If you aren’t from the Southeast, you might not know that Chattanooga is a fun city. I recommend it as a place to spend a day, with or without kids. The aquarium and Lookout Mountain attractions are fun.
The riverfront downtown area is booming (in a low height building restriction kind of way). Developers are building fancy new townhomes near the Walnut Street pedestrian bridge. The middle of the bridge offers lovely views of the river and mountains.
I noticed a sign saying that residents had “fought” to save the bridge from being demolished. Sometimes, it seems like a bad idea for residents to fight to save a historic structure. Insisting that a house built in 1890 must remain as it looked in 1890 can stifle the growth of a city. This instance seems different to me. The story of this beautiful bridge is an example of having a vision, clever city planning, and providing public goods through a mix of private and government funds.
The bridge was closed to motor vehicles in 1978. It’s not hard to imagine why a bridge built before automobiles could become unsafe for modern traffic by 1980.
The Tennessee Department of Transportation recommended demolishing the bridge, but Chattanooga’s then-Mayor Pat Rose suggested another idea: use it for pedestrians only. Rose and Ron Littlefield, AICP, the city’s Public Works Commissioner, kept the idea alive by hiring local architects … to develop a study for restoring the bridge.
Under the auspices of the not-for-profit organization Chattanooga Venture, a committee was formed to determine whether the bridge could and should be restored. Once it was determined a rehabilitated bridge could support pedestrian traffic, the local community rallied behind saving the bridge. Helping transfer the $2.5 million in federal funds originally designated for demolition to rehabilitation were former Chattanooga Mayor Gene Roberts, former U.S. Representative Marilyn Lloyd and former Sen. Al Gore. Local fundraising efforts secured the additional $2 million needed to restore the bridge.
The ice cream and coffee shop at the beginning of the bridge has a menu in English, Spanish, French, Chinese, and Russian. That’s pretty cosmopolitan for the American South. The lovely historic bridge really draws a crowd.
On Thanksgiving, we cook a bird. We eat meat. Then I make turkey soup by boiling the carcass and such. After making turkey soup, I have nagging thoughts, ‘That seemed quite economical. I have so much food now. I should make soup from scratch again soon.’
In fact, I will not make soup from scratch again until next Thanksgiving.*
Part of the reason for starting this blog is to explore my own cognitive dissonance. Is making soup from scratch economical and should I be doing it more? Right now I’m trying to work full-time and also produce food for a family 2 or 3 times every day. I want to minimize the time I spend cooking.
To start, naturally, I Googled “is soup the most economical food”.
Peasants and poor folk could get nutrients out of bones and root vegetables by making soup. Soup is economical in that sense, but I’m not talking about making broth.**
During America’s inexorable march toward processed food, chicken soup became something to buy, not something to make … and many cooks simply don’t know how satisfying a project it is.
So, they are admitting that it’s a lot of work. I do not want a “satisfying project”. I want food that is healthy and appealing; and I also want to avoid buying food from restaurants constantly.
Another article I arrived at was by Prudent Penny Pincher. The title is “60 CHEAP AND EASY FALL SOUPS”. Never trust all-caps. According to this site:
Name brand soups are about $2 per serving. Many soups can be made at home for under $1 per serving with less 30 minutes of prep/cook time.
The Prudent Penny Pincher page is little more than a list of links to other recipe sites. They wash their hands of the responsibility of telling you how to actually make soup. For research, I clicked their link for chicken soup.
What do you need to have on hand to make chicken soup in a mere 30 minute? Canned broth, for one. Making your own broth is not ‘quick and easy.’ You also need to have cooked chopped chicken and chopped vegetables.
If I have cooked chopped chicken and chopped vegetables, then I could just eat that! That’s a meal nearly finished. My guilt over not making soup from scratch regularly was completely resolved when I read that.
I had a similar revelation after I tried juicing for a week. Not counting the cost of a juicing machine, should you be juicing? If you have never once felt a pang of guilt for not juicing, then maybe you are male.
I borrowed a juicer once and I bought lots of fresh produce. I chopped fruits and veggies into chunks and juiced them. One cup of juice came out, which I drank while spending 20 minutes cleaning the yucky machine covered in pulp.
I realized that I should stop at the step where I have chopped fruits and veggies and just eat them. Fortunately, I hadn’t bought the juicer. Pity the women who juice regularly because of sunk cost bias after they bought the machine. Anyway, I concluded that juicing was expensive in terms of ingredients and time.
