Relative Fiscal Stimulus

The Biden administration and Democrats in Congress are closing on passing a new fiscal stimulus package.

As always, an economist should be asking, compared to what? Greg Mankiw posted this chart drawn from Moody's Analytics:

Do note that this is scaled by GDP, so this is showing that the 2 stimulus packages passed so far are already more generous than those in every other large economy except Japan. Reasonable estimates of the size of our third package will blow right past Japan.

And, of course, as the largest economy in the world, the close to largest percentages of the largest economy indicate that our government has already gone bigger than everyone else.

N.B. While the cases and deaths in the U.S. are large, we are the third most populous country in the world. Relatively speaking, our pandemic is objectively not worse than most of the other countries on this list.

You Trade Most with People Who are Physically Closer, and Most Like You

Visual Capitalist posted a chart of the UK's trade relationships with the EU members. A chart like this could be done for just about any country.

 

Also, rather obviously, countries also trade most with whomever is economically biggest: bigger economy tends to mean more trade.

So, not surprisingly, the UK trades the most with Germany: it's big, it's fairly close, and it's culturally similar to the UK.

France and Italy are the next two biggest countries in the EU, and the UK trades less with them because they're smaller. They're also more culturally similar to each other, than they are to the UK, which will tend to decrease trade. Italy is further away than France, so it's lower on the list.

The UK, the Netherlands, and Germany share a more Germanic culture, language, and majority Protestant background. On the other hand, Belgium shares with France and Italy a Romance language and "latin" culture, and majority Catholic background. So, the Netherlands is a bigger trading partner than is Belgium, even though the countries are comparable and Belgium is closer.

The other big trading partner is Ireland. Different culture, different religious background, but a mostly shared language. AND, Ireland is the only country that shares a land border with the UK, and it's also the fairly close over water, so, lots of trade there.

Alternatively, if you look down towards the bottom of the list, you see mostly countries that have a Slavic rather than a Germanic language, most of which are further away, and smaller, with a religious background that is sometimes Catholic and something Eastern Orthodox. The UK doesn't trade much with those countries, but there are exceptions. Cyprus and Malta, are the two smallest countries in the EU. And yet they are not at the bottom of trade with the UK. Why is that so? Probably because both of them spent time as colonies of the UK!

FWIW: a model in which trade depends on distance is called a "gravity model". Paul Krugman (who now writes an opinion column for The New York Times) won a Nobel Prize for its development.

List of GDP Stats

In class I showed a bunch of these. There are more. I'm not concerned about you knowing all this stuff. It's just lists of facts after all. The bigger point is that you should know that these things exist and you can go find them if necessary.

Here's what I showed you in class:

• List of U.S. states ranked by GDP (you should know the big four, and the areas of the country where the poorer states tend to be located).
• List of U.S. metropolitan areas ranked by GDP (you should know the big three, and be able to name a bunch of the top 10 or 15.
• List of U.S. counties ranked by GDP (you should know that this exists, that New York county, New York City, and New York state are not similar at all, and maybe the names of a few prominent counties around the country. For example Harris County was in the news about mail in voting quite a bit back in October, but it would be natural for someone to say "who the heck cares about some county down in Texas", but it would be missing why that county is important. It's also important because of the counties that are very big economically, but really don't have a major city in them, like Orange County, and Fairfax County).
• List of world metropolitan areas by GDP (you should know the big ones, but also the surprises on the list: that Tokyo is huge, that Seoul is one of the biggest, or that Istanbul would be near the top of the list).
  
• List of provinces in China by GDP (you should know the big ones, like Guangdong, Shandong, Jiangsu, because these things are as populous as big western European countries, and some are approaching their level of GDP. You should also know that the U.S. has one city that isn't in a state but is comparable in its own right — the Washington metropolitan area is our 6th biggest and sprawls past the limits of D.C., while China has designated three this way: Shanghai, Beijing, and Chongqing).
• List of NUTS-1 regions in Europe by GDP. The EU is trying to get rid of the old subdivisions within member countries, which were of different sizes and often had more relevance historically than they do today, with a new system called by its acronym NUTS (there's maps here). NUTS-1 are the biggest ones, these are divided into NUTS-2 sub-regions, and those further divided into NUTS-3 sub-sub-divisions. By the time you retire, people may talk about something like Northwest Italy or East Sweden or South Spain as naturally as Americans talk about North and South Carolina. It's also plausible that some of the NUTS preserved historical features. For example, is the NUTS-1 containing Paris really the richest one in Europe, or did the French convince everyone else that their main one should include more territory than others, while the English didn't bother to add stuff on to Greater London? You should know the big ones around the continent: this is where the action is.

