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Updating the Petri dishes of democracy: population density remains a primary determinant of intensity

Updating the Petri dishes of democracy: population density remains a primary determinant of intensity

Over the weekend I saw a map indicating that new coronavirus infections have been increasing on a relative basis in different and generally more rural parts of the country, especially in the Baltimore-Washington portion of the eastern megalopolis and the “black belt” in the South, the interior Midwest and Mountain West:

Below are two charts consisting of the 12 most and least densely populated States, their respective population densities, and several measures of coronavirus infections.

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Three virus-related thoughts for Sunday

Three virus-related thoughts for Sunday

There are a few posts I have been working on, but haven’t had the energy to complete.  But since I wanted to make the point, let me use this opportunity to quickly set forth a few thoughts.

1. I suspect that the virus has been “burning through the dry tinder” in March and April. At least 1/3, and possibly 1/2, of all deaths from the disease have been at nursing homes. When you consider this disease thrives on indoor spaces, recirculated air, repeated dosing with the virus, compromised immune systems, and those who already have cardiovascular disease, that ought to be no surprise.

What I suspect, but don’t have good sourcing for yet, is that a huge percentage of all residents at such facilities have already been infected. Since the turnover at these facilities is on average about every 2.5 years, once the disease has “burned through” this population, that source of fatalities immediately vanishes. Which means that the number of infections and fatalities would presumably decrease.

How much of the decrease in infections and fatalities we have seen in the past month is due to coronavirus burning through this dry timber? I suspect it plays a significant role. Which would mean that the next phase would be determined by how much community spread can occur without these facilities being part of the outbreak.

2. I have read dozens of reports since March of wages being cut. This trend was overwhelmed in the April jobs report by the fact that low wage workers were those who took the brunt of the virus-related shutdowns. Professional workers and other higher-wage employees who could work from home were much less affected.

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Jobless claims show new damage ongoing, but some damage repaired

Jobless claims show new damage ongoing, but some damage repaired

 

Now that we have more than one month of data from initial and continuing jobless claims since the coronavirus lockdowns started, we can finally begin to trace whether the economic impacts of the virus are being contained, or are continuing to spread out into further damage.

Eight weeks in, the answer is mixed.
First, let’s look at initial jobless claims both seasonally adjusted (blue) and non- seasonally adjusted (red). The non-seasonally adjusted number is of added importance since seasonal adjustments should not have more than a trivial effect on the huge real numbers:

There were 2.614 million new claims, which after the seasonal adjustment became 2.981 million.
By now, virtually all of the people laid off due to the initial lockdowns in March and early April should have already applied for benefits. Further, last week was the first week after some States “reopened.” Thus these new claims are almost certainly primarily represent the spreading second-order impacts of the coronavirus shutdowns. In other words, this is evidence that new economic damage continued to spread, and in a very large way.
Next, looking at continuing claims, which lag one week behind, we see a slight (-3%) decline in the non-seasonally adjusted number (red). The less important seasonally adjusted number (blue) rose:

This tells us that, as of two weeks ago, although the new damage was spreading (I.e., the initial claims), there were enough callbacks to work to slightly outweigh that new damage. In a very, very limited sense that is “good” news. But if “reopening” leads to a significant new upturn in cases, it is likely to be a very short-lived positive trend.

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Coronavirus dashboard: updating the 52 Petri Dishes of democracy

Coronavirus dashboard: updating the 52 Petri Dishes of democracy

[Note: There is no significant economic data today (Dan here…May 13)  Thursday we’ll get initial claims, and on Friday retail sales and industrial production for April, both of which will be important]

Here is the update through yesterday (May 12).

I will restart giving the daily increase in infections if States that have “reopened” start to increase significantly again. The preliminary evidence is that customers are largely staying away from reopened businesses in those States.

Number of new and total reported Infections (from Johns Hopkins via arcgis.com and 91-divoc.com):  

  • Number: 22,080, total 1,370,016 (vs. day/day high of +36,161 on April 24)

There has been a 1/3 decrease in the number of new cases in the US from peak. The US nevertheless has the worst record in the world, by far.

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April deflation follows a typical recessionary pattern

April deflation follows a typical recessionary pattern

This morning’s consumer price index for April gives us our first indication of what the coronavirus recession has done to inflation.

Overall consumer prices declined by -0.8% (blue), while consumer prices excluding energy (gas) declined -0.2% (red).

Note that in 2015 when gas prices collapsed, prices otherwise continued to increase, showing the underlying strength of the economy. But in March and April of this year, even prices outside of gas declined, showing underlying weakness.