Through writing this, I realized that I make soup from scratch at Thanksgiving because it’s a holiday and I’m on vacation. It’s fun when you have free time.
Anyone who disagrees is welcome to comment. Am I discounting the future too much? Should I put work into making soup so that we can eat soup for days?
*There is an exception. I make delicious scallop corn chowder once a year when I am on vacation with extended family in the summer. So, that’s also when I’m not doing my professional work and an extended family member is taking care of my children.
**I do not participate in trendy “bone broth”. Do you think my son would be happy if I put bone broth on the table for dinner?
We wish you all a happy Thanksgiving day. I wondered if the academic literature could provide any insights to use on this day. If Google is a good guide, the formal economics literature has ignored the phenomenon of the Thanksgiving tradition.
“We Gather Together” from the Journal of Consumer Research in 1991 does, at the very least, exist. The first line of the abstract made me smile.
Thanksgiving Day is a collective ritual that celebrates material abundance enacted through feasting.
The third line of the abstract made me think.
So certain is material plenty for most U.S. citizens that this annual celebration is taken for granted by participants.
How do we conduct cost-benefit analysis when different policies might harm some in order to help others? This question has become increasingly important in the Year of COVID.
In particular, it is possible that some interventions to prevent the spread of COVID may save the lives of the vulnerable elderly, but have the unfortunate effect of causing other harms and potentially deaths. For example, increased social isolation could lead to increased suicides among the young (we don’t quite have good data on this yet, but it’s at least a possibility).
If you don’t think any public policies will reduce COVID deaths, then the post isn’t for you. It’s all cost, no benefit!
But for those that do recognize the trade-offs, a common way to do the cost-benefit analysis is to look at “years of life lost” or YLL. This is a common approach on Twitter and blogs, but I’ve seen it in academic papers too. In this approach, you look at the age of those that died from COVID, and use an actuarial life table to see how long they would have been expected to live. For example, an 80-year-old male is expected to live about 8 more years. Conversely, a 20-year-old males is expected to live another 56 years.
So, here’s the crude (and possibly morbid) YLL calculus: if a policy saves six 80-year-olds, but causes the death of one 20-year-old, it’s a bad policy. Too much YLL! (Net loss of 8 years of life.) However, if the policy saves eight elderly and kills just one young person, it’s a good policy. A net gain8 years of life. (Of course, we can never know these numbers with precision, but that’s the basic idea.)
But I think this approach is fundamentally flawed. Not because I oppose such a calculation (though maybe you do, especially if you are not an economist!), but because it’s using the wrong numbers. Briefly: we shouldn’t value every year of life equally.
The superior approach for this calculation is to use an approach called the “value of a statistical life” (VSL). In this approach, we assign a value to human life (the non-economists are really cringing now) based on revealed preferences of various sorts. Timothy Taylor has a nice blog post summarizing how this value can be estimated, which is much better than how I would explain it.
In short, the average VSL in the US is around $10-12 million, depending on how you calculate it. You might be skeptical of this figure (I was at first too!), but what really convinced me is that you get roughly this number when you do the calculation using very different approaches. It just keeps coming up.
So how does VSL apply to our COVID calculation? What’s really interesting about VSL is that it varies with age. And not perhaps as you might expect, as a constantly declining number. It’s actually an inverted-U shape, with the highest values in the middle of the age distribution. Young and old lives are roughly equally valued! Once we realize this, I think we can see how the YLL approach to analyzing COVID trade-offs is flawed.
Kip Viscusi has been the pioneer in establishing the VSL calculation. If you’ve heard that “a life is worth about $10 million” and scratched your head, Viscusi is the man to blame. Over the weekend, Viscusi gave his Presidential Address to the Southern Economic Association (he actually delivered it in-person at the conference in New Orleans, but to a very small crowd since the conference was over 90% virtual).
As you might have guessed given his area of research, Viscusi used this address to estimate the costs of COVID, both mortality and morbidity (the talk is partially based on this paper). He didn’t talk much about the policy trade-offs, but we can use his framework to talk about them. Here’s a very relevant slide from the presentation.
