From a new working paper “The Price of Housing in the United States, 1890-2006” by Ronan C. Lyons, Allison Shertzer, Rowena Gray & David N. Agorastos (emphasis added):
“Zoning was adopted by almost every city in our sample during the 1920s. We see a slightly steeper gradient over the next two periods (coefficients of .48 and .29, respectively). In these periods it is possible both that the existing zoning regimes were causing higher price growth and that home price appreciation was incentivizing cities to adopt even more restrictive measures, particularly by the 1970s (Fischel, 2015; Molloy et al., 2020). The gradient in the final period (1980-2006) is even steeper, however (coefficient of .67), suggesting a closer relationship between zoning and home price appreciation towards the end of the 20th century.”
Back in January I encouraged you to follow the money in the Presidential race, by which I meant follow the betting markets. I suggested this was a good way to cut through the sometimes inaccuracy of polls, and the uncertainty of listening to any one expert or group of experts. Bettors in prediction markets can take all of these into account.
Lately of course the big question in the Presidential race is whether Biden will actually be the Democratic nominee. There is much uncertainty right now, and you will all kinds of predictions from experts, media quoting “inside sources,” and other such rumors. How are you, as a relatively uninformed outsider, supposed to know who to trust?
The answer again I will suggest is: watch the betting markets. And if you check the betting markets today (aggregated across multiple markets by EletionBettingOdds.com), you will see that Biden and Kamala Harris have roughly equal chances of becoming the next President (and Trump is about a 60% favorite):
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:
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.
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.
While many data watchers eagerly anticipate the monthly jobs report coming out this Friday, today the Bureau of Labor Statistics released another set of jobs data, and arguably a much better and more complete set of jobs data for 2023. It’s called the Quarterly Census of Employment and Wages, and I have written about this data before.
The QCEW data is better because, as the name implies, it is a census of employment, rather than just a survey, meaning it is an attempt to measure the universe of employment (or at least, the universe of employment covered by unemployment insurance, which is something like 95% of the workforce). Surveys are nice, because they can provide us more timely information — notice that the QCEW is 5-6 months out of date. It is also useful to have this complete data to check on the monthly data and see if it was mostly accurate — indeed, the data is updated through a process called “benchmarking” on a regular basis.
What do the latest QCEW show us? The headline number is that total employment grew by 2.3 million jobs from December 2022 to December 2023, which is 1.5% job growth (if we use annual averages, growth is a little stronger at 2%). That’s a healthy rate of job growth, but it’s less than the familiar Nonfarm Payroll series (CES) shows from December to December: about 3 million jobs added, or a growth rate of 1.8% If we focus just on private-sector employment, we see again that the monthly series is running faster than the more comprehensive QCEW: 2.3 million jobs in the monthly report added versus 1.7 million.
Does all this mean that the monthly jobs numbers are “fake”? Of course not. Surveys will always be imperfect, but they are still useful. But it does mean that you might want to discount them by about 25 percent.
Many people have nostalgia for nominal prices of the past. I’ve written about this topic in various contexts before, but the primary error in doing this is that you must also look at nominal wages from the past. Prices in isolation give us little context of how affordable they were.
One area with a lot of nostalgia is food prices of the past, specifically grocery prices (I’ve also written about fast food prices). While I have addressed grocery price inflation since 2021 in another post (it’s bad, but probably not as bad as social media leads you to believe), there is another version of grocery price nostalgia that goes back even further. For example, this image shows up on social media frequently with nostalgia for 1980 prices:
(Note that the image also mentions housing prices, but the clear focus of the image is on groceries. I won’t dig into housing in this post, but it’s something I have written a lot about before, and I would recommend you start with this post on housing prices from February 2024. But she sure looks happy! As models often do in promotional photos.)
Could you buy all those groceries for $20 in 1980? And how should we think about comparing that to grocery prices today?