When I write that you should "know" this stuff, that does not mean I'm going to check if you have them memorized for an exam. It does mean you should know where to find this information, and be curious about the details. 

For example, you might note that Wuhan is on the list of biggest metropolitan areas, not far below Miami and Madrid and above Sydney: Wuhan was not a household word 13 months ago, but perhaps people would have paid more attention to COVID-19 if they knew how big Wuhan actually was on the world stage. Wuhan was really the first place in the world to ever be locked down (on January 23, 2020); can you imagine our reaction if Miami had been locked down? 

It was also a big thing when COVID-19 had its first sustained outbreak in Europe, in northern Italy in late February (a map towards the bottom of this post shows that it didn't land in a poor area, or slowly spread from the outside, but rather landed in one of the richest areas of Europe, and quickly clobbered it). Perhaps we would have paid more attention if people knew that the Northwest Italy NUTS that was hit initially has a GDP larger than the entire San Francisco Bay area.

Also, everything in this class coverage was about size of GDP. But we're also concerned about GDP per capita, and there's a wealth of lists of that too.

Our Strange Recession

The way we study things in generally experimental sciences is to make multiple observations of the same thing under controlled conditions.

The way we study things in generally non-experimental social sciences is to make multiple observations of things we presume might be the same, and then make sure we have all the other variables that might change that included in our analysis. 

The problem with doing this with recessions is ... they're often very different from the preceding one, and often from the whole set of about 40 recessions that we know of, going back in the U.S. to 1820.

Take the example of the current pandemic/lockdown recession. It's an even 100 years since we had a pandemic during a recessions. And, no country on Earth ever locked down anything before they all pretty much decided to just that late last winter.

Anyway, let's take a look at the labor market to get a sense of how different it is. The two graphs come from the Bureau of Labor Statistics website, which has nice tools for finding and charting data.

First up, here's the unemployment rate. This only goes back to 1948, so it missed the Great Depression, which would be not just off the chart, but probably 2 additional blocks up at 25%.

Even so, we can see how the ongoing recession is different: the peak rate is higher, while always steeper on the LHS this one is even steeper, and while it may not continue this way all the way down, it looks like the descent is faster than in earlier recessions. 

Even stranger is the labor force participation rate:

 

Generally, it's fairly hard to spot recessions in the labor force participation rate. 

Digression: this data is often interpreted incorrectly, and this is as good a time as any to explain. Notice that the hump is roughly symmetrical from about halfway up on the left (around 1980), to around 2015 on the right. Casual observers are inclined to say that the labor force participation rate went up, almost exclusively, because of women entering the workforce. That isn't the case: that was more of a smooth rise up until about 2000 — it does little to explain the hump. Instead the hump is likely from baby boomers being in the labor force during their prime years.

Anyway, it's fair to say from this chart that labor force participation has also behaved unusually in the pandemic/lockdown recession.

Looking at the raw numbers may help.

 

