Last Friday the Supreme Court overturned the doctrine of Chevron deference as part of its ruling in Loper Bright Enterprises v Raimondo. This might not have even been their most discussed ruling of the past week, but in my (non-lawyerly) opinion, there is a good chance it will be their most economically impactful ruling of the past decade. SCOTUSblog explains the basics:
the Supreme Court on Friday cut back sharply on the power of federal agencies to interpret the laws they administer and ruled that courts should rely on their own interpretation of ambiguous laws. The decision will likely have far-reaching effects across the country, from environmental regulation to healthcare costs.
By a vote of 6-3, the justices overruled their landmark 1984 decision in Chevron v. Natural Resources Defense Council, which gave rise to the doctrine known as the Chevron doctrine. Under that doctrine, if Congress has not directly addressed the question at the center of a dispute, a court was required to uphold the agency’s interpretation of the statute as long as it was reasonable. But in a 35-page ruling by Chief Justice John Roberts, the justices rejected that doctrine, calling it “fundamentally misguided.”
Justice Elena Kagan dissented, in an opinion joined by Justices Sonia Sotomayor and Ketanji Brown Jackson. Kagan predicted that Friday’s ruling “will cause a massive shock to the legal system.”
When the Supreme Court first issued its decision in the Chevron case more than 40 years ago, the decision was not necessarily regarded as a particularly consequential one. But in the years since then, it became one of the most important rulings on federal administrative law, cited by federal courts more than 18,000 times.
The most common reaction I’ve seen is that people expect this to reduce the power of executive branch agencies, both in general and relative to courts and businesses, likely resulting in deregulation. Thus those on the economic left have been mostly decrying the decisions, while free–marketers and businesspeoplehave mostly beencelebrating:
We all know about inflation. One popular measure is the Consumer Price Index (CPI), which measures the change in price of a fixed basket of goods. The other popular measure used for inflation is the Personal Consumption Expenditures (PCE) price index. This index measures the price of what consumers actually purchase and captures the effects of consumers changing their consumption bundles over time. While the latter is a better measure for the prices at which consumers make purchases, it takes longer to calculate. In practice, the earlier CPI release gives a pretty accurate preview to the PCE price index.
While consumption is a substantial two-thirds of total expenditures in the US economy, other prices definitely matter. On average, a third of our income is spent on other things. Below is a stacked bar chart of quarterly GDP components – the classic Y=C+I+G+NX.* Investment spending composes a relatively stable 16.7% and Government spending composes about 16.5% of GDP. We almost never hear much about the price of these other things.
The Fed has now almost landed the plane, bringing us down from 9% inflation during the Covid era to something approaching their 2% target today. But it is not yet clear how hard the landing will be. Back in March I thought recurrent inflation was still the big risk; now I see the risk of inflation and recession as balanced. This is because inflation risks are slightly down, while recession risk is up.
Inflation remains somewhat above target: over the last year it was 3.3% using CPI, 2.7% by PCE, and 2.8% by core PCE. It is predicted to stay slightly above target: Kalshi estimates CPI will finish the year up 2.9%; the TIPS spread implies 2.2% average inflation over the next 5 years; the Fed’s own projections say that PCE will finish the year up 2.6%, not falling to 2.0% until 2026. The labels on Kalshi imply that markets are starting to think the Fed’s real target isn’t 2.0%, but instead 2.0-2.9%:
The Fed’s own projections suggest this to be the somewhat the case- they plan to start cutting over a year before they expect inflation to hit 2.0%, though they still expect a long run rate of 2.0%. In short, I think there is a strong “risk” that inflation stays a bit elevated the next year or two, but the risk that it goes back over 4% is low and falling. M2 is basically flat over the last year, though still above the pre-Covid trend. PPI is also flat. The further we get from the big price hikes of ’21-’22 with no more signs of acceleration, the better.