Notice here we see the inverted-U shaped VSL curve. You may not be able to read it very well, but Viscusi helps us with a bullet point: VSL at age 62 is greater than at age 20. Joseph Aldy, a frequent co-author of Viscusi, has extended the curve even further up to age 100 which you can see in this column. Aldy and Smyth use a slightly different approach, but the short version is that the VSL for a 62-year-old is much greater than a 20-year-old (roughly double). The 20-year-old VSL is roughly equal to that of an 80-year-old.
So let’s go back to the above YLL calculation, which told us that if a policy intervention only saves six 80-year-olds but results in the death of one 20-year-old, it’s bad policy. Too many YLL!
However, using the VSL calculation, this policy is actually good, since 20- and 80-year-olds have roughly equally valued lives. The policy only becomes bad if it kills more 20-year-olds than elderly folks. This may seem strange, given the short life left for the 80-year-old, but it is where the VSL calculus leads us.
I will admit, this calculations are morbid in some sense. But we live in morbid times. Death is all around us, and we need to some clear method for assessing trade-offs. YLL seems like the wrong approach to me. VSL seems better, but if we take a third approach, something like All Lives Matter (and matter equally), we end up with the same calculation when comparing a 20- and 80-year-old.
In the end, we should also be looking for policy interventions that have low costs and don’t result in additional deaths. For example, I think there is now good evidence that wearing masks slows the spread of viruses, which will lower deaths without any major costs. But if we are going to talk about trade-offs, let’s do it right.
(Final technical note: there is an approach that combines YLL and VSL, called “value of a statistical life year” [VSLY]. Viscusi discusses VSLY in the paper that I linked to above. I won’t get into the technicalities here, but suffice it to say VSLY involves more than simply adding up the years of life lost.)
How often do we hear about “data heroes”? As a data analytics teacher, this just thrills me. Bloomberg reported on the Data Heroes of Covid this week.
One of the terrible things about Covid-19 from the perspective of March 2020 was how little we knew. The disease could kill people. We knew the 34-year-old whistleblower doctor in China had died of it. We knew the disease had caused significant disruption in China and Italy. There were so many horror scenarios that seemed possible and so little data with which to make rational decisions.
The United States has government agencies tasked with collecting and sharing data on diseases. The CDC did not make a strong showing here (would they argue they need more funding?). I don’t know if “fortunately” is the right word here, but fortunately private citizens rose to the task.
The Covid Tracking Project gathers and releases data on what is actually happening with Covid and health outcomes. They clearly present the known facts and update everything as fast as possible. The scientific community and even the government relies on this data source.
Healthcare workers have correctly been saluted as heroes throughout the pandemic. The data heroes volunteering their time deserve credit, too. Lastly, I’d like to give credit to Tyler Cowen for working so hard to sift through research and deliver relevant data to the public.
This weekend I am participating (virtually, remotely) in the Southern Economics Association annual meeting where economists talk about research in progress. I saw Laura Razzolini present a new project yesterday.
She and coauthors surveyed people in the city of Birmingham, AL before and after a major disruption to commuter traffic. One thing they find is that people who have a longer commute due to a road closure are more stressed.
AS IT HAPPENED, Covid came along and started stressing people soon after. So they did another round of surveys and have great baseline data to compare Covid-stressed people with. I will not discuss her results on how stress affects decision making here. She has got some really neat results. The paper will be called something like “Uncovering the Effects of Covid-19 on Stress, Well-Being, and Economic Decision-Making”.
The magnitude of the increase in stress from a longer commute was something like 2.5 on a scale of 1-10. (Do not quote me – I do not have her paper to reference – this is from memory)
A comment from the audience was that it looked like the magnitude of the increase of stress from a longer commute and from Covid were similar. How could that be? Isn’t a deadly disease worse than traffic?
To explain this, I return to my favorite xkcd comic. When you hover your mouse over the comic, it says “Our brains have just one scale, and we resize our experiences to fit.” (Apropos of nothing, the fact that the comic artist picked Joe Biden as an example of someone who isn’t very important in 2011 seems pretty strange now.)
So, when traffic got worse people could only express “my life got worse”. And when Covid-19 caused shutdowns in the Spring of 2020, people again said “my life got worse”.
We only have one scale, and we resize our experience to fit. Thanksgiving is coming up. I would hope that we could take a day off from the 2020 year-of-doom talk and find something to be grateful for, because things actually can get worse. I also send out sincere condolences to all those who will be spending The Holidays apart from loved ones because of Covid-19.