Year and month	Civilian noninsti- tutional population	Civilian labor force						Not in labor force
		Number	Percent of population	Employed		Unemployed
				Number	Percent of population	Number	Percent of labor force
1986	180,587	117,834	65.3	109,597	60.7	8,237	7.0	62,752
1987	182,753	119,865	65.6	112,440	61.5	7,425	6.2	62,888
1988	184,613	121,669	65.9	114,968	62.3	6,701	5.5	62,944
1989	186,393	123,869	66.5	117,342	63.0	6,528	5.3	62,523
1990	189,164	125,840	66.5	118,793	62.8	7,047	5.6	63,324
1991	190,925	126,346	66.2	117,718	61.7	8,628	6.8	64,578
1992	192,805	128,105	66.4	118,492	61.5	9,613	7.5	64,700
1993	194,838	129,200	66.3	120,259	61.7	8,940	6.9	65,638
1994	196,814	131,056	66.6	123,060	62.5	7,996	6.1	65,758
1995	198,584	132,304	66.6	124,900	62.9	7,404	5.6	66,280
1996	200,591	133,943	66.8	126,708	63.2	7,236	5.4	66,647
1997	203,133	136,297	67.1	129,558	63.8	6,739	4.9	66,837
1998	205,220	137,673	67.1	131,463	64.1	6,210	4.5	67,547
1999	207,753	139,368	67.1	133,488	64.3	5,880	4.2	68,385
2000	212,577	142,583	67.1	136,891	64.4	5,692	4.0	69,994
2001	215,092	143,734	66.8	136,933	63.7	6,801	4.7	71,359
2002	217,570	144,863	66.6	136,485	62.7	8,378	5.8	72,707
2003	221,168	146,510	66.2	137,736	62.3	8,774	6.0	74,658
2004	223,357	147,401	66.0	139,252	62.3	8,149	5.5	75,956
2005	226,082	149,320	66.0	141,730	62.7	7,591	5.1	76,762
2006	228,815	151,428	66.2	144,427	63.1	7,001	4.6	77,387
2007	231,867	153,124	66.0	146,047	63.0	7,078	4.6	78,743
2008	233,788	154,287	66.0	145,362	62.2	8,924	5.8	79,501
2009	235,801	154,142	65.4	139,877	59.3	14,265	9.3	81,659
2010	237,830	153,889	64.7	139,064	58.5	14,825	9.6	83,941
2011	239,618	153,617	64.1	139,869	58.4	13,747	8.9	86,001
2012	243,284	154,975	63.7	142,469	58.6	12,506	8.1	88,310
2013	245,679	155,389	63.2	143,929	58.6	11,460	7.4	90,290
2014	247,947	155,922	62.9	146,305	59.0	9,617	6.2	92,025
2015	250,801	157,130	62.7	148,834	59.3	8,296	5.3	93,671
2016	253,538	159,187	62.8	151,436	59.7	7,751	4.9	94,351
2017	255,079	160,320	62.9	153,337	60.1	6,982	4.4	94,759
2018	257,791	162,075	62.9	155,761	60.4	6,314	3.9	95,716
2019	259,175	163,539	63.1	157,538	60.8	6,001	3.7	95,636
2020	260,329	160,742	61.7	147,795	56.8	12,947	8.1	99,587
Monthly data, seasonally adjusted(1)
2020
January	259,502	164,455	63.4	158,659	61.1	5,796	3.5	95,047
February	259,628	164,448	63.3	158,732	61.1	5,717	3.5	95,180
March	259,758	162,721	62.6	155,536	59.9	7,185	4.4	97,037
April	259,896	156,478	60.2	133,370	51.3	23,109	14.8	103,418
May	260,047	158,200	60.8	137,224	52.8	20,975	13.3	101,847
June	260,204	159,797	61.4	142,100	54.6	17,697	11.1	100,407
July	260,373	160,085	61.5	143,777	55.2	16,308	10.2	100,288
August	260,558	160,818	61.7	147,276	56.5	13,542	8.4	99,740
September	260,742	160,078	61.4	147,543	56.6	12,535	7.8	100,664
October	260,925	160,718	61.6	149,669	57.4	11,049	6.9	100,207
November	261,085	160,536	61.5	149,809	57.4	10,728	6.7	100,548
December	261,230	160,567	61.5	149,830	57.4	10,736	6.7	100,663
2021
January	260,851	160,161	61.4	150,031	57.5	10,130	6.3	100,690
Footnotes (1) The population figures are not adjusted for seasonal variation.
NOTE: Revisions to population controls and other changes can affect the comparability of labor force levels over time. In recent years, updated population controls have been introduced annually with the release of January data. Additional information is online at https://www.bls.gov/cps/documentation.htm#pop.

I think it's fair to assume that we were at full employment in January 2019. Using that as a baseline, it's fair to say that unemployment is up by about 4.3 million people.

Given the scale of the numbers, population growth contributes a little to that, but can probably be safely ignored. For example, if population grows by about 0.5%/year (as shown in the leftmost column), each column in the table should go up by about that rate, and for unemployment that might contribute 0.1 (rounded up) to the 4.3 million increase.

For the employed, we are off about 8.6 million. But, from above, only half of those are unemployed. Where did the rest go? They are picked up in the decline in the labor force by 4.3 million. 

Note that for the employed and the labor force that the population increase of 1.3 million will make more of a difference. Doing a rough distribution, I'd say that 1.2 million should have gone into employment (meaning the announced number is down more than it looks), 0.1 into unemployment, and 1.3 into the labor force. So, adjusting a little gets the labor force down by 5.6M, employment down by 9.8M, and unemployment up by 4.2M.

Recall how people can be outside the labor force, but still in the civilian non-institutional population: they do not have a job and they are not looking. Further, if they all were looking, unemployment would be up to about 15.7M or a rate of 9.5%. That should put things in perspective that we are in pretty bad shape: it's comparable to the two highest peaks in the unemployment rate in the top chart.

From my longer perspective, I'll tell you that this is something the Democrats have always worried about in recessions, but which never really showed up in the data very seriously: people not interested in being employed. Now we have that, so it's important not to judge this recession by just its unemployment rate. 

