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Coronavirus dashboard: emphasis on testing

(Dan here…NDd’s post points to more than the impact of the US catching up in testing only recently, but also points to beginning answers readers have asked in comments about what the statistics show regarding re-opening and where we might be failing to report. )

Coronavirus dashboard: emphasis on testing

I want to focus this edition on testing issues.

While the seven day average number of deaths continues to decline:

The seven day average number of new infections has leveled off:

The average number of daily tests *may* also be leveling off again in the past few days:

What is worse is that the number of new cases in the US has only declined -22% from its peak in the last 45 days. Meanwhile, even hard hit countries in Europe like Spain have seen a -90% decline from peak in new cases over a similar timeframe:

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RIP Oliver Williamson

RIP Oliver Williamson

Oliver Williamson died yesterday at age 87, I do not know of what. He was famous as the main developer of New Institutional Economics, following the influence of Ronald Coase, which emphasizes the role of transactions costs in the formation and development of economic (and some other) institutions.  He received the Nobel Prize in 2009, along with Elinor Ostrom, but his influence was really quite vast for a man from a working class background, born in Superior, Wisconsin.

I checked, and although there may be another one ahead of him, as near as I can tell at his death only one other living economist had more Google Scholar citations. Andrei Shleifer has about 295,000 (h has a problem getting that trip to Stockholm because of his infamous corruption problem involving Russia), while Ollie had about 276,000.  The all time leader is Karl Marx at about 333,000.

A reason for this is that his work became enormously influential across other disciplines, especially management and law.  Indeed, he is one of the few individuals I know of (I know of only one other, Shyam Sunder at Yale) who was simultaneously in an economics department, a separate business school (I think the Management dept), and a law school, all of which he was at UC-Berkeley.  The only other person I know of who was in more departments was the late polymathic Nobel Prize winner, Herbert Simon, more on him below.

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Bad news and good news on coronavirus; plus, implications for Election Day

Bad news and good news on coronavirus; plus, implications for Election Day

No economic news today as we head into the Memorial Day weekend, but there are a few coronavirus and economic/political developments of note.

First, the bad news: the declining trend in new diagnosed cases of coronavirus in the US has stopped in the past week. Instead new cases have leveled off. Here’s a graph from Conor Kelly’s excellent tableau coronavirus dashboard page:

Cases in the US outside of NY actually increased slightly (2%) in the past week.

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The Cass County, Indiana, Easter Effect

As noted in my last post, I have been looking at data. This usually causes trouble, and today is no exception.

As anyone who was paying attention predicted, the “Easter Effect”–a large gathering of people (“EC” or Otherwise) in an enclosed area that likely has multiple asymptomatic carriers (and likely a few with symptoms) is a recipe for infection. With a two- to three-week gestation period, that there was going to be an increase in cases at the end of April was well known. The only question was how much. Without running the numbers carefully, it looks as if it was about 10% above trend.

But the overall data covers for a lot of local sins. If you look at the places that have a high percentage of people infected, the relatively large Metropolitan Areas are no surprise: Providence, Worcester (MA), and NYC suburbs and exurbs (think Rockland, Westchester, Nassau, Suffolk, and Orange Counties in NY State; Passaic, Union, Hudson, Bergen, and my own Essex County in NJ).

But Cass County, Indiana, is running at over a 4% infection rate. With a County size of about 38,000 people, they’re reporting just under 1,600 cases.

Will anyone be surprised that those cases were not evenly distributed? Not really convinced this is what people have in mind when they say, “He is Risen”:

My source is the New York Times’s County-level data (h/t Charles Gaba at acasignups.net); their source is probably the Indiana State Deparment of Health, which has been getting an A+ for their data from the Covid Tracking Project.)

Data excerpt attached below.

CassCountyTable

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How Large is the Income Shifting Problem?

How Large is the Income Shifting Problem?

I took up this invitation from Dan Shaviro:

tomorrow morning I’ll be participating in a very interesting international tax policy conference with a number of outstanding participants. It’s on Zoom … I’m actually the second speaker on Panel II (although we’re listed above alphabetically), so I will be speaking from roughly 11:08 to 11:15. I’m planning to discuss the OECD’s Pillar 1 and Pillar 2 initiatives, although what exactly I’ll say remains somewhat flexible pending the keynote address, which may offer updates (at least to me) that are of interest

He gives the entire agenda, which can also be found here. This blog post focuses on Panel I, which noted the difficulties of measuring the extent of income shifting to tax havens as we have the papers that formed the basis of the presentations by Kimberly Clausing and Leslie Robinson. Before I proceed an appeal to anyone who has a transcript of what Dan Shaviro and Victoria Perry (IMF) said as both had intriguing remarks on the enforcement of transfer pricing, which I want to include in a follow-up post. Clausing noted:

This research note describes the plausible magnitude of US revenue loss due to profit shifting, building on recent developments in the literature as well as new country-by-country data on US multinational companies in 2017. In the past, the most complete data sources have all shown large magnitudes of profit shifting, suggesting substantial revenue losses in non-haven countries. Blouin and Robinson (2019) have challenged this consensus, noting that many data sources may be flawed due to the inadvertent inclusion of double-counted profits or through an inadvertent misallocation of profit. Nonetheless, their proposed adjustment to the data generates its own puzzles, and experts at both the BEA and the JCT believe that the proposed adjustment will omit some types of profit shifting. Beyond that, Blouin and Robinson’s conclusions regarding how their adjustments affect the scale of profit shifting set aside many nuances in method that affect bottom-line findings about the scale of profit shifting. This research note uses recently released country-by-country tax data to estimate plausible benchmarks regarding the scale of profit shifting, finding that profit shifting is likely to be costing the US government over $100 billion a year in 2017 (at 2017 tax rates). While much can be done to refine these estimates and learn more about the scale of the problem, the problem remains unambiguously very large.

