Modi-Covid19 Bio-Economic and -Politics Simulation - Institut für Customer Insight

Modi-Covid19 Bio-Economic and -Politics Simulation

 National Resource Protectionism, Lockdown, Citizen Loyalty, Workplace
 Reorganization and Health-cum-Economic Policy Stabilization on the Road to
 Pandemics Defense Planning

 Ernst Mohr*
 Institute of Customer Insight (ICI-HSG)
 University of St. Gallen
 Switzerland

 March 30, 2020
 Abstract:
Modi-Covid19 is hypothetical! It differs by assumption from Covid19 in one respect: It
poses the same health risk to all cohorts of the population. Workforce health is
therefore more at stake than under the actual Covid19 pandemic. For this contingency
a qualitative simulation is made of the health-cum-economic policy and politics
challenges posed by a Modi-Covid19 epidemic. The simulation suggests that they
would be markedly different from the Covid19 one.
 Why is this of interest today? Modi-simulations are useful because governments will
take their lessons from the current pandemic to better prepare for more pandemics to
come. But viruses are born out of chance. Pandemics defense planning should
therefore be based on mutation contingency simulation if different contingencies pose
markedly different policy challenges. The Modi-Covid19 simulation suggests that
pandemic defense planning should not solely be based on the actual lessons learned
from the Covid19 event only, but also on a set of modi-virus simulations, of which the
Modi-Covid19 simulation is an example of.
 The starting point of the simulation are economies which have fallen immediately
upon the arrival of the virus into a Nash-type protectionism equilibrium such as that of
mid-March 2020. In a static model in which the disease is under control of lockdown
policies, economic output depends on workforce health and politics optimizes the
lockdown-intensity by heeding the political tradeoff between individual freedom and
economic success. In this supply-side bio-macroeconomic framework of Modi-
Covid19, quite different policy challenges arise compared to the Covid19 event, that
can feed into an emerging contingency pandemics defense planning.

*I thank Simon Evenett for very valuable comments on an earlier version of the paper. Needless to say,
I am solely responsible for all remaining errors, misconceptions and misinterpretations that may still
haunt the ideas expressed in the paper.

1. Introduction
Within just three months after the arrival of the virus, as of the middle of March 2020,
the fight against the Covid19 pandemic has left the international political and economic
cooperation in a wreckage. Even Europe’s densely knit supranational institutions have
been paralyzed by the tsunami of national go-it-alone approaches. Yet there are two
commonalities across almost all of Europe: A lockdown on citizens’ freedom and open
protectionism at the national borders.
 Protectionism shows everywhere much the same pattern: the workforce, goods and
services from abroad are allowed in (commuters, imports, experts); outgoing business
is curtailed wherever it strains the national resources deemed important for handling
the crisis (protection masks, drugs, medical equipment); other cross-border human
traffic is curtailed such that non-residents are kept from straining the domestic
infrastructure, although from an epidemiological stance crossing the border between
similarly affected regions is no more conductive to the pandemic than movement
within. With European institutional cooperation torn to pieces, leaving the EU-
Commission with the role of ex post endorsing faits accompli, governments have within
weeks found their Covid19 protectionism Nash equilibrium: Each government opts for
protectionism, because all other governments do. All are then worse off compared to
open European borders (given the virus has spread all across Europe by now): Supply
chains for sanitary and medical equipment and the production of drugs are hampered
everywhere; drugs and equipment are kept from moving to where they are needed
most; built-up medical capacities are kept idle on guard, out of reach for non-residents;
on a larger scale, supply chains in industry and international services are hampered or
have been broken down. Within weeks Europe has moved back to a set of non-
cooperative nation states, each alone at war with the virus.
 This is the assumed starting point for a qualitative simulation of the challenges
posed by a hypothetical mutant of Covid19, which in the following is called Modi-
Covid19. It differs from Covid19 in one respect only: It poses the same health risk to
all cohorts of the population, whereas Covid19 affects much more the elder
generations and the already sick. Workforce health is therefore more at stake than
under the actual Covid19 pandemic.
 Why is this of interest today? Modi-simulations are useful because governments will
take their lessons from the current pandemic to better prepare for more pandemics to
come. But viruses are born out of chance. Pandemic defense planning should
therefore be based on mutation contingency simulations if different contingencies pose
markedly different policy challenges. The Modi-Covid19 simulation suggests that
pandemic defense planning should not solely be based on the actual lessons learned

 2

from the Covid19 case only, but also on a set of modi-virus simulations of which the
Modi-Covid19 simulation is an example of.