My local Facebook community group is a treasure trove of unfiltered NIMBY and YIMBY sentiments. I’m creating a “nimby” tag for blogs I write about them.
This FB post went up last week about some proposed townhouses that would be build on what is currently an ugly empty paved area of land on the side of a highway.
There were 40 “likes” and only 5 angry face reactions. Given some of the vitriol I have seen against building previously, I was surprised at how many people reacted positively. This can’t be treated as a scientific poll, but the fact that so many people bothered to say they approve was interesting to me.
Most of the land in our city is zoned for single-family detached houses, meaning most of it looks more like what people call suburbs.
Here’s what people said in the comments:
“I like the look. I also like Chaise’s term ‘vibrancy’.”
“ I wish they weren’t going to be so tall.” (Note that they are not tall. Most of this town used to be one-story 1-bathroom ranch houses, and there is a lot of nostalgia for those tiny houses.)
“Why are we junking up our downtown with condos.” (That one got 8 likes, and someone replied “because they sell.” Isn’t it astounding that someone would call this “junking”?)
“Almost Anything built in that location is a step in the right direction.” (8 likes)
Some people complained that this is not adding “affordable housing” to our city because these units are expensive. I might post more explicit debates over affordable housing in the future.
Apparently, currently, there isn’t much opposition to developing an empty lot on the side of the highway with a few expensive units. There has been a WAR for the past year after a proposal to increase the density of housing closer to downtown. Anti-development types are angry that the city council is not doing more to block new building.
The prospective developer for this empty weed lot needs approval from the city council. Our city elections last month became rather contentious. It was, in part, a struggle between people who want to preserve curbs and doors just as they were in 1970 versus newer younger residents who are more pro-development.
In recent years, we have seen growing discontent with the distributional effects of free trade, widespread favor-seeking from businesses, and a recurring sentiment that the economy is rigged.
Distributional concerns. The 2016 election of Donald Trump likely stemmed from graphics like those below that demonstrate the geography of job losses and gains. According to this Bloomberg article, the goods-producing sector that includes manufacturing, construction, and mining lost 1.2 percent of their jobs during the Obama administration.
The notion that the economy is harmful to some and the deck is stacked against most is damaging the reputation of capitalism. According to a 2019 Gallup Poll, positive views of capitalism among young adults (ages 18-39) have declined from 66 percent in 2010 to 51 percent in 2019 and positive views of socialism are now at the same level as positive views of capitalism.
While this same group does hold a favorable view toward free enterprise, even that is down to 83 percent in 2019 from 89 percent in 2010. Individuals also view small businesses favorably but many of those local businesses are having difficulty weathering the shock of COVID-19 and that could tilt the composition of the economy toward big business which is viewed less favorably.
This constellation of public opinion is the milieu from which calls for “common-good capitalism” (focuses policies on firm and government obligations to workers) have emerged. Personally, I do not have great confidence in proposals like higher minimum wages, increased tariffs, and more that aim to address the pain that underlies these attitudes about the market economy.
At the same time, I do not have a clear path forward. The favorable views toward free-enterprise suggest individuals want others to exercise their talent and freedom in markets. That is good! At present, we do not want to kill the goose that lays the golden eggs. On the other hand, it is difficult for me to fathom how we can have a government big enough to help workers and one that creates conditions of fairness. It is wishful thinking to assume that the rent-seeking that has undermined the credibility of the market will go away if we turn to “common-good capitalism” or something else.
There is an old Jewish aphorism that, “the clever man can extricate himself from a situation into which the wise man never would have got himself in the first place.” Leadership in the United States lacked the wisdom to adhere to the limited and enumerated powers in the Constitution and Congress has long abandoned being jealous guardians of their authority that undermines checks and balances. Hopefully we are not all out of clever.
How have countries around the world fared so far in the COVID-19 pandemic? There are many ways to measure this, but two important measures are the number of deaths from the disease and economic growth.
Over the past few weeks, major economies have started releasing data for GDP in the third quarter of 2020, which gives us the opportunity to “check in” on how everyone is doing.
Here is one chart I created to try to visualize these two measures. For GDP, I calculated how much GDP was “lost” in 2020, compared with maintaining the level from the fourth quarter of 2019 (what we might call the pre-COVID times). For COVID deaths, I use officially coded deaths by each country through Nov. 15 (I know that’s not the end of Q3, but I think it’s better than using Sept. 30, as deaths have a fairly long lag from infections).