Robinson writes:

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Weekly Indicators for May 18 – 22 at Seeking Alpha

by New Deal democrat

Weekly Indicators for May 18 – 22 at Seeking Alpha

My Weekly Indicators post is up at Seeking Alpha.

Many of the indicators are bouncing off their worst levels, especially those like mortgages that are affected by lower interest rates. On the other hand, employment losses continue to spread out.

As usual, clicking over and reading not only should be educational for you, but rewards me a little bit for my efforts.

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Initial jobless claims: employment damage continues to spread

Initial jobless claims: employment damage continues to spread

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

Nine weeks in, it appears that, insofar as employment is concerned, the damage is continuing to spread.

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 2.174 million new claims, which after the seasonal adjustment became 2.438 million. This is a slight decline from last week’s number which was revised down to 2.687 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 second 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 have  continued to spread, and in a very large way.

Next, looking at continuing claims, which lag one week behind, both the non-seasonally adjusted number (red), and the less important seasonally adjusted number (blue) rose:

This tells us that, as of two weeks ago, there were not enough callbacks to work to offset the spreading new damage. If “reopening” leads to a significant new upturn in cases – something that may have begun in the past week – this will only get worse.

Bottom line: confining my comments strictly to the economy, while there have been significant or small rebounds in many of the series, the news on employment is not just bad, but it is still getting worse, albeit getting worse at a slower rate.

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Hydroxychloroquine After Action Report

I was a vehement advocate of prescribing hydroxychloroquine (HCQ) off label while waiting for the results of clinical trials. I wasn’t all that much embarrassed to agree with Donald Trump for once. Now I feel obliged to note that my guess was totally wrong. I thought that the (uncertain) expected benefits were greater than the (relatively well known) costs.

The cost is that HCQ affects the heart beat prolonging the QT period (from when the atrium begins to contract to when the ventrical repolarizes and is read to go again). This can cause arrhythmia especially in people who already have heart problems. I understood that one might argue that all people with Covid 19 have heart problems but didn’t consider that argument decisive (I probably should have).

The positive expected value of the uncertain benefits was based on strong in vitro evidence that HCQ blocks SARS Cov2 infection of human cells in culture. (this is a publication in the world’s top general science journal).

Already in early May, there was evidence that any effect of HCQ on the rate of elimination of the virus must be small. In this controlled trial conducted in China, the null of no effect is not rejected. Much more importantly, the point estimates of the effects over time are all almost exactly zero. I considered the matter settled (although the painfully disappointed authors tried to argue for HCQ and that their study was not conclusive).

There are now four large retrospective studies all of which suggest no benefit from HCQ and two of which suggest it causes increased risk of death. I am going to discuss the two studies most recently reported.

One is a very large study (fairly big data goes to the hospital) published yesterday in The Lancet. In this study patients who received HCQ had a significantly higher death rate with a hazard of dying 1.335 times as high. The estimate comes from a proportional hazard model with a non parametric baseline probability and takes into account many risk factors including crucially initial disease severity. It is also important that only patients who were treated within 48 hours of diagnosis were considered.

I am, of course, dismayed by this result. I am also puzzled, because it is quite different from the result obtained in a smaller retrospective study published in JAMA

I think the practical lessons are that it seems unwise to give Covid 19 patients HCQ. Also maybe Robert Waldmann should be more humble. After the jump, I will discuss the two studies in some detail and propose an explanation of the difference in results.

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Maybe This Is Not (Technically) A Recession?

Maybe This Is Not (Technically) A Recession?

Here I am using what is the journalistic definition of a “recession,” also used in many nations although not officially in the US, where these things are determined ex post by an NBER committee.  Anyway, that “journalistic” definition is that there be two consecutive quarters of negative GDP growth.  Today in the Washington Post I saw a story on global carbon emissions, which are very closely correlated with GDP, if not perfectly. Anyway, it appears that global carbon emissions hit bottom on April 7 and have been slowly rising since then (not sure about US separately, although US somewhat behind most other nations on the covid curve and so on the economic impact as well). I note that April 7 is one week into the second quarter.

This means it is very likely that at the global level we shall see positive economic growth in the second quarter, basically rising since the end of the first week of the quarter, although due to reporting lags in many countries this will not show up as positive growth in the data until later, possibly by the end of the month. This growth is slow, but it is positive, definitely not a V.

So, assuming this slow growth continues,the world will have seen a massive shock in the first quarter, with most of that in a single month, March, the largest such short term shock in recorded history by far. But it looks that it may have hit bottom quite quickly, then to turn into a slow recovery shortly after the end of the first quarter.  First quarter is certainly going to be negative, but second looks very well like it might be positive, at least at the global level, hence, not technically quite a “recession” according to this journalistic definition.

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