 What in the simulated Modi-Covid19 economy is different from the pre 2020
economy is the lockdown on the freedom of citizens. They are also economic agents.
They staff both the supply side and the demand side of the economy. The simulation,
though, addresses mostly the supply side of the economy. This is motivated by the
belief that an overall analysis must be based on the particularities of each market side,
which therefore warrant separate consideration at first.1
 The lockdown has two economic supply-side effects. First, its scale and length affect
the speed of dissemination of the disease. The more and the longer citizens are locked
down the slower its dissemination and the smaller the sick-leave and under-quarantine
numbers of the workforce. Consequently, the better off is the supply side of the
economy. The greater the left-over freedom of flocking together for social purposes the
faster the disease spreads and the worse off the supply-side of the economy is. This
first supply-side effect is the bio-economic effect of curbing individual freedom. It
establishes a tradeoff between individual freedom and aggregate economic supply.
 It should perhaps be made explicit, that, in the simulation the virus has been tamed
so much that the number of the sick is endogenous to policymaking (lockdown policy).
The perspective on this is, of course, a minefield for politics, with politicians caught in
between citizen rights and economic needs.
 The second economic effect of the lockdown is organizational: The physical
distance of the workforce in workflows will be systematically enlarged (workforce
distancing). For example, whoever can be sent, will be sent home for home (office)
work. Workforce distancing has two economic supply-sides to it. First, the size and
quality of the IT-infrastructure, such as for home (office) work, affects the capacity for
workforce distancing. Second, it is unknown yet whether workforce distancing is
motivating or demotivating. We don’t know yet, whether the new home (office) work
makes us work harder for the organization that sent us home or lets us chicken out.
This risk-laden side of work organization affects the productivity of the IT-infrastructure.
The combined capacity-cum-productivity effect is the techno-human effect of the
lockdown.
 Because of the bio-economic effect, governments lean towards locking down
individual freedom (when they are concerned with the supply side of the economy).

1
 As a side note on the demand side, the lockdown is a ban on economic transactions in whole industries
 of the economy. Together with the supply-side effects to be dealt with subsequently, the health-first
 motivated actual Covid19 lockdown is a (global) emergency policy experiment of stressing the world
 economy with a combined supply-side and demand-side shock. Eichenbaum, Rebelo and Trabant
 (2020), for example, model the transaction ban as a consumption tax.
 3

But because of the techno-human effect they may disagree as to the preferred scale
and length of the lockdown. Modi-Covid19 economies differ in their capacity for
workforce distancing and governments may differ in their judgements of the
motivational effect of workforce distancing. In this simulation, the techno-human effect
is therefore a source of obstructing the return to international cooperation.
 Covid19 is highly infectious, but the risk group is mostly outside the ranks of the
workforce (cohorts older than 65 and those under severe pre-existing health
conditions). Modi-Covid19 is as infectious as Covid19 but the sickness and death toll
payed by the workforce is higher. This is the epidemiological difference in the
simulation compared to the actual situation the world is in. The policy regime
(lockdown) and the state of international economic disintegration (protectionism Nash
equilibrium) are the same in the simulation as we actually observe.

 2. The Pandemic Background of the Modi-Covid19 Simulation
Suppose hypothetical Modi-Covid19 has the same pandemic properties as the
hypothetical Modi-SARS virus on which the Robert Koch-Institute (RKI) based its 2013
scenario of a pandemic hitting Germany (Modi-SARS Report).2 In the RKI-scenario,
except for two properties, hypothetical Modi-SARS is identical to the actual SARS-
CoV, which hit China in 2003 but which was successfully contained in Asia. One
difference is that the hypothetical virus was assumed to be more infectious than its
actual ‘role model’, spreading also from persons who do not yet feel sick. The second
difference between the Modi-SARS simulation and SARS-CoV are assumptions about
the vulnerability of the population. Whereas SARS-CoV affected the cohorts of the 65-
year-old-and-over more than the younger, the Modi-SARS Report assumed for its
simulation that the entire population is equally likely to fall ill or die. Hence the RKI-
simulation assumed a virus which in these two respects is more dangerous than the
SARS-CoV event of 2003. Dissipation of Modi-SARS was assumed to be driven by
population density, the more densely a region is populated the more quickly Modi-
SARS spreads. Vaccination was assumed to be deliverable to the population not
before three years of the outbreak of the disease. The Modi-SARS Report classified
the likelihood of the event as one in 100 - 1000 years.
 The actual Covid19 has almost all of the properties of the 2003 hypothetical Modi-
SARS. In particular it shows much the same high infectiousness as was modeled in
the Modi-SARS Report. However, it is in one respect tamer than Modi-SARS: Like
SARS-CoV, Covid19 affects primarily the health of older generations and those already

2
 Bundesregierung 2013.
 4

sick, whereas the symptoms the younger generations and hence the workforce shows
are weaker. The Modi-Covid19, assumed in the present simulation, has the more
severe properties of Modi-SARS: It is assumed to affects all parts of the population in
much the same way, and hence also the workforce. The epidemic potential of these
four viruses can therefore be brought in the following epidemiological escalation order:

 SARS-Covid (real) < Covid19 (real) < Modi-SARS = Modi-Covid19

 The 2013 Modi-SARS Report predicts the pandemic to hit Germany in three waves,
driven by mutations and reinfections over three years. Anti-epidemic action is taken as
from the time of ten deaths. They last for 408 days, as thereafter further anti-epidemic
measures are not considered effective anymore. At the peak of the first wave, around
month ten, six million show symptoms of Modi-SARS, of which four million should be
hospitalized, of which one million need intensive care. Over the duration of the first
wave a total of 29 million fall sick. A lockdown on individual freedom is part of the
portfolio of actions taken by national authorities. The Modi-SARS Report mentions the
possibility that 10 per cent of the workforce could die. On top of which come, of course,
all those who fall ill for some time of the pandemic or are set under quarantine, all of
which strains the labor supply to the economy. However, the Report also predicts the
save functioning of all essential infra-structure, such as water and electricity, except
the health system, and of the public sector. The Modi-SARS Report can be
summarized as follows: Apart from the health infrastructure, the State keeps going
whereas the health of the population and the economy are severely ailing for three
years to come. All cohorts of the population are affected alike.
 This is the pandemic background of the Modi-Covid19 simulation. Today, demand-
side macroeconomic considerations are in the foreground, of when and how to ease
the transaction-ban on entire industries brought about by the lockdown.3 In the Modi-
Covid19 simulation, health-cum-economic policymaking will necessarily be more
supply-side driven, paying more attention to the health of the workforce.

 3. The Bio-Economic Tradeoff
For pre-Covid19 economies, economists by and large have assumed a positive
relation between individual freedom and overall economic success. This is depicted in

3
 Dorn, Fuest, Göttert, Kronlage, Lautenbacher, Link, Peichl, Sauer, Stöckli, Wohlrabe and
 Wollmershäuser 2020. On the supply-side today’s focus is on (international) supply chains, but not
 on the supply of labor directly.
 5

Diagram 1 by a movement from E to G, where Y is aggregate output and F is freedom
left under the national lockdown regime.

 The supply side of Modi-Covid19 economies is different. The bio-economic effect of
a lockdown introduces a novel socio-economic transformation curve, depicted in
Diagram 1 by the declining function of the form = − • (tradeoff-line). The
smaller the individual freedom left under the lockdown, F, the less of the workforce is
sickened or under unproductive home quarantine and the larger is aggregate output.4
 The intersection of the tradeoff-line with the horizontal axis marks the fixed ex ante
state of individual freedom, where, by assumption, the virus sickens the entire
workforce.5 The techno-human effect of a lockdown affects the intersection with the
vertical axis, and hence the maximum aggregate supply possible.
 Whereas the intersection of the tradeoff-line with the horizontal axis is (by and large)
the same for all Modi-Covid19 economies, they differ in their maximum aggregate
supply, r. Given maximum positive motivation of the distanced workforce everywhere
(the productivity effect of the joint techno-human effect), r still differs between countries
for a number of reasons. One is the capacity effect of workforce distancing, i.e. the
state of the national IT-infrastructure at the outbreak of the crisis.
 Slope b is, however, also affected by the productivity of the national IT-
infrastructure, which in turn is also driven by the motivation of the newly distanced
workforce. In contrast to the capacity effect of distancing the workforce, the
motivational effect is not a bequest from the past but is forged in the evolving history
of the Modi-Covid19 society, including governments’ lockdown policies.6
 That is, slope b can change in the course of affairs, i.e. the tradeoff-line can rotate
around in the course of health-cum-economic policy making and countries will differ in
the trade-off lines they face. These differences between countries alone suffice that
governments pursue different Modi-Covid19 policies, everything else the same.
 Policy differences are also driven, of course, by other inheritances from the pre-virus
economies. One is the domestic resource base on which a country can rely at times of
Covid19-type protectionism. Another is the former integration in the world economy,
which affects the vulnerability of the supply side of the economy to interruptions of

4
 From a demand-side perspective the tradeoff-issue is different: As the lockdown strangles entire
 industries (transaction ban) easing the lockdown will push up aggregate demand. The bio-
 economic tradeoff returns, however, if consumers become too sick for spending.
5
 The Modi-SARS Report predicts that 19 million will be sickened at the peak of the first wave if anti-
 epidemic measures are not taken.
6
 Of course, the (young) history of (zigzagging) politics (such as in the US and the UK in March 2020) is
 also a driver of citizens’ motivation to cooperate in lockdowns. But this is not the topic in the
 simulation.
 6

international supply chains. These inheritances also affect the propensity to return to
international cooperation.

 A paper from March 2020 takes a different stance on the tradeoff between
aggregate supply and health.7 Voluntary distancing slows down the dissipation of the
virus, as is assumed in the present simulation for imposed lockdowns. But they also
drive down output by driving up voluntary unemployment, which is a self-protection
measure of private households (radical workplace distancing). The basic supply-side
difference between their bio-economic approach and the one presented here appears
to be that in their approach effective labor supply (those willing to work) is negatively
related to anti-epidemic measures (quitting the job in order to reduce the risk of
infection), whereas in the present simulation effective labor supply (those healthy at
work) is positively related to anti-epidemic measures. The two approaches highlight
the importance of novel workflow organization that keeps the infection risk of the
distanced workforce low. The Modi-Covid19 simulation assumes that this
reorganization is quick and successful enough that the workforce need not quit the job
for self-protection.