But I would no longer say the labor market is “quite tight”. Payrolls remain strong but unemployment is up to 4.0%. This is still low in absolute terms, but it’s the highest since January 2022, and the increase is close to triggering the Sahm rule (which would predict a recession). Prime-age EPOP remains strong though. The yield curve remains inverted, which is supposed to predict recessions, but it has been inverted for so long now without one that the rule may no longer hold.
Looking through this data I think the Fed is close to on target, though if I had to pick I’d say the bigger risk is still that things are too hot/inflationary given the state of fiscal policy. But things are getting close enough to balanced that it will be easy for anyone to find data to argue for the side that they prefer based on their temperament or politics.
To me the big wild card is the stock market. The S&P500 is up 25% over the past year, driven by the AI boom, and to some extent it pulls the economy along with it. The Conference Board’s leading economic indicators are negative but improving overall this year; recently their financial indicators are flat while non-financial indicators are worsening.
Overall things remind me a lot of the late ’90s: the real economy running a bit hot with inflation around 3% and unemployment around 4%; the Fed Funds rate around 5%; and a booming stock market driven by new computing technologies. Naturally I wonder if things will end the same way: irrational exuberance in the stock market giving way to a tech-driven stock market crash, which in turn pushes the real economy into a mild recession.
Of course there is no reason this AI boom has to end the same way as the late-90’s internet boom/bubble. There are certainly differences: the Federal government is running a big deficit instead of a surplus; there are barely a tenth as many companies doing IPOs; many unprofitable tech stocks already got shaken out in 2022, while the big AI stocks are soaring on real profits today, not just expectations. Still, to the extent that there are any rules in predicting stock crashes, the signs are worrying. Today’s Shiller CAPE is below only the internet and Covid meme-stock bubble peaks:
Again, this doesn’t mean that stocks have to crash, or especially that they have to do it soon; the CAPE reached current levels in early 1998, but then stocks kept booming for almost two years. I’m not short the market. But the macro risk it poses is real.
First, here is an updated chart on average wealth by generation, which gives us the first glimpse at 2024 data:
I won’t go into too much detail explaining the chart here, as I have done that in more detail in pastposts. But one brief explanatory note: I’m now labeling the most recent generation “Millennials & Gen Z (18+).” Because of the nature of the data from the Fed’s DFA, I can’t separate these two generations (it can be done with the Fed SCF data, but that is now 2 years old). This combined generation now includes everyone from ages 18 to 43 (which means that technically the median age is 30.5, not quite 31 yet), somewhere around 116 million people, which makes it a bit of a weird “generation,” but you work with the data you have. Note though that this makes the case even harder for young Americans to be doing well, as every year I am adding about 400,000 people to the denominator of the calculation, even though 18-year-olds don’t have much wealth.
What’s notable about the data is just how much the youngest “generation” in the chart has jumped up in recent years. They have now have about double the wealth that Gen X had at roughly the same age. Average wealth is about as much as Gen X and Boomers had 5-6 years later in life — and while there are no guarantees, odds are Millennial/Gen Z wealth will be much, much higher in another 5-6 years. You may notice at the tail end of the chart that Gen X and Boomers now have roughly equal amounts of average wealth at the same age (Gen X’s current age), while 2 years ago they were $100,000 ahead. I suspect this is just temporary, and Gen X will soon be ahead again, but we shall see.
Of course, the most common complaint about my data is that these are just averages, so they don’t tell us a lot about the distribution of wealth and could be impacted by outliers. That’s why I’m really excited to share this new data on wealth by decile from the 2022 Fed SCF survey. This data was put together by Rob J. Gruijters and co-authors, and it allows us to compare the wealth of Boomers, Gen X, and Millennials across the wealth distribution. You should read their analysis of the data, but in this post I’ll give my slightly different (and optimistic) interpretation of it.