One approach to grocery affordability is to look at how much a family spends as a share of their budget on food and other items. In the past I’ve used this approach to show that food spending has fallen dramatically over time as a share of a household’s budget, including since the early 1980s. But perhaps that approach is flawed. Maybe housing has got more expensive, so families are cutting back on food spending to accommodate for that fact, but they are getting less or lower quality food.
For another approach, I will use Average Price Data for grocery items from the BLS CPI series. Note that I am using actual average retail price data, not prices series data, which means there are not adjustments for quality changes or substitutions. No funny stuff, just the raw price data (the only adjustment is if product sizes changes, which of course we want them to do, so we aren’t fooled by shrinkflation — so BLS uses a constant package size, such as 1 pound for many items or a dozen eggs, etc.).
The items I have chosen out of the 150-plus price series are the 24 items which are available in both 1980 and 2024. There may be some biases by doing this, but in general BLS is continuing to collect data on things that people continue buying. So it’s the best apples-to-apples comparison we can do (note that there are no apples in this list! Apples are tracked in the CPI, but there is no continuous price series from 1980 to 2024 for one apple variety).
How best to compare prices over time? Rather than “adjusting for inflation,” as is common in the popular press and by some economists, a better approach that I and other economists use is called “time prices.” Time prices show the number of hours or minutes it would take to purchase the good in two different years, using some measure of wages or income (I will use both average and median wages in this post). By looking at prices compared with wages for individual items, we can see whether each items as well as the entire basket has become more or less affordable.
Here is what time prices for these 24 items look like if we use average wages (I use a series that covers about 80% of the workforce, but excludes supervisors and managers). For this chart, I use prices in April 1980 and April 2024, since there is some seasonality to some prices (and April 2024 is the most recent price and wage data available, so it’s as current as I can get).
The chart shows that for 23 out of the 24 items, it takes fewer minutes of work to buy the items in April 2024 than it did in April 1980. For many items, it is a huge decrease: 13 items decrease by 30 percent or more (30 percent is also the average decrease). And while we once again might be concerned by selection bias of the goods, we have a nice variety here of proteins, grains, baking items, vegetables, fruits, snacks, and drinks. Unfortunately for the bacon lovers out there it is the one product going in the other direction, but there are still a variety of other proteins that have become much more affordable (pork chops are much cheaper!).
Here’s one way in which the image of the lady shopping wasn’t wrong: you could get a basket of groceries for about $20 in 1980. The basket I’ve put together (which is obviously different from the woman’s basket, but you work with the data you have) would cost $27 if you bought the package sizes BLS tracks (e.g., one pound for most of the meats and produce). In 2024, that same basket would cost $84. That’s 3 times as much! But since wages are over 4 times higher, the family is better off and groceries are, in a real sense, more affordable.
Speaking of wages though, is my chart perhaps biased because I’m using the average wage? What if we used another measure, such as the median wage? For that, I can use the EPI’s median wage series (which comes from the CPS), and I also converted it to a nominal wage for 2023. This wage data is only available annually, with the most recent being 2023, so I will also use 2023 price data for this chart (note: for oranges and strawberries, I use the second quarter average price, since they weren’t available year round in 1980 — another subtle example of growing abundance and prosperity today).
The immediate thing you will notice is that there isn’t much difference between the average wage chart. Bacon is still less affordable. We know have oranges being slightly less affordable and strawberries being basically the same, though keep in mind as I mentioned above the chart that these weren’t available year-round in 1980.
But other than bacon and those seasonal fruits, everything is more affordable in 2023 than 1980. The average decrease is the same as the prior chart: 30 percent fewer minutes of work at the median wage to purchase this basket of goods, with 13 of the 24 items decreasing by more than that 30 percent average. The reason for this similarity is that both the average and median wages as measured by these series are more than 4 times higher than 1980.