 4. The Bio-Political Tradeoff
According to the bio-economic tradeoff-line, motivated in the previous section,
individual freedom is inversely related to economic success due to the effect of Modi-
Covid19 on workforce health and the effect of lockdowns on the dissemination of the
virus in the population. The less individual freedom there is the better off is the supply
side of the Modi-Covid19 economy.
 Suppose citizens’ preferences for individual freedom and aggregate output are as
depicted by the system of indifference curves I1-I5 in Diagram 1. By and large, citizens
accept limitations on their individual freedom if they are compensated by aggregate
supply and vice versa. The sustained physical availability of goods and services is one
driver of accepting interferences in individual freedom. Another is the larger job security
due to a healthier workforce that can sustain production chains. Vice versa, citizens
accept concessions in terms of economic supply-side stability only when compensated
in terms of individual freedom.8
 The simulation assumes that the government seeks to maximize utility by choosing
an internal optimum between aggregate output and individual freedom such as point

7
 Eichenbaum, Rebelo and Trabant 2020.
8
 Here also, the demand-side perspective is different. Pushing up demand by lifting lockdowns has a
 bearing on the wellbeing for all those employed in industries which suffer from the transaction ban
 (tourism-, entertainment-, lifestyle industries). Hence, for those citizens, happiness faces no
 tradeoff between individual freedom and the economy.
 7

D with utility I5. D represents an optimum, given the tradeoff between economic output
and individual freedom (bio-economic tradeoff) and a larger resource base, e.g. under
conditions of a world economy which is again more integrated than under Covid19
protectionism (dashed line). Relative to that hypothetical optimum D, all economies
have been contracted into a happiness range below I5.
 The following considerations address health-cum-economic policy scenarios in
which the indifference curves of the population do not behave as is generally assumed,
but feature, instead, ‘pathological’ regions such as the concave areas of I2 – I4. This
‘pathology’ (by orthodox standards) is motivated by two simple considerations. First, in
exceptional situations people behave exceptionally, not conforming to the ex-ante
courant normal. In other words, there is no more justification for sticking with the
traditional model component than for abandoning it. By abandoning it a Modi-Covid19
world can be simulated in which not only the grand societal relation between freedom
and economic success has changed but also some of the mental dispositions of the
population to judge them. For scenario building this approach may be a valuable com-
plement to more orthodox approaches.
 Second, the concrete shape of the preference ‘pathology’, the concave area
depicted, can be motivated as follows: in the concave area, e.g. around B of
indifference curve I2, the government needs to compensate the population for a further
decline of individual freedom only with a smaller and decreasing increase in aggregate
output, compared to non-‘pathological’ preferences, in order to keep the level of
happiness constant. This captures in a simple way a sense of urgency and
collaboration in the population. Conversely, the government needs to compensate the
population for a further decline in economic output only with a smaller and decreasing
increase in individual freedom. The convex form of indifference curves for very low
levels of either output, Y, or freedom, F, or both represents a radicalization potential in
the population at extreme Y-F combinations where the population loses all sense of
cooperation with and loyalty to the government. That sense of loyalty also fades out as
happiness reaches again those levels which existed before the outbreak. Hence
indifference curves like I5 are shaped as usual.
 In the ‘pathological’ area of preferences, mid-range between extreme health-cum-
economic policy making and mid-range between very low and accustomed to high
levels of happiness, governments enjoy a discount region for policy making where
policy changes are cheap to obtain from the population in terms of compensation given
for sacrifices requested. This is the Modi-Covid19 society, special both in terms of the
bio-economic tradeoff-line and in terms of the population’s loyalty to its government
(policy discount region). With these assumptions on government behavior (local
maxima orientation) and the political tradeoffs it must thereby heed (the preferences
 8

for economic and biological wellbeing of the electorate), the simulation is also a bio-
political one. In the combined bio-economic and bio-political simulation health-cum-
economic policy scenarios will now be derived.

 5. Workforce Motivation and Local Policy Optima
In the policy discount region multiple local maxima can exist. For example, in Diagram
1 there are the local maxima at A and at C (B is a minimum). Type A maxima feature
a high level of individual freedom, an associated high level of sick-leaves and a
corresponding low level of output. Call a policy/politics corresponding to maxima of
type A freedom policy/politics. Type C maxima feature high output due to a relatively
healthy workforce because of low individual freedom. Call a policy/politics corres-
ponding to maxima of type C workforce health policy/politics.

 Multiple local maxima are another source of national differences in policies/politics.
If two countries face the same tradeoff-line one may be in A in Diagram 1 and the other
in C. Although A is only a local maximum whereas C is the global maximum, countries
may still differ in their policies if governments favor gradual policy changes over radical
shifts. Two otherwise identical countries may stick with prolonged policy and political
differences and conflicts. A first hypothesis derived from the simulation therefore is:

 H1: Otherwise identical Modi-Covid19 economies may persistently differ in their
 health-cum-economic policies/politics. While some pursue freedom policies/
 politics, others pursue workforce health policies/politics.