For all three generations, wealth in the bottom 10% is negative when that generation is in their 30s. And for Millennials, it is the most negative: -$65,000 compared to -$30,000 for Gen X and -$17,000 for Boomers in the bottom decile (as always, the figures are adjusted for inflation). While I haven’t dug into the data, my suspicion is that student debt is driving a lot of the increase. Since this is households in their 30s, I suspect a lot of the bottom decile is composed of folks that just finished graduate and professional school, and are only now starting to acquire assets and pay down debt — they have very high earning potential, which means over their lifetime they will do great, but they are starting from behind. Again, we’ll have to wait and see, but I suspect many in the bottom will quickly climb up the wealth distribution over their working years.
That being said, in the following chart I have left off the bottom 10% for each generation, since displaying negative wealth would make the chart look a little weird. But this chart shows a very optimistic result: Millennials are doing better than Boomers across the distribution, and Millennials are ahead of almost all deciles for Gen X except a few, where they are essentially equal to Gen X (2nd, 7th, and 8th deciles).
The chart may be a little confusing (give me your suggestions to improve it!), but here’s how to read it. The blue bars show Millennial wealth relative to Gen X, at the same age, for each decile (excluding the bottom 10%). For example, the first bar shows that Millennials in the 2nd wealth decile had 100% of the wealth that Gen Xers in the 2nd wealth decile had at the same age — in other words, they were equal. The orange bars show Millennial wealth relative to Baby Boomer wealth at the same age, in the same decile (to repeat, it’s all adjusted for inflation).
Notice that other than the very first bar (Millennials vs. Gen X in the 2nd wealth decile), all of the other bars are over 100%, indicating that Millennials have more wealth than the two prior generations for almost every decile. For some of these, they are much, much greater than 100%. In the 5th decile (close to the median), Millennials have over 50% more wealth than Gen X and almost 200% (double the wealth) of the wealth of Boomers. That’s a massive increase!
A pessimistic read of the chart is that the biggest gains went to the top 10%. Though notice that’s only true relative to Baby Boomers. When compared with Gen X, the 4th and 5th deciles did better than the top 10% in terms of relative improvement. To relate this to the earlier chart in this post, it suggests that relative to Boomers, outliers at the top end might be skewing the average a bit, but that’s probably not the case relative to Gen X. And again, the broad-based gains are visible throughout the distribution from the 2nd decile on up.
Finally, on social media I’ve got several objections about the chart, such as folks not liking the log scale y-axis, and preferring the CPI-U for inflation adjustments instead of the PCEPI that I use. For those objectors, here is a different version of the chart:
When I first started reading of “Lender-on-Lender Violence” this year, images of bankers in three-piece suits brawling in the streets of Lower Manhattan came to mind. It turns out that this is a staid legal term for a practice which has been around for some time, but is becoming more common and consequential.
Consider a case where say three lenders (e.g. banks or more likely venture capital funds) have lent money to some startup or struggling company XYZ. Let’s call these lenders A, B, and C. Now XYZ needs even more funding, perhaps because they need to build another factory, or perhaps because things are not working out as they hoped and they cannot pay off the original loans and still stay in business.
Now Lenders A and B get together and cook up a scheme. They will lend some more money to company XYZ to largely replace the original loan, but they contrive to get legal terms for that new loan that give it a higher priority for payment than the original loan. This is called “up-tiering” the new loan. This has the effect of reducing the market value of the original loan.
Lender C is now hosed. It faces murky prospects for repayment on that original loan. Lenders A and B offer to buy them out of the original loan for 40 cents on the dollar. Lender C proceeds to sue Lenders A and B.
Will Lender C prevail? Probably not, if the course of recent cases is any guide. Unless there is very specific language in the legal “covenant” regarding the first loan forbidding this practice, it seems to be legal.
A similar maneuver would be for a new Lender D to offer a replacement loan to Company XYZ, with legal language giving it priority over the original loan. This is called “priming.”
Yet another tactic by the aggressive lenders includes working with Company XYZ to move its more valuable assets into a subsidiary or shell company, and to get the new loan to hold that as collateral. This again hoses the “victim” lenders, since again the assurance that they will be repaid has gone down.