It also means that as far as policy goes, we also shouldn't be excessively focused on the unemployed. Yes, we have a lot of them, and many deserve help from policy. But, since past recessions had far fewer people dropping out of the labor force, it's not clear that existing policies or off-the-shelf proposals are going to be focused on the right people.

Historical Real GDP (for the U.S)

The extended data set shown in the beginning of the Barro text comes from the Maddison Project. Angus Maddison was an economist whose life work was the creation of very long historical GDP series. The project is now part of the Groningen Growth and Development Centre at the University of Groningen in The Netherlands.Their data is free to download.

As of this semester, the data for the U.S. is annual, and has been extended back to 1820. These charts come from a spreadsheet saved to the class's shared Google Drive folder.

I am not certain, but I'm really sure that Barro got his data from the Maddison Project (there really aren't any competitors), and that probably at the time he downloaded it, it only went back to 1869.

U.S. Costs of COVID-19

Here's an infographic from Visual Capitalist:

These are drawn from "The COVID-19 Pandemic and the $16 Trillion Virus," by Cutler and Summers.

Since you're all undergraduates, I do try to clue you in to authors and sources you should pay attention to. 

• Visual Capitalist didn't do the research, just the image. 
• The article was published in the Journal of the American Medical Association, the best or second best journal in the field.  But, it was published in their opinion section, rather than under basic research.
• David Cutler is a professor of economics at Harvard (and a former department chair and college dean). He is one of the top researchers in health economics. Here's Cutler's Google Scholar page (70K cites and an h-index of 107 is crazy), Cutler's biographical page at Harvard, and his Cutler's Wikipedia page. He also served in the Clinton and Obama White Houses. In my view, Cutler might be moving up from the medium to the short list for a future Nobel Prize.
  
• And Lawrence Summers is even bigger. He's a professor of economics and former president of Harvard, He is one of the top researchers in macroeconomics, and was already huge when I was in graduate school 30 years ago. Here's Summer's Google Scholar page (157K cites, and an h-index of 178), and Summers' Wikipedia page. He served in the Clinton White House, topping out at Secretary of the Treasury. He was also Chief Economist at the World Bank. Here's Summers' press biography. And, both his parents were economics professors at the University of Pennsylvania, and ... get this ... each of his parents had a brother who was an economist who won a Nobel Prize. I think Summers is probably a lock on a Nobel Prize in the next decade.

The bottom line is that they will have a huge audience.

The biggest piece of the pie is reduced economic activity. Cutler and Summers draw on the Congressional Budget Office for that number, which is a sum over the next decade. Personally, it seems high, but not outrageous to me.

For regulatory purposes, economists have estimated the value of a human life. Non-economists find this controversial, mostly because we do it at all, but it serves a need. Their figure of $4.4T for premature death uses an estimate of ultimately 625K deaths valued at $7M each (that's a conservative value, I usually use $10M). I find the deaths number plausible, but not the overall total. This is because the $7M figure is for a whole life. While I don't generally agree with the common viewpoint that a COVID-19 diagnosis affects the old, I do think the total number should be cut down by quite a lot ... probably over half.

I am less well-versed in the costs of long-term health impairment. Having said that, I think everyone underestimates this. Since Cutler and Summers can back up their numbers, I'll accept them. The same goes for the mental health costs.

Now let's put this in perspective. It would be incorrect to naively compare this number to GDP. The value above is a stock variable, while GDP is a flow variable. (The authors do this anyway, because it's popular, but I don't encourage my students to do this). A better comparison might be to financial wealth of the United States, which is about $100T. So COVID-19 is going to knock off about a sixth of that. 

That's like a college student with a car worth $12K, and a huge insurance deductible of $2K, getting into a collision: it would hurt, big time.

**********************************

BTW: At this time last year, I was ballparking that the costs to China (a smaller and poorer economy) would be in the range of several hundred billion dollars. But, there are researchers in Australia who made the definitive estimates of the costs of SARS-COV-1. And 11 months ago they estimated the costs to the U.S. of COVID-19 would be $1.7T. Cutler and Summer's guesstimate is 10 times as high. Of course, when they published that estimate, the U.S. had a handful of cases in the Seattle area, and it was still a week before Rudy Gobert and the shutdown of the NBA, and everything else after that.

Visualizing Income Support Due to COVID-19

Income support means, roughly, that a government provides some replacement income to people during a time of crisis. In the case of COVID-19 this support is often large, but combines temporary and permanent measures.

All of this is being tracked through this site at Oxford. And all their data is available for free download. Ain't the internet great?

Visual Capitalist has put together an interactive graphic showing countries shaded by their level of support with the passage of time. Click through to play with it. Do press play at the bottom left to show changes through time.