One major caution: don’t interpret this chart as one variable causing the other. It’s just a way to visualize the data (notice I didn’t try to fit a line). Also, neither measure is perfect. GDP is always imperfect, and may be especially so during these strange times. Officially coded COVID deaths aren’t perfect, though in most countries measures such as excess deaths indicate these probably understate the real death toll.
You can draw your own conclusions from this data, and also bear in mind that right now many countries in Europe and the US are seeing a major surge in deaths. We don’t know how bad it will be.
Here’s what I observe from the data. The countries that have performed the worst are the major European countries, with the very notable exception of Germany. I won’t attribute this all to policy; let’s call it a mix of policy and bad luck. Germany sits in a rough grouping with many Asian developed countries and Scandinavia (with the notable exception of Sweden, more on this later) among the countries that have weathered the crisis the best (relatively low death rates, though GDP performance varies a lot).
And then we have the United States. Oddly, the country we seem to fit closest with is… Sweden. Death rates similar to most of Western Europe, but GDP losses similar to Germany, Japan, Denmark, and even close to South Korea. (My groupings are a bit imperfect. For example, Japan and South Korea have had much lower death tolls than Germany or Denmark, but I think it is still useful.)
To many observers, this may seem strange. Sweden followed a mostly laissez-faire approach, while most US states imposed restrictions on movement and business that mirrored Western Europe. Some in the US have advocated that the US copy the approach of Sweden, even though Sweden seems to be moving away from that approach in their second wave.
Counterfactuals are hard in the social sciences. They are even harder during a public health crisis. It’s really hard to say what would have happened if the US followed the approach of Sweden, or if Sweden followed the approach of Taiwan. So I’m trying hard not to reach any firm conclusions. To me, it seems safe to say that in the US, public policy has been largely bad and ineffective (fairly harsh restrictions that didn’t do much good in the end), yet the US has (so far) fared better than much of Europe.
All of this could change. But let’s be cautious about declaring victory or defeat at this point.
Coda on Sweden Deaths
Are the officially coded COVID deaths in Sweden an accurate count? One thing we can look to is excess deaths, such as those reported by the Human Mortality Database. What we see is that Swedish COVID deaths do almost perfectly match the excess deaths (the excess over historical averages): around 6,000 deaths more than expected.
Some have suggested that the high COVID deaths for Sweden are overstated because Sweden had lower than normal deaths in recent years, particularly 2019. This has become known as the “dry tinder” theory, for example as stated in a working paper by Klein, Book, and Bjornskov (disclosure: Dan Klein was one of my professors in grad school, and is also the editor of the excellent Econ Journal Watch, where I have been published twice).
But even the Klein et al. paper only claims that “dry tinder” factor can account for 25-50% of the deaths (I have casually looked at the data, and these seems about right to me). Thus, perhaps in the chart above, we can move Sweden down a bit, bringing them closer to the Germany-Asia-Scandinavia group. Still, even with this correction, Sweden has 2.5x the death rate of Denmark (rather than 5x) and 5x the death rate of Finland (rather than 10x, as with officially coded deaths).
As with all things right now, we should reserve judgement until the pandemic is over (Sweden’s second wave looks like it could be pretty bad). The “dry tinder” factor (a term I personally dislike) is worth considering, as we all try to better understand the data on how countries have performed in this crisis.
A week ago, we described commercial loans in general, and how they differ from bonds. Companies nearly always need money to make money, and thus have to borrow money in addition to selling stock shares. Companies that are new or smaller or doing poorly or have already borrowed a lot can still get loans, but these loans typically come with stringent conditions and require paying relatively high interest. These “leveraged loans” are the loan equivalent of “junk” bonds. When a bank lends money as a “Senior Secured Loan”, this entails agreements (“covenants”) which may specify that in event of default, this loan gets paid off ahead of any other creditor, and also that some specific asset held by the company, such as a building or an oil field, will be given over to the bank.
Financial institutions like insurance companies and pension funds are hungry for “investment grade” securities like bonds rated BBB or higher. Normally, these institutions would not consider buying into the senior loan marketplace, since these instruments are not considered investment grade.