 Now consider distanced workforce motivation. Suppose the slope of the tradeoff-
line, b, in Diagram 1 is the result of a medium motivation, i.e. of a medium productivity
of the national IT-infrastructure. As, for example, homework motivation is not inherited
from the past but develops in the Modi-Covid19 economy, it may change over time.
However, if the tradeoff-line rotates up or down around the intersection of the tradeoff-
line with the F-axis, then local maxima may all of a sudden disappear and governments
are forced to radical policy/politics switches.
 For example, if the economy is at maximum C, with low individual freedom and high
output, and distanced workforce productivity declines, the tradeoff-line may rotate
down so much that type C maxima will vanish and only type A maxima are left. Despite
its distaste of radical policy changes the government will all of a sudden switch from
workforce health-type policies/politics to freedom-type policies/politics: Distanced

 9

workforce productivity has become so low that workforce protection is no viable politics
anymore.

 If the economy is at maximum A, with a high sick rate of the workforce but large
individual freedom, and if workforce productivity increases enough (rotating the
tradeoff-line upwards enough), then type A maxima vanish and only type C maxima
are left. The government cannot adapt its policy anymore gradually. It will therefore all
of a sudden switch from freedom-type policies/politics to workforce health-type
policies/politics: The loss of aggregate economic output by high sick leaves has
become so high that pursuing the freedom policy is no viable politics anymore. This
establishes

 H2: Health-cum-economic policies/politics will be unstable if distanced workforce
 productivity remains unstable or is unpredictable. Politics then will lack the
 reliability conductive for both the social and the economic.

 Note that the contingency spelled out in H2 is by itself an obstacle for countries to
give up their Covid19-type unilateralism. Because governments will be reluctant giving
up their room for maneuvering, required in binding international agreements, if their
domestic policy environment is still that unstable. If H2 is a correct prediction, this will
also complicate business planning and asset pricing, slowing down investment and
weakening recovery or worsening recession. Politics, exploiting citizen loyalty when
domestic productivities are still unstable, will be the ‘sickening virus’ of the economy
and not Modi-Covid19 per se.
 Also note that this policy and perhaps associated political instability is not due to a
virus still haunting society unpredictably or as predicted by epidemiology (Modi-SARS
Report). The Modi-Covid19 is, simply by model assumption, totally under control of
health-cum-economic policy making. And yet politics remains inherently unstable. A
lesson learned from this combined virus-cum-economic supply-side simulation is that
erratic policy shifts are immanent to politics and not systematically due to the natural
dynamics of the virus. The downside of the analysis given here is that erratic virus-
politics will not be contained to the early phase of a pandemic but will stick in the longer-
run.
 A corollary of H2 is

 H3: Modi-Covid19 politics will feature a novel political dichotomy in addition to
 the traditional battle lines of left vs. right, economy vs. ecology, state vs. citizen,
 10

which is: workforce health vs. individual freedom. Industry and commerce-
 friendly parties by tradition, e.g. economic liberalism, will be trapped in between
 the old harmony between freedom and economic success (from E to G in
 Diagram 1) and the novel disharmony dictated by the bio-economic tradeoff.

 6. The European Crises Policy Rollercoaster
Here are some challenges for the reanimation of European (not necessarily EU-)
cooperation out of Covid19-type protectionism, which is also assumed to reign the
simulated Modi-Covid19 world: First, formerly similar countries, even used to
(sporadic) allying, will pursue inconsistent if not conflicting policies (H1). Second, the
national policies stay inherently unstable if the distanced workforce productivities
remain unstable (H2). Third, European politics must come to grips with a new conflict
between liberalism and economic success, which will now also depend on the
sustainability of workforce health (H3). The third challenge will, of course, also include
the issue of global economic integration and its effect on domestic workforce health.
 The third issue also requires an analytical departure from the orthodox thought-of
independence of nature from society.9 Nature cannot be thought of as yet another
shock falling onto society. It must be thought of as systematically interacting with the
social, of which the economy is a part. From the epidemiological stance, the inroad
into the management of the virus is towards the human: Manipulate human distancing
and you can contain, although not control the virus!
 From the other, economics perspective this inroad has two sidewalks, though: You
can be on the pandemic side of management or on the endemic one. Pandemic-side
management is opportunistic: It seeks making the best of a virus out of control.10
Endemic-side management is strategic: It seeks to manage the policy alternatives left
once the virus is sufficiently under control. This side of bio-management is the topic of
the current simulation. It is bio-macroeconomic in spirit, as is the opportunistic one. But
it differs from it by addressing the society-inherent, endogenous sources of the

9
 The economic scenario in Dorn, Fuest, Göttert, Kronlage, Lautenbacher, Link, Peichl, Sauer, Stöckli,
 Wohlrabe and Wollmershäuser 2020, for example, implicitly assumes an independence of
 economic policy from virus dynamics: The virus does what it does to workforce health, independent
 from what governments decide.
10
 For example, Eichenbaum, Rebelo and Trabant 2020) say lockdown policy should react to heating-
 ups and calming-downs of the epidemic. Their logic is one of internalizing the external effects of
 citizens’ self-protection: People optimize their physical distance to the social whole for self-
 protection reasons but not for the reason of protecting others. This externality should be internalized
 by the lockdown policy which corrects for this externality by overshooting the household optimum
 of social distancing. This depresses the economy, in their model, but saves lives.
 11

unpredictability and unreliability of virus policies and politics, not the natural ones, left
over from a recalcitrant virus.