But are these 24 items representative of other grocery items that we don’t have complete price data in the public BLS series? They are probably pretty close. The unweighted percent change in the items from April 1980 to April 2024 was 201%. If we use the CPI Food at Home component, which includes many more items but also changes in composition as buying habits change, we see a slightly larger 255% increase. But that is still less than wages have increased since 1980 (by over 300% for both average and median wages). As our incomes rise, we will naturally switch to better and more expensive foods, which can explain the 255% vs 201% difference in price increases, but it also shows the BLS isn’t engaging in any funny business with the indexes: if they kept the basket of goods constant, price increases would be smaller.
While the rise in prices since 2021 might rightly make us nostalgic for the pre-pandemic era of prices, let’s not be nostalgic for 1980 grocery prices.
Has the US tax and transfer system reached an egalitarian ideal? That’s one reading of this new working paper “How Progressive is the U.S. Tax System?” by Coleman and Weisbach. After accounting for all taxes and transfers (red lines in the charts), Americans across the income distribution saw roughly 250% real gains in income since 1966:
While market income has grown faster at the top of the income distribution (especially the top 1%), we also tax the rich heavily and use much of that tax revenue to fund direct transfers to poorer Americans and fund programs (such as Medicaid) which benefit poorer Americans. Put it all together, and everyone has seen similar gains over the past five decades, and these gains are fairly large: no Great Stagnation!
Inflation continues to remain stubbornly high in the US. While Core CPI is down to 3.6%, the lowest it has been in 3 years, this is still well above the Fed’s 2% target (the Fed’s preferred Core PCE is a bit lower at 2.8%). But consumers are tired of the cumulative inflation, which, depending on your preferred gauge of inflation, is somewhere around 20% in the past 4 years. Consumers want to know: will prices ever go down again?
The answer is: Yes, and some prices already have declined!
For example, you can look at broad categories of consumer purchases, such as durable goods, which are down almost 5 percent since the peak in August 2022. Durable goods include items such as used cars (down 17.3 percent since February 2022), furniture (down 6 percent since August 2022), and appliances (down 7.2 percent since March 2023).
We can even jump into the nondurables category and look at specific items, such as groceries which seem to be on everyone’s mind. Here’s a list of items and the price decrease since their peak (I ignore a few items where it is only a purely seasonal cycle that made them cheaper in April 2024):
Spaghetti and macaroni: -4.3% (Feb 2023)
Bacon: -12.8% (Oct 2022)
Chicken legs: -10.6% (Aug 2023)
Chicken breasts: -14.4% (Sept 2022)
Eggs: -40.6% (Jan 2023)
Milk: -8.3% (Nov 2022)
Cheddar cheese: -9.4% (Sep 2022)
Bananas: -2.6% (Sept 2022)
Oranges: -14.7% (Sept 2022)
Lemons: -12.3% (May 2022)
Strawberries: -12.9% in the past year (and down 34.6% since seasonal peak in Dec 2022)
Ground coffee: -6.2% (Dec 2022)
It’s true that this is a cherry-picked list: lots of items are at all-time highs! My goal here is to show that, Yes!, some prices will fall. Others may too in the near future. And while it’s also true that most prices are still well above 2019 levels, that’s not universally true. The April 2024 prices of lemons, strawberries, and tomatoes are roughly equal to their April 2019 prices.
And it’s not just food. Natural gas this January was 20% cheaper than January 2023. Regular unleaded gasoline is down 11.6% from 2 years ago (and down 25% from the peak in Summer 2022, but we’ll wait to see what this summer looks like). Even some services, such as airline fares, are down 6.7% from 2 years ago (and down 16% from June 2022).
Some of these price decreases could be due to factors specific to the production and supply of those goods, but another factor is monetary policy. Broad measures of the money supply such as M2 show a decline of about 4 percent in the past 2 years. That hasn’t yet produced overall deflation, but it has probably contributed to the decline in the goods and services mentioned.
Looking at price changes can only tell us so much though, especially focusing on individual item prices. The big picture is that over the past 4 years, wages have increased more than prices overall across most of the income distribution (only the highest quintile lost out on the race between wages and prices). Falling prices would certainly help this trend continue, but most consumers have more buying power than they did in 2019, even if they don’t feel like they do.