Enter “Collateralized Loan Obligations” (CLOs). With a CLO, 200 or so loans which have been made by banks and then sold off into the market are bundled together, and then the cash flow from the interest paid on these loans plus the principal paid back is repackaged into slices or “tranches”. The highest level tranches get first dibs on being paid from the overall CLO cash flow, then the lower and lower tranches. The majority of bank loans today end up being packaged into CLOs. CLOs are an example of a lucrative operation known as “securitization”: “Securitization is the process of taking an illiquid asset or group of assets and, through financial engineering, transforming it (or them) into a security” (per Investopedia).
The rate of loan defaults in recent years has been only 3-4%, and on average the recovery on a given defaulted senior secured loan has been around 80%. So the actual losses (e.g. 4% x 20%, or 0.8% net) have been quite low. The highest annual default rate in recent memory was about 10%, in the Global Financial Crisis of 2008-2009.
The theory is that, although any particular loan has a nontrivial chance of defaulting, it is unthinkable that more than say 20% of all loans would default; and even if a full 20% of the loans did default, we would expect that the actual losses after liquidating the pledged collateral would be more like 4% of the entire loan portfolio (i.e. 20% defaults x 20% loss per default). This means that the top 95% or so of CLO cash flow should be considered very secure, and the top 60-70% are utterly secure.
Thus, the top 60-65% of the CLO cash flow is packaged as super secure, relatively low-yielding AAA rated debt, and as such is bought up by conservative financial institutions, including banks. This arrangement keeps those institutions happy, and also facilitates the making of loans to the needy companies who are taking out the underlying loans.
The figure below from an Eagle Point Investment Company presentation depicts typical CLO tranches:
The lower the position in the CLO cash flow “waterfall”, the higher the yield and the higher the risk of non-payment. The AA, A, and BBB debt tranches are all considered investment grade, though with higher risk and higher yields than the AAA tranche. The Eagle Point Investment Company happens to buy into the BB-rated debt tranche, which is just below investment grade. You, the public, can buy shares Eagle Point Investment (stock symbol EIC). These shares pay about 7% yield, after hefty management fees have been subtracted.
The equity tranche lies at the very bottom of the CLO heap. If there were, say, 20% loan defaults with only 50% recovery of the loans, the equity tranche might get completely wiped out. So these are more risky investments. As usual, there is high reward along with the risk. Oxford Lane Capital (OXLC) deals in CLO equity, and it will pay you about 15% per year, which is huge in today’s low-interest world. But….you need to be prepared to have the stock value cut in half every ten years or so, whenever there is a big hiccup in the financial world.
Anyone who was an economics-savvy adult during the GFC should be asking, “But, but, but…aren’t these CLOs essentially the same thing as the collateralized debt obligations (CDOs) that blew up the world in 2008?” The answer is partly yes, in that in both cases a bunch of loans get bundled together and then resliced into tranches. That said, we hope that the underlying loans in today’s CLOs are more robust than the massively shady home mortgage loans of 2003-2008 that fed into those CDOs. Back then, unscrupulous banks and mortgage companies handed out thousands of housing loans to ill-informed private individuals who did not remotely qualify for them, and then the banks dumped these loans out into the broader financial markets via CDOs. The bank loans behind today’s CLOs are more sober, serious, vetted affairs than those ridiculous subprime home mortgages.
This past summer, in the thick of the Covid shutdowns which have stressed small businesses, The Atlantic published a dire assessment of the potential for CLOs to sink the system, with the catchy title “The Looming Bank Collapse “. The article noted, fairly enough, that there has been a trend in the past few years to weaken the covenants on loans which would normally protect the lender against losses. Most loans these days are considered “covenant-lite”, compared to several years ago. There is genuine concern that the recovery on these loans might be more like 40-50%, instead of the historic 70-80%. On the other hand, the looser requirements on these loans may mean that fewer of them will technically violate these looser covenants and thus fewer companies will actually default. A recent survey estimates that the default rate in the $ 1.2 trillion dollar leveraged loan universe will peak at only 6.6% in 2021.
Also, today’s CLOs seem to be rated by the major ratings agencies more responsibly than the notoriously optimistic ratings given to CDO’s back in 2008. “CLOs are usually rated by two of the three major ratings agencies and impose a series of covenant tests on collateral managers, including minimum rating, industry diversification, and maximum default basket”, according to an article by S&P Global Market Intelligence. That article has a good description of CLOs, including a brief tutorial video on the nuts and bolts of how they work.