 A pandemic will thus pave the way for endemic policy crises11 (plural!). Diagram 2
serves for illustration. It is a projection of Diagram 1 into a three-dimensional space,
relating equilibrium values of F (local happiness maxima) to parameter values of b and
r (and thereby, implicitly, to the corresponding equilibrium aggregate output Y). The
graph in Diagram 2, akin to a snapshot of a breaking wave, is the three-dimensional
surface open to policy makers for gradual policy adaptations of the chosen policy type
(freedom policy or workforce health policy) and, if needed, radical switches of policies
(switches from freedom policy to workforce health policy or vice versa).12
 Suppose two Modi-Covid19 economies are identical, i.e. ride the same (frozen)
wave, but differ in their chosen policy type. Suppose one economy is in a freedom
policy maximum such as H’ in Diagram 2, and the other is in a workforce health
maximum such as K’. In the policy discount region of Diagram 1 there are two local
maxima. In Diagram 2 the policy discount region is the projection of the frozen wave
into the two-dimensional (b,r)-plane, which is the shaded area with the corners Q’’, S’’
and R’’ (bifurcation set). A parameter combination such as T’’ induces two local
maxima, the freedom type policy A’ and the workforce health type C’. (The shaded
area of the frozen wave represents minima such as B’ which corresponds to B in
Diagram 1.)
 Suppose in the country which is in the workforce health equilibrium K’, workforce
productivity improves (which corresponds to an upward rotation of the tradeoff-line in
Diagram 1). The corresponding gradual adaptation of the government policy is
represented in Diagram 2 by a move from K’ via P’ to L’. That is, productivity
improvements are gradually given back to the population also in terms of gradual
improvements in individual freedom. At L’, a further improvement of productivity cannot
be answered by the government by further gradual adaptations of the workforce health
policy, as this type of maximum vanishes. At L’, government switches to M’ of the
freedom policy type. Further improvements of productivity lead the country into
maximum H’.
 Suppose the other country, being in freedom-type maximum at H’, is facing a
gradual deterioration of its workforce productivity, corresponding to a downward
rotation of the tradeoff-line. The economy is then on the gradual move from H’ via M’
to N’ with ever more restrictions on individual freedom to protect deteriorating economic

11
 In the technical sense of Thom 1976.
12
 The wave surface of Diagram 2, derived from Diagram 1, is equivalent to the cusp catastrophe of
 Woodcock and Davis 1978, ‘catastrophe’ meant also in a purely technical sense.
 12

output. At N’ a discrete policy shift from freedom type policies to workforce health type
policies brings the economy into maximum P’ and then further to maximum K’.

 In this example, two otherwise identical Modi-Covid19 economies, starting from
different types of policy maxima, have completely switched places by opposite
developments of their workforce productivity.13 Both policy trajectories include discrete
policy type switches into the former type of the respective other economy. Each switch
of type goes along with a policy crisis when, all of a sudden, the formerly reliable
commitment of the government to a policy type vanishes. Both, individual freedom and
aggregate output then face all of a sudden fast and large changes. Given the number
of countries, e.g. in Europe, with volatile workforce productivities, there is waiting
ahead a Modi-Covid19 health-cum-economic-policy rollercoaster.
 Note that with radical policy shifts there comes along sudden surges and
slowdowns of the epidemic, depending on the direction of the shift. Also note, these
are endogenous surges and slowdowns due to radical changes in how much physical
distance people are requested to keep. This is a further stress to returning to
international cooperation, because governments, having recently switched into a
workforce protection type policy or have been there for long, will reproach others,
switching (prematurely) to a freedom type policy, for their irresponsibility to the health
of people and disloyalty to the international community. This motivates

 H4: The stabilization of the productivity of workforces that were subjected to
 organizational change for health protection reasons (workforce distancing) is a
 step on the way back to international political cooperation.

 7. Protectionism, the Second Dividend and the Road Back to Cooperation
But suppose in the simulation, stabilization of workforce productivity is not yet possible.
Then the risk of sudden unilateral policy switches, which poison the return to
international cooperation, can be also diminished by bilateral reductions of Covid19-
type protectionism, for example between Germany and France or Switzerland. That is
to say, curbing back protectionism not only reoils commerce and trade but also pays a
second dividend by stabilizing coordinated multilateral policy making. Here is the
argument.
 The bilateral lifting of trade and mobility restrictions cancels some of the resource
base deteriorations brought about by the current Nash equilibrium Covid19-type

13
 It is obvious that either country’s policy trajectory is independent of the other country’s trajectory and
 only depends on the trajectory of the domestic productivity.
 13

protectionism, installed within weeks of the outbreak. In the Modi-Covid19 simulation
international supply chains are even more interrupted than today because the epidemic
is more severe than the current. Bilaterally reopening borders (step by step)
corresponds in Diagram 1 to a rotation upwards of the tradeoff-line because the
domestic resource base is beefed up by reestablishing international supply chains.
This rotation will eventually sail the policy maxima clear of the political discount region,
in which citizens accept in times of crisis political compensations for sacrifices made,
which at normal times were insufficient.
 The intuition of the second dividend is simple: Bilateralism restores the national
resource bases beyond the domestic one and brings back economic wellbeing up to a
level where citizens reduce and eventually vacate their crisis-driven loyalty to their
government. As this loyalty is the source of the multiplicity of local policy maxima, the
disappearance of loyalty leads to the disappearance of multiple maxima. Governments
then will have no reason to radically switch policies for endogenous reasons. Lifting
Covid19-type protectionism bilaterally therefore pays the second dividend of making
national health-cum-economic policy making more reliable at times when the workforce
productivity is not yet in its long-run stable state.
 Bilateralism pays a dividend that helps unlock the Covid19-type protectionism Nash
equilibrium when reliability in health-cum-economic policy making becomes again a
valuable international currency. This motivates the following simulation hypothesis:

 H5: Bilateralism in abolishing Covid19-type protectionism pays a second
 dividend in making national health-cum-economic policies more reliable.

 8. Outlook on Contingent Pandemics Defense Planning
The foregoing Modi-Covid19 qualitative simulation is in one respect more apocalyptic
than the current outlook on Covid19. It assumes a virus which is a risk to workforce
health and hence aggregate economic output, more so than Covid19 appears to be
today. The simulation is therefore about future battles against a new and perhaps even
more dangerous mutant, more so than about the path ahead in the current one. Yet
the simulation assumes that countries will exactly open up that battle as they just now
did, leading the world in virus-driven open protectionism with paralyzed international
institutions and lockdowns on the freedom of their citizens.
 When the current battle will enter the endemic phase of Covid19, when govern-
ments will abandon their health-first policies for a more balanced health-cum-
 14

economic-policy approach, they will presumably adopt a demand-side driven policy
regime, lifting the transaction ban, perhaps selectively and successively, on those
industries which are hit most by the lockdown. Yet there is a supply-side economics
perspective to such battles too, not to be forgotten if workforce health is also at stake.
Pointing to this perspective was the purpose of the contingency simulation of a
hypothetical Modi-Covid19 event, which has the same epidemiological properties as
the hypothetical virus in the 2013 Modi-SARS simulation of the Robert Koch-Institute.
 Politicians will have to take their lessons from the current event, more so than from
scenarios they have taken note of in the past.14 But a pandemics defense plan must
not rely only on the lessons learned now. Because the next event could pose different
societal challenges, for example, health-cum-economic challenges, out of a small
mutation of a virus, such as that from Covid19 to Modi-Covid19, assumed in the
present simulation. As the qualitative simulation showed, the challenges posed by
Modi-Covid19 are quite different than those posed by Covid19. Pandemic defense
plans should therefore not rely only on the past lessons learned, but be also based on
mutation contingency simulations, because viruses are born out of chance. This is,
perhaps, the most important message sent out from the Modi-Covid19 simulation.
 The simulation can be criticized for its assumption of the virus being in a controlled
equilibrium. It thereby ignores the highly dynamic nature of a pandemic in the
interaction of host organisms (mobile people), nature (infection and immunization) and
the race against time of science and the pharma industry (drugs and vaccines). Rightly
so. But the foregoing simulation demonstrates that you don’t need a virus out of control
to throw governments into unreliable policymaking that obstructs private business
planning and investment as well as the return to international cooperation and the
international division of labor. A controlled virus suffices, as long as distanced
workforce productivity is still unstable and citizens exhibit emergency-driven loyalty to
government policy. A pandemic defense plan therefore must not only contain the bio-
dynamic side of the event, but also the societal, including politics and policies. The
second message of the Modi-Covid19 simulation is that politics itself is prone to sudden
policy shifts, the virus cannot be blamed for. Pandemic defense planning therefore
must include measures how endogenous policy making can be made reliable.
 Workforce productivity under new forms of workflow organization is inherently risky
if organizational change is hastily cobbled together. Even if it follows a masterplan, set
up for day x, sandbox games going live can produce surprising psychological reactions.
Furthermore, distancing of workforce will not be a once and for all organizational

14
 Such as the German Parliament taking note of the Modi-SARS Report in 2013. Apparently, a different
 thing is to develop from it a clear plan of how to prepare for further events to come, such as, for
 example, Bill Gates’ 2015 pandemics defense plan.
 15