I’m in the process of writing a review of Jon Haidt’s book The Anxious Generation. I wrote some preliminary thoughts a few weeks ago, but I’m diving a lot deeper now, so watch for that review soon. But one of the main startling pieces of data in the book is the dramatic rise in suicides among young girls. Haidt isn’t the first to point this out, but in large part his book is an attempt to explain this rise (as well as the rise among boys and slightly older girls).
This got me thinking a bit more broadly about not just suicides, but all causes of mortality among young Americans. So in the style of my 2022 post about the leading causes of death among men ages 18-39, let’s look at the historical trends for deaths among girls 10-14 in the US.
Data comes from CDC WONDER. The top dark line shows total deaths, and the scale for total deaths is the right-axis. Notice that for total deaths, there is a U-shaped pattern. From 1999 to about 2012, deaths for girls aged 10-14 are falling. Then, the bottom out and start to rise again. While the end point in 2022 is lower than 1999 (by about 9 percent), there is a 22 percent increase from 2010 to 2022.
What’s driving those trends? A fall in motor vehicle accidents (blue line, the leading cause of death in both 1999 and 2022) is driving the decline. This category fell 41 percent over the entire time period: a big drop for the leading cause of death!
But the rise in suicides (thick red line) starting in 2013 is the clear driver of the reversal of the overall trend. Suicides for this demographic in 2022 were 268 percent higher than 1999, and 116 percent higher than 2010. Haidt and others are right to investigate the causes of this trend (I’m not convinced they have the complete answer, but more on that in my forthcoming book review).
There has been no clear trend in cancer deaths over this time period, and the combination of all the three of these trends means that roughly equal number of girls ages 10-14 die from car accidents, suicide, and cancer.
What can we learn from this data? First, we should acknowledge just how rare death is for girls ages 10-14. At 14.8 deaths per 100,000 population, it is the lowest 5-year age-gender cohort, other than the ages just below it (ages 5-9, for both boys and girls). But just because it is small doesn’t mean we should ignore it. The big increase, especially in suicides, in the past decade is worrying and could be indicative of broader worrying social trends (and suicides have risen for almost every age group too, see my linked post above).
If a concern, though, is that we are over-protecting our kids and this is leading them to retreat into a world of social media, we might want to see if there are any benefits of this overprotection in addition to the costs. The decline in motor vehicle accidents is one candidate. Is this decline just a result of the overall increase in car safety? Or is there something specific going on that is leading to fewer deaths among young teens and pre-teens?
As we know from other data, a lot fewer young people are getting driver’s licenses these days, especially compared to 1999 (and engaging in fewer risky behaviors across the board). Of course, 10-14 year-olds themselves usually weren’t the ones getting licenses — they are too young in most states — but their 15 and 16 year-old siblings might be the ones driving them around. Is fewer teens driving around their pre-teen siblings a cause of the decline in motor vehicle deaths? We can’t tell from this data, but it is worth investigating further (note: best I can tell, only about 23 percent of the decline is from fewer pedestrian deaths, though in the long-run this is a bigger factor).
Social tradeoffs are hard. If there really is a tradeoff between fewer car accident deaths and more suicides, how should we think about that tradeoff? Or is the tradeoff illusory, and we could actually have fewer deaths of both kinds? I don’t think I know the answer, but I do think that many others are being way too confident that they have the answer based on what data we have so far.
One final note on suicides. For all suicides in the US, the most common method is suicide by firearm: about 55% of suicides in the US were committed with guns in 2022, with suffocations a distant second at about 25%. For girls ages 10-14, this is not the case, with suffocation being by far the leading method: 62% versus just 17% with firearms. I only mention this because some might think the increasing availability of firearms is the reason for the rise in suicides. It could be true overall, but it’s not the case for young girls.
Economist Writing Every Day 