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If you open it, they still won’t come: restaurant edition

If you open it, they still won’t come: restaurant edition

In case you haven’t already seen it, here is the OpenTable restaurant reservation data from 3 Confederate States that “reopened” their economy at the end of April:

Even though restaurants were open again, reservations were still down over 80% from a year ago.

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Testing and the 52 Petri dishes of democracy

Testing and the 52 Petri dishes of democracy

Since the federal government has abandoned the field, fighting the coronavirus pandemic has been left to the States, territories, and the District of Columbia. This means that there is no unified response and instead there are 52+ individual responses. That is the biggest challenge in tracking the pandemic.

Which means I’ve been looking for the best resources to show how the States are doing in comparison with one another. I’ve found a couple of good ones, so here are snapshots below.

The first is endcoronavirus.com . They have thumbnail graphs of the 50 States, DC, Puerto Rico, Guam, and the Virgin Islands, divided by the trend in their daily new cases. The list needs to be updated from May 5, as shown in the below sample of “States and Territories that are Nearly There”

The 6 jurisdictions “Beating COVID-19” are either low-density, rural States, or islands:

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The humans always observe back: why I am rooting for NY to “crush the curve”

The humans always observe back: why I am rooting for NY to “crush the curve”

So, in addition to a bunch of States in the Confederacy and a few in the high plains deciding that May 1 was the Day of Virus Jubilee, yesterday saw further discouraging news that not one but two epidemiological models drastically increased their estimates of deaths, while there was another revelation that Trump and the White House were relying on a “cubic” model devised by Kevin Hassett of “Dow 36,000” infamy, showing death abruptly declining to zero by about May 15.

First came the CDC model, that shows deaths beginning to increase exponentially again after May 14 (not coincidentally, two weeks after the Day of Virus Jubilee):

Then, the IHME, whose model had only recently forecast 60,000 total deaths from the virus, and as recently as a week ago showed deaths declining to *0* by June 1, revised the model to show instead a gradual tapering off of deaths throughout the summer, with a total death toll of 135,000:

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April jobs report: disastrous, but not as cataclysmic as feared; lower paid part time workers take the biggest hit

April jobs report: disastrous, but not as cataclysmic as feared; lower paid part time workers take the biggest hit

 

HEADLINES:
  • -20.5 million jobs lost. Between March and April this is a loss of 14.0% of all jobs since February.
  • U3 unemployment rate up 10.3% from 4.4% to 14.7%
  • U6 underemployment rate rose 14.1% from 8.7% to 22.8%
  • February and March were both revised downward, by -45,000 and -169,000 respectively, for a net decline of -214,000 jobs from previous reports.

Leading employment indicators of a slowdown or recession

 

I am still highlighting these because of their leading nature for the economy overall.  These were uniformly very negative:

  • the average manufacturing workweek fell -2.1 hours from 40.4 to 38.3 hours. This is one of the 10 components of the LEI and will be a big negative.
  • Manufacturing jobs fell by 1.3 million. Manufacturing has lost 1.364 million  jobs in the past 2 months.
  • construction jobs fell by -975,000. In the past 2 months 1.08 million construction jobs have been lost.
  • Residential construction jobs, which are even more leading,  declined by -117,600. In the past two months together there have been -119,200 lost jobs.
  • temporary jobs declined by -842,000.
  • the number of people unemployed for 5 weeks or less rose by 14.3 million.
  • Professional and business employment fell by -2.128 million, or -10.0% for the month.

Wages of non-managerial workers

 

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Infections in US States by population density

Infections in US States by population density

Since COVID-19 is a communicable disease, it should hardly be a surprise that the most densely populated States have the most cases per capita, and conversely the least densely States have the least cases.  But since that basic point is lost in a lot of the analysis, let’s take a look.

Below are two charts consisting of the 12 most and least densely populated States, their respective population densities, and several measures of coronavirus infections.

The first column gives the rank of the State based on the total number of infections recorded since the start of the pandemic. The second column gives their rank per capita over time since the start of the pandemic. Finally, the third column gives their rank per capita based on infections just over the past week:

State Population
Density
(Per Sq. Mile)
Total #
Infections
(Rank)
Infections
Per capita
(Rank)
1 week
Infections
Per capita
NJ 1208 2 2 2
RI 1010 21 4 1
MA 867 3 3 3
CT 741 9 5 6
MD 614 12 10 8
DE 484 33 8 9
NY 419 1 1 7
FL 376 8 28 37
PA 286 6 12 14
OH 284 14 27 26
CA 251 5 32 31
IL 231 4 29 5
Figure 1
On a per capita basis, the data closely fits these States ranks in population density, with three noteworthy outliers: Florida, Ohio, and California. Florida has been rumored to have been massaging their data, but those issues do not pertain to the other two.

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