change but be experimental for a while. Also, productivity will be affected by social
factors, for example home (office) work to be combined with teaching children, idled
from school shutdowns. Furthermore, layoffs blow working teams apart, further
stressing their productivity. It is also an open question whether the duration of
homework increases or decreases productivity. It is therefore reasonable assuming
unstable productivities of a distanced workforce for quite a while. Also, as the
comparison with the Eichenberg-Rebelo-Trabandt model suggests, success or failure
of workforce-health protection can have a decisive effect on which bio-economic
tradeoff politics face. To include workforce health provisions and measures that sustain
and stabilize distanced workforce productivity in pandemics defense planning is the
third message of the paper.
 Citizen loyalty to government may be a noble human treat and is in times of national
crises desirable. For example, the Modi-SARS Report simulation assumes an overall
high degree of citizens’ sense of emergency and solidarity. The present simulation
assumes that this also pertains to government loyalty as long as matters neither turn
to the extreme nor become normal again. The simulation results suggest, however,
that it better be kept to times of actual emergency as it fosters unreliable government
behavior. In the interest of the resilience of society, reduit- and other national bunker
mentalities should not be part of longer-term politics. Although the simulation treats
government loyalty of citizens as an endogenous variable, it totally abstracts from the
endogeneity of citizens’ voluntary lockdown compliance and hence from the
epidemiological effectiveness of containment measures. However, given the
simulation-immanent instability of local policy maxima, and given the already observed
radical policy switches such as in the US and in the UK in March 2020, it is doubtful
that a government’s track record of lockdown-zigzagging will strengthen citizens’
voluntary compliance with official social distancing policies. The compliance issue is,
of course, also a policing issue. The less compliant citizens are the more creative
policing will be.15 Policy reliability therefore has also the side effect of strengthening
citizens’ rights at times of emergency. The surprising consequences of the assumed
political discount region suggests that mutation contingency simulations should not shy
away from ‘anomalous’ assumptions concerning the human factor in emergency
situations. This makes pandemics defense planning itself more resilient. This is the
fourth message of the simulation.
 Free trade is not tantamount to unlimited movement of persons. The new optimal
international integration will most likely stay short of the pre-Covid19 one. For example,
permanent and systematic health checks for all outgoing overseas passengers may

15
 Hariri 2020.
 16

be required under an international pandemics defense agreement. National economies
may also settle with a larger domestic resource base as a strategic reserve for the risk
of future Covid19-type protectionisms. But this will be at costs. So much more will it be
necessary to form a resilience alliance in a reliable international health-cum-economic
partnership, such as in a newly emerging Europe (not necessarily the present EU).
Bilateralism and its second dividend are an entry into this process.
 True, the simulation petered out of addressing the biggest challenge ahead. When
(or if) a pandemic will overstress the health infrastructure, tough ethical decisions will
have to be made. Anticipating this, the Modi-SARS Report has pleaded:

 So far there are no guidelines on how to deal with a mass influx of infected persons
 in the event of a pandemic. This problem requires complex medical, but also ethical
 considerations and should be considered preferably not as late as when a special
 crisis situation already has arisen. (Translation of Footnote 7, p. 65).

Pandemics defense planning must plan for ethics discussions simply by initiating them
as early as possible. Concerning health-cum-economic policymaking, one may not
have to face up to identifiable persons, who individually benefit or suffer from tough
triages to be made. But that does not imply that these more abstract choices to take
pose smaller ethical challenges. One issue will be health and economic resilience
improvement. Here comes in herd immunity: How should we ethically pit the risk
groups of today against the benefit of future herd-immunity, which requires a high
degree of infections today and many recoveries later?
 Here again, but now from an ethical stance, we are confronted with different stakes
of demand-side and supply-side bio-macroeconomics. Demand-side stakeholders will
advocate for a herd immunity strategy, as it aligns with their short-run concern of lifting
the transaction ban. However, the supply-side perspective in the Modi-Covid19
simulation is more in line with the current health-first position, as it protects today’s
supply of labor. The challenge from an integrated demand and supply-side perspective
is setting an ethically sound priority or balance between today and tomorrow.
 The tough ethical tradeoffs between todays containment and tomorrows resilience
will be weaker the more cohort-specific the health risk is. Cohorts less vulnerable can
contribute more to the build-up of herd immunity, easing the ethical dilemma between
today and tomorrow. This leaves us begging for Covid19 not to become Modi-Covid19.
In a less fatalistic approach, though, it is also for reasons of easing tough ethical
decisions to be taken, that pandemics defense planning should adopt vulnerability-
specific anti-epidemic measures wherever possible. In the Covid19 crisis, for example,
age-specific quarantine for those 65 and over and easing shutdowns for less
vulnerable cohorts are cases in point. That should be complemented by random testing
in less vulnerable cohorts to monitor the path to go to herd immunity. There will then
 17

be a give-and-take division of labor between generations: The young produce herd
immunity and are compensated with more individual freedom; the old invest into the
resilience of the health infrastructure by complying with an (unjust) age-specific
lockdown, and the return on their investment is the safety net of herd immunity which
allows for a step-by-step lifting of age-specific lockdown. A division-of-labor-between-
generations principle of pandemics defense is a promising ethical mechanism-design
for taking ethical decisions. After all, the population and electorate has been, so far,
not made ready for tough ethical decisions to take.

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References
Deutsche Bundesregierung, D. (2013). Bericht zur Risikoanalyse im
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Dorn, F., et al. (2020). Die volkswirtschaftlichen Kosten des Corona Shutdown für
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Institut.

Eichenbaum, M. S., et al. (2020). The Macroeconomics of Epidemics. NBER Working
Paper Series. NBER.

Gates, B. (2020) https://www.ted.com/talks/bill_gates_the_next_outbreak_we_re_not_ready/details#t-
110391

Hariri, Y. N. (2020). The World After Coronavirus - The Storm will Pass. But the
Choices we Make Now Could Change our Lives for Years to Come. New York Times.
New York.

Thom, R. (1976). Structural Stability and Morphogenesis. New York, Benjamin.

Woodcock, A. and M. Davis (1978). Catastrophe Theory. New York, Dutton.

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