My Personal Experience with Lender-on-Lender Violence
Some years ago, I bought the bonds of a company called SeaDrill. I bought the bonds instead of the common or preferred stock, for an additional margin of safety. Unlike the stock, the bonds must be repaid in full, right? Both the bonds and the preferreds were paying about 9%, back when general interest rates were much lower than that are now. So, I was a lender to the company.
Silly me. Times got tough in the oil patch, and the company would have had difficulty paying off its bonds AND paying its management their high salaries. So, they went for Chapter 11 bankruptcy. I had not realized the difference between Chapter 7 bankruptcy, where the company shuts down and liquidates and pays off its creditors in pecking order, and Chapter 11, which is largely a chance for the company to put the losses on its creditors and to keep on operating.
As with the example above, some big institution offered to refinance things with new secured bonds that had priority ahead of the old bonds (which I held). In the end I got about 44 cents on the dollar for my bonds. I was not happy about that, but I did make out better than the hapless preferred stockholders, who got just a tiny crumb to make them go away. It was a learning experience. I did feel, well, violated.
Implications for the Burgeoning Private Credit Market
I will be writing more on the booming “private credit” market. Many of the loans in this space are “covenant-lite.” Back before say 2008, a large fraction of loans to business were through banks, who would insist on strong legal protection for their money. But in recent years, private equity funds have competed for this lending, allowing the borrowers to borrow on terms that give much less protection to the lenders. Cov-lite is now the norm.
Traditionally, loans (as distinct from bonds) to businesses have enjoyed decent recoveries (e.g., around 70%) in case of defaults, thanks to strong collateral backing the loans. But if we face any sort of prolonged recession and elevated defaults, the recoveries on all these loans will be far less than in the past. These are uncharted waters.
This week, I’m doing some review for a macro-related project. In economics, the concepts of real and nominal rigidities help explain why prices and wages do not always adjust quickly in response to shocks. These rigidities create frictions that affect how markets function. A well-known rigidity is downward nominal wage rigidity (I have an experimental paper on that).
“Nominal rigidities” refer to the stickiness of prices and wages in their nominal (monetary) terms. These rigidities prevent immediate adjustment of prices and wages to changes in the overall economic environment.
Examples of Nominal Rigidities
Menu Costs: The costs associated with changing prices, such as reprinting menus or reprogramming point-of-sale systems. For instance, a restaurant might avoid changing its menu prices frequently because of the costs involved in printing new menus and the risk of confusing or losing customers.
Nominal Wage Contracts: Many workers are employed under contracts that fix their wages for a certain period, such as a year. This means that even if the demand for labor changes, wages cannot adjust immediately. For example, a factory might have a one-year wage contract with its workers, preventing it from lowering wages even during a downturn.
Price Stickiness Due to Psychological Factors: Prices may remain rigid because businesses fear that frequent changes might upset customers or erode their trust. A classic example is a retail store keeping prices stable to maintain a reputation for reliability, even when costs fluctuate.
Side note: Lars Christensen predicts less nominal rigidity in our future. Menu costs are getting smaller and customers could become accustomed to, for example, watching the price of milk fluctuate in real time in response to statements by the Fed. Click here for related Twitter joke.
African Americans have seen much adversity throughout US history, but also significant economic progress. One way to measure economic progress is by looking at wealth. There is a fantastic paper by Derenoncourt and co-authors recently published titled “Wealth of Two Nations: The U.S. Racial Wealth Gap, 1860–2020” which puts together the best historical data on Black and White wealth in the US.
The paper primarily focuses on wealth inequality, and here it paints a pessimistic picture since 1950: while the racial wealth gap was closing up until 1950, it stalled after that, and possibly got worse after the 1980s. But using that same data, we can focus on the growth of Black wealth, and here the results are quite optimistic: inflation-adjusted Black wealth per capita was about 7 times larger in 2019 than it was in 1950. Black wealth per capita has roughly doubled since from about 1992 to 2019 (inflation adjusted).
