As the world's scientists race to find a cure for the Coronavirus, will they work in private labs or will they openly share data and work in an "open source" research environment.  While fame and fortune (the Nobel Prize, billions in drug patents) would suggest the private lab approach, science will make faster progress if they work as "open source hippies".

Suppose that each research team has its own favorite drug treatment.  This treatment consists of injecting m different compounds into a person suffering from the Virus. So, if m=2 --- each treatment can be described by how much of the first attribute and how much of the 2nd attribute is injected into a person to potentially cure the sick person.  Call this (x1,x2).

If each research team reports the following information;

Did the Coronavirus infected person recover within Z days?
Age of the infected person
gender of the infected person
race of the infected person
major medical pre-existing conditions of the infected person
(x1,x2) injected into the person.

Then a Machine Learning team can use these data from each research team (so if there are 20 research teams that each enroll 500 sick people) there would be 10,000 data points in total.

The ML approach would yield an estimate of what is the best treatment for different demographic groups and an efficient vaccination process could begin.

If the samples can be pooled across science teams, could the vaccine be more quickly identified and started to be mass produced?

Do science teams have ways to co-ordinate their sharing of their data or will some famous scientist start the process by sharing her data and hoping others will follow?

In the past economists have debated the merits of open-source science.    In a high stakes setting, these issues arise again. 

With some types of machines such as a coffee grinder, it works when you press the button and it doesn't work when you don't.   As aggregate demand for air travel, restaurants and just about everything declines due to virus self distancing efforts, will a short run recession trigger long run negative effects?

An optimist might posit that the coming recession will have little medium term impact because our human capital and physical capital will not change much.  Such an optimist would have to grapple with the wealth effect of the lost income due to a decline in real estate values and asset values.

A more pessimistic view points to the human capital dynamics of adult workers.  Throughout this blog post, I assume that the death count of adult workers is low. 

Labor economists have discussed duration dependence for decades.  Under this theory, a worker's skills atrophy as she spends more time not working.  So, note that this is a treatment effect (causal) hypothesis. The more time you have not worked causes you to be less employable in the next moment. One micro mechanism is that you start drinking or become even more lazy.

The Coronavirus will provide a natural experiment for studying the duration dependence hypothesis.  Economists should note that the virus causes mass unemployment and thus concerns about selection bias (that losers are more likely to be fired) is a 2nd order concern here.

In an underappreciated AER paper from 20 years ago, Lanier Benkard explores a different theme. In this paper. Lanier argues that if a producer releases workers during bad times that these workers with firm specific and product specific production skills may not return to the same firm. Once product demand increases, this means that the firm will need to bear new training costs to produce the same products. A prediction of this model is that such firms should keep such "asset specific" workers on payroll during bad times to avoid organizational forgetting.

So, unemployment will increase more in firms and industries that feature less firm specific human capital and where worker training costs are lower.

The medium term economic loss, caused by reduced demand caused by the virus, will be larger if firms fire workers now have plenty of firm specific human capital.  If these fired people lose their ability to work  (duration dependence) , forget their skills (Benkhard) or find other jobs; then the productivity losses for the firms will be more persistent.

The City of Baltimore has one of the highest murder rates in the United States. The murder rate has remained high since the Freddie Gray riots in April 2015.  Could a sharp reduction in the city's murder rate now take place as a silver lining of the social distancing induced by fear of the Coronavirus?

Here is my logic.   Fear of the Coronavirus nudges people to not interact at close range.  As we spread out and crowds disperse, fewer violent face to face social interactions will take place.  Thus, fewer murders will take place in the short run.

Could a short run reduction in violence feed on itself through a path dependence effect?   In New York City, Mike Bloomberg's policies reduced crime.  Bill De Blasio has relaxed many of these stringent policing policies but crime has remained low.  One story that is out there is that Bloomberg's policies changed the social norm and the time of peace has persisted under Mayor De Blasio's time even though the "stick" incentives to deter crime have ended in New York City.

So, could the Coronavirus cause peace in Baltimore and then peace feeds on peace?

I recognize that this logic is less likely to hold if most murders are related to the drug business in eliminating rivals and privatizing the commons (i.e street corners where drugs are sold).

The aggregate contagion risk posed by the Coronavirus declines if more people engage in voluntary social distancing.    Given that social distancing reduces one's own risk of being infected, is this a sufficient incentive to enforce this public health mandate?

Standard microeconomic logic would predict that there will be a free rider problem here as everyone hopes that everyone else will engage in social distancing.   This issue of course arises with respect to greenhouse gas emissions production in an economy where there is no carbon tax. 

This free rider concern may not be important here because it "takes two to tango".  The joy from not engaging in social distancing hinges on finding other people who want to be close to you.  Given the anxiety about this "known unknown", are there many urban people who are eager to break the norm and agglomerate close to each other? 

If there are many of these people and if cell phones allow these urbanites to find each other in cities, then contagion rates could increase as a byproduct of their face to face interactions.  These folks do not want to cause the epidemic to be exacerbated. Instead, they may be impatient people with a need to socialize and some may underestimate the probability that their encounters pose risks to themselves and society.  Young people know that their death risk is lower and thus they may be more likely to free ride and continue with their lives.

In this case,  Robert Ellickson's work on "norm enforcers" is relevant.  If the police do not use their power to disperse crowds, will social sanction identify such individuals and further ostracize them?

Dora and I explore shame and ostracism in our 2007 paper on deserters in the U.S Civil War.

Now a completely different urban economics issue pertaining to the Coronavirus focuses on the size of housing.  The media is reporting that those in jails and other settings featuring high indoor population density are at risk because people are too close to each other.  This issue that cities "are too dense" will emerge here.

If we were uniformly distributed across the U.S, then reducing contagion risk here would be much easier because transportation costs to allow people to meet would be high enough to reduce disease transmission.

Benchmarking "relative performance" can yield bad outcomes when the other cities do not represent a valid control group for the treated entity.  Since Wuhan has no "twin", benchmarking Wuhan to other cities is not a valid comparison. Since the Wuhan leaders want to be promoted and know the promotion criteria (that involves benchmarking relative to other cities), this introduces perverse incentives for their early effort.