.1%. 

Absent reliable data, decision-makers were forced to make major policy decisions on the basis of estimates of IFRs that remain uncertain and vary by a factor of up to 20. Even if sufficient testing capacity existed, random sampling would present challenges given the uneven pace of growth in infections across the US (and the world). Different parts of the US (and the world) are presumably at different stages of the epidemic and the epidemic has likely taken different trajectories in different countries (and US states) due to varying local conditions (e.g. population density), policy measures (e.g. social distancing) and behaviorial differences among local populations (e.g. Texas vs Taiwan). Moreover, infections often grow exponentially, making random sampling at given point in time less reliable (and less useful). Sampling is also complicated by the fact that it will fail to count as infections people who have recovered at the time of sampling. (This is just one reason why antibody tests are so important.)

The longer the epidemic goes on, the more the uncertainty attaching to the date will diminish. The problem is of course that the longer one waits for the reliability of data to improve, the further one is likely to be “behind the curve” in terms of successful policy intervention – and this is especially problematic when dealing with a problem characterised by exponential growth. Policymakers (some more than others!) have been seeking to “flatten the curve” (that is, slowing the pace at which infections grow) in order not to overburden the healthcare system that can help save lives. Again, the problem is that at the very beginning of an outbreak, it is very difficult to estimate how many lives adequate healthcare can save. (At the beginning of an outbreak when uncertainty is high, policymakers may be well-advised to err on the side caution.)

Even if more reliable data were available, however, policymakers would need to rely on epidemiological models to assess possible outcomes. Epidemiological models generate scenarios based on assumptions. The better the empirical estimates of the model parameters and the better the model inputs, the better the projection. A significant degree of uncertainty about model parameters typically leads policymaker to rely on different models and/ or various model projections based on different assumptions. Important policy decisions need to be taken on the basis of assumptions that are based on estimates that are in turn based on not very reliable data. As more data becomes available, the estimates improve. In the face of an exponentially growing problem, waiting too long is however not necessarily an option. As an aside, if policy action is taken, it will be difficult to determine ex post whether the baseline scenario would have occurred, absent policy intervention. Counterfactuals are notoriously difficult to evaluate.)

Ex post, the most sensible way to measure risk and cost is to determine ‘excess mortality’. This is the difference between actual deaths during a disease outbreak compared to the expected number of deaths over a certain period. While this approach circumvents to some extent the issue of classifying the correct cause of death, it will need to make adjustment to account for increased or decreased numbers of death due policy intervention and possible second-round effects on mortality. A shelter-in-place policy is bound to reduce deaths due to traffic accidents or gun violence but may increase deaths due to domestic violence or suicide.

Regardless, the epidemic is set to kill significantly more people than seasonal influenza. The estimated IFR of seasonal influenza is about 1/1000 and infects up to 55 million people in the US in a given year, translating into 55,000 deaths, Given that covid is novel and population immunity non-existent, it is reasonable to expect that absent suppression/ mitigation measures, the total number of infections will reach the immunity threshold. The basic transmission rate (R0) absent policy measures is estimated at 2-3. A back-of-the-envelope calculation therefore suggests that 50-66% (herd immunity threshold) of the US population might get infected, translating into 320m x 0.66 x 0.5% > 1 million (US population x herd immunity threshold x IFR). This is at best a rough guess, but it illustrates how sensitive projections are to the empirically estimated parameters like the basic transmission and infection-fatality rates. Getting back to ‘excess mortality’, every year about 3 million Americans die. This means that if none of the 1 million projected corona-related deaths would occur absent corona, the US would suffer a 33% increase of deaths due to covid. Assuming that these projections are in the proverbial ballpark, the question from a public policy perspective is: how many lives can different policy measures save – and what cost in terms of public health and human lives?

The policy responses adopted to fight the epidemic have generally sought to “flatten the curve” by reducing the pace at which the number of infections grows. This is meant to (1) prevent overburdening the health care system, thus making it possible to save more lives, (2) limit the absolute number of infections, leading to fewer deaths, and, if (2) cannot be realised, (3) stretch out the infections over time in the hope of finding effective treatments or a vaccine before the herd immunity threshold is reached. Flattening the curve holds out the promise to prevent preventable deaths. How many deaths can be prevented depends again on estimates and likelihoods attaching to (1)-(3). Moreover, the expected number of lives saved needs be set against the expected lives lost due to policy intervention. The social and economic disruption caused by mitigation and especially suppression policies may lead to increased deaths. Different policies will tend to lead to different trade-offs (e.g. herd immunity approach, risk stratification, mitigation, suppression). It is obvious how much uncertainty attaches the costs and benefits of the various policy options. And this is before the longer-term consequences of the various policies are taken into account.

Ironically, countries that do a good job at suppressing a large-scale outbreak will be at greater risk of a second and even a third wave of infections, unless effective treatments or a vaccine is discovered. While the authorities and the public will be more vigilant and the healthcare system better prepared, countries that fared well initially will be at greater risk later on, compared to countries where infections have reached the herd immunity threshold, whether through a conscious herd immunity policy or mitigation. This may translate longer-term costs, including negative effects on psychological health due to continued uncertainty and related costs in terms of economic well-being due to restrictions on trade, travel etc.

Source: The Lancet

Policy choices hinge on estimates of fatality and mortality risk and the degree to which policy intervention can be expected to lower it. Mortality risk is strongly determined by the number of infections and the infection-fatality rate. These two variables can be influenced by public policies. But policymakers are forced to take impactful decisions on the basis of not very reliable estimates of key parametres. This may lead policymakers to opt for economically and socially and ultimately public health wise costly measures. If fatality is significantly lower than present estimates suggest (possible if not necessarily likely), then the mitigation measures would be more difficult to justify once the public health costs of the second-round effects of these decisions are factored into the equation. And maybe it will turn out that based on the actual value of key variables, a risk stratification could have reduced the number of deaths to levels comparable to a lockdown policy, but at a lower (second-round) costs. While policy decision should be scrutinized carefully once all the facts are in, it would be unfair to forget that crucial policy decisions have to be taken under conditions of heightened uncertainty and immense time pressure in the case of pandemics. Beware of armchair epidemiologists and policymakers who benefit from hindsight!