Here’s the long-run data in a chart, which shows that in 2019 Black wealth per capita was 86 times greater than in 1870 (inflation adjusted). That’s some real economic progress!
For income data, there is no long-run historical series similar to the wealth series that I am aware of, but there are estimates for particular years. For example, Robert Margo estimates that Black income per capita was about $1,500 in 1870 (inflation adjusted to 2022 dollars) and $2,400 in 1900 (once again, inflation adjusted to 2022). Margo says that these data should be comparable to the Census CPS Historical Income tables, and the 2022 estimate from this series is $31,180 for Blacks. This data suggests that Black per capita income is 21 times was it was in 1870, and about 3 times what it was in 1967 (first year in the Census CPS series).
Using the same census data for families, rather than individuals, we can also look at the growth of Black family income since 1967. This data suggests that both median and mean family income for Blacks roughly doubled in inflation-adjusted terms from 1967 to 2022, which isn’t as impressive as the tripling of per capita income, but keep in mind that families are smaller today than in 1967. When we look at the distribution of those incomes, the progress becomes very clear:
In 1967, half of Black families had incomes under $35,000 (in 2022 inflation-adjusted dollars), which is close to the official definition of poverty (depending on family size). By 2022, this had been cut in half: just 25 percent of Black families were under $35,000.
The number of “rich” Black families (incomes of at least $100,000) in 1967 was miniscule: only about 200,000 families, just 5 percent of the total. In 2022, there were an additional 3 million rich Black families, now comprising almost one-third of the total, and outnumbering poor Black families. The number of rich Black families has grown by about 1 million in just the past decade — no stagnation there! The Black “middle class” (incomes between $35,000 and $100,000) now has 4.5 million families — the same number as the total count of Black families in 1967.
Of course, there is still much work to be done on economic progress in the US. But the astonishing economic progress of Blacks since emancipation and since the Civil Rights era is worth celebrating, even if racial gaps haven’t closed much recently.
Economics as a discipline really likes to boil things down to their essentials. There are plenty of examples. How many goods can one consume? Just two, bread and not bread. How can you spend your time? You can labor or leisure. How do you spend your money? Consume or save. It’s this last one that I want to emphasize here.
First, all income ultimately ends up being spent on consumption. Saving today is just the decision to consume in the future. And if not by you, then by your heirs. One determinant of inter-temporal consumption decisions is the real rate of return. That is, how many apples can you eat in the future by forgoing an apple eaten today? The bigger that number is, the more attractive the decision to save.
Further, since most saving is not in the form of cash and is instead invested in productive assets, we can also characterize the intertemporal consumption problem as the current budget allocation decision to consume or invest. The more attractive capital becomes, the more one is willing to invest rather than consume. The relative attractiveness between consumption and investment informs the consumption decision.
How attractive is investment? I’ll illustrate in two graphs. First, if the price of investment goods falls relative to consumption goods, then individuals will invest more. The graph below charts the price ratio of investment goods to consumption goods. Relative to consumption, the price of investment has fallen since 1980. Saving for the future has never been cheaper!
Of course, as in a price taker story, I am assuming that individuals don’t affect this price ratio. Truly, prices are endogenous to consumption/investment decisions. For all we know, it may be that the prices of investment goods are falling because demand for investment goods has fallen. But that doesn’t appear to be the case.
I’m back from Manifest, a conference on prediction markets, forecasting, and the future. It was an incredible chance to hear from many of my favorite writers on the internet, along with the CEOs of most major prediction markets; in Steve Hsu’s words, Woodstock for Nerds. Some highlights:
Robin Hanson took over my session on academic research on prediction markets (in a good way; once he was there everyone just wanted to ask him questions). He thinks the biggest current question for the field is to figure out why is the demand for prediction markets so low. What are the different types of demand, and which is most likely to scale? In a different talk, Robin says that we need to either turn the ship of world culture, or get off in lifeboats, before falling fertility in a global monoculture wrecks it.
Play-money prediction markets were surprisingly effective relative to real-money ones in the 2022 midterms. Stephen Grugett, co-founder of Manifold (the play-money prediction market that put on the conference), admitted that success in one election could simply be a coincidence. He himself was surprised by how well they did in the 2022 midterms, and said he lost a bunch of mana on bets assuming that Polymarket was more accurate.
Substack CEO Chris Best: No one wants to pay money for internet writing in the abstract, but everyone wants to pay their favorite writer. For me, that was Scott Alexander. We are trying to copy Twitter a bit. Wants to move into improving scientific publishing. I asked about the prospects of ending the feud with Elon; Best says Substack links aren’t treated much worse than any other links on X anymore.
Razib Khan explained the strings he had to pull for his son to be the first to get a whole genome sequence in utero back in 2014- ask the hospital to do a regular genetic test, ask them for the sample, get a journalist to tweet at them when they say no, get his PI’s lab to run the sample. He thinks crispr companies could be at the nadir of the hype cycle (good time to invest?).
Kalshi cofounder Luana Lopes Lara says they are considering paying interest on long term markets, and offering margin. There is enough money in it now that their top 10 or so traders are full time (earning enough that they don’t need a job). The CFTC has approved everything we send them except for once (elections). We don’t think their current rule banning contest markets will go through, but if it does we would have to take down Oscar and Grammy markets. When we get tired of the CFTC, we joke that we should self certify shallot futures markets (toeing the line of the forbidden onion futures). Planning to expand to Europe via brokerages. Added bounty program to find rules problems. Launching 30-50 markets per week now (seems like a good opportunity, these can’t all be efficient right?).
There was lots else of interest, but to keep things short I’ll just say it was way more fun and informative doing yet another academic conference, where I’ve hit diminishing returns. More highlights from Theo Jaffee here; I also loved economist Scott Sumner’s take on a similar conference at the same venue in Berkeley:
If you spend a fair bit of time surrounded by people in this sector, you begin to think that San Francisco is the only city that matters; everywhere else is just a backwater. There’s a sense that the world we live in today will soon come to an end, replaced by either a better world or human extinction. It’s the Bay Area’s world, we just live in it.
In my Inbox I read the following sentence, summarizing an article on child health in Arkansas: “The latest Annie E. Casey Foundation KIDS COUNT Data Book shows 2022 was the deadliest year on record for child deaths in Arkansas.”
Deadliest on record! That certainly grabbed my attention. I clicked the link and read the article. Indeed, they emphasize three times that 2022 was the “deadliest year” for kids in Arkansas, including with a chart! And the chart does seem to support the claim: in 2022 there were 44 child and teen deaths per 100,000 in Arkansas, higher than any year on the chart.
But wait a minute, this chart only goes back to 2010. Surely the record goes back further than that? Indeed it does. It took me three minutes (yes, I timed myself, and you have to use 4 different databases) to complete the necessary queries from CDC WONDER to extract the data to replicate their 2010-2022 chart, and to extend the data back a lot further: all the way to 1968 (though in 30 seconds I could have extended it back to 1999).
And what do we find in 1968? The death rate for children and teens in Arkansas was twice as high as it was in 2022. Not just a little higher, but double. With some more digging, I might be able to go back further than 1968, but from the easily accessible CDC data, that’s as far back as “the record” goes. Of course, I knew where to look, but I would hope that a group producing a data book on child health also knows where to look. And you don’t need to extend this very far past the arbitrary 2010 cutoff in the article quoted: 2008 and every year before it was more deadly than 2022 for children in Arkansas. Here’s a chart showing the good long-run trend:
Now there is a notable flattening of the long-run trend in the past 15 years or so, and a big reversal since 2019. What could be causing this? The article I read doesn’t get specific, but here’s what they say: “The state data isn’t broken out into cause of death, but firearm-related deaths have become the leading cause of death among U.S. teens in recent years. Deaths from accidents such as car crashes account for most child deaths.”
But using CDC WONDER, we can easily check on what is causing the increase since 2019. “Firearm-related deaths” is an interesting phrase, since it lumps together three very different kinds of deaths: homicides, suicides, and accidents. And while it is true that “deaths from accidents” are the leading category of deaths for children, this also lumps together many different kinds of deaths: not only car crashes, but also poisonings, drownings, or accidental firearm deaths.
For Arkansas in 2022, here are the leading categories of deaths for children and teens (ages 1-19) if we break down the categories a bit:
Homicides: 66
Non-transport accidents: 58 (largest subcategories: poisonings/ODs and drowning)
Transport accidents: 52 (almost all car crashes)
Suicides: 24
Birth defects: 16
Cancers: 14
Cardiovascular diseases: 13
And no other categories are reported, because CDC WONDER won’t show you anything smaller than 10 deaths.
We might also ask what caused the increase since 2019, especially since this a report on child health and possible solutions. The death rate increased by 9 deaths per 100,000, and over 80% of the increase is accounted for by just two categories: homicides and non-transport accidents. Car crashes actually fell slightly (though the rate increased a bit, since the denominator was also smaller). Deaths from suicides, cancer, and heart diseases also declined from 2019 to 2022 among children in Arkansas, and these are the three on the list above that we would probably consider the “health” categories. Things actually got better!
But the really big increase, and very bad social trend, is the category of homicides. Among children and teens in Arkansas, it rose from 35 deaths in 2019 to 66 deaths in 2022. It almost doubled. That’s bad! But homicides are not mentioned anywhere in the article on this topic that I read (“firearm-related deaths” is the closest they get). And while car accidents are definitely a major problem, they didn’t really increase from 2019 to 2022 (among kids in Arkansas).
One more thing we can do with CDC WONDER is break down the homicides by age. The numbers so far are looking at a very broad range of children and teens, from ages 1-19. As I’ve written about before, the is a huge difference between homicide rates for older teens versus all of the kids. Indeed for Arkansas we see the same pattern, such as when I run a CDC WONDER query for single-years of age: only the ages 17, 18, and 19 show up (remember, anything less than 10 deaths won’t register in the query).
Breaking it down by five-year age groups, we see that 53 of the 66 homicides (in Arkansas among kids and teens) were for ages 15-19, that is 80% of the total. And further if we run the query by race, we see that 40 of the 66 homicides were for African Americans age 15-19. This is clearly a social problem, but it’s an extremely concentrated social problem. And the increase for older teen Blacks has been large too: it was just 17 deaths in 2019, more than doubling to 40 homicides in 2022.
Now, small numbers can jump around a bit, so just looking at 2019 and 2022 might be deceptive. What if we had a longer annual series to look at? Again, CDC WONDER allows us to do this. Here is the chart for homicides among older Black teens in Arkansas:
This is a dramatic chart. The steady rise in homicides among this demographic since 2019 is staggering. Not only the dramatic increase, but notice that 2021 and 2022 are much worse than the crime wave of the early 1990s, which also jump out in this chart. The homicide rate for older Black teens in 2022 was almost 50 percent higher than 1995, the prior worst year on record.
So is there a problem with child and teen deaths in Arkansas? Yes! But with just a few minutes of searching on CDC WONDER, I think we can get a much better picture of what is causing it than the article I read summarizing the report. Indeed, if we read the full national report, the word “homicide” is only mentioned once in a laundry list of many causes of death.
The most important part of addressing a social problem, such as “deadliest year on record for child deaths in Arkansas” is to know some basic details about what is causing a bad social indicator to worsen. Hopefully after reading this blog post you know a little bit more. If you want to read my summary of the research on how to reduce deaths from firearms, see this June 2022 post.
Economist Writing Every Day 