Recently there has been some discussion in the Presidential race about the taxation of parents vs. childless taxpayers. The discussion has been ongoing, but it was kicked up again when a 2021 video of J.D. Vance resurfaced where he said that taxpayers with children should be lower tax rates than those without children. There was some political back-and-forth about this idea, much of it tied up in the framing of the issue, with the usual bad faith on both sides about the fundamental issue (in short: most Democrats and a small but growing number of Republicans support increasing the size of the Child Tax Credit).
Let’s leave the politicking aside for a moment and focus on policy. As many pointed out in response to Vance’s idea, we already do this. In fact, we have almost always done this in the history of the US income tax — “this” meaning giving taxpayers at least some break for having kids. For most of the 20th century, this was done through personal exemptions which usually included some tax deduction for children, and later in the century the Child Tax Credit was added (after 2017, the exemptions were eliminated in favor of a large CTC). Other features of the tax code also make some accounting for the number of children, most notably the size of the Earned Income Credit.
The chart below is my attempt to show how the tax breaks for children have affected four sample taxpaying households. What I show here is sometimes called the “zero bracket” — that is, how much income you can earn without paying any federal income taxes. The four households are: a single person with no children, a married couple with no children, a single person with two children (“head of household”), and a married couple with two children. All dollar amounts are inflation-adjusted to current dollars
The concept of “fiscal illusion” has long existed in public finance, but it is difficult to test. The basic theory is that people will underestimate how much they pay in taxes, as well as underestimate government expenditures. A forthcoming paper in Public Choice by Kaetana Numa uses survey data from the United Kingdom to test the theory, and finds support. From the abstract of “Fiscal illusion at the individual level“:
“providing personalized fiscal information reduces support for higher taxes and spending and increases support for lower taxes and spending. These findings indicate that taxpayers underestimate both their tax liabilities and the costs of public services.”
The paper uses a “novel personalized fiscal calculator” to estimate how much tax an individual would actually owe. It then randomizes which taxpayers get this information, and finds that “the treated respondents… were less supportive of raising taxes and more supportive of cutting taxes than the respondents in the control condition.”
And the results are large. For all taxes, in the treated group that saw their personalized fiscal calculator, 61 percent support cutting taxes, versus just 50 percent in the control group. The differences show up across the major taxes that individuals pay in the UK, including the income tax, national insurance contributions (both employer and employee sides), and the VAT. There is no tax category where the treatment group is more likely to want to increase the tax, though the VAT and the smaller Fuel duty and Council tax are about equal on the percent wanting an increase (but the median response for these last two is to decrease the tax — in both the control and treatment groups).
Do these results from the UK hold up in other developed nations? Possibly. In a 2014 Eurobarometer survey, the percent of EU citizens that could correctly identify their nation’s VAT rate varied widely. The high was 89 percent in Germany correctly identifying the rate, down to 31 percent in Ireland. The average was 65 percent — though the UK was at the low end with only about 47 percent correctly identifying the VAT rate.
Fiscal illusion appears to be a real issue, and probably an important one in the UK.
Among the former G8 countries, Russia has by far the highest cumulative inflation rate since January 2020, almost double the amount of inflation we’ve seen in the US and in most G7 countries. No doubt the effects of the wartime economy are contributing to this, but even in February 2022 before they invaded Ukraine, their inflation still had clearly been worse.
The US is on the high end for this group, but pretty close to the median. Japan looks really good on inflation, but that’s probably not much comfort to them since their economy is still smaller than before the pandemic. By this measure, the US looks pretty good (chart from Joey Politano):
GDP estimates for Russia are a little tricky because of the war, but according to IMF estimates, Russia’s economy in 2023 was about 5.6% larger than 2019 in real terms.
Crime of all forms certainly spiked in 2020 and 2021 in most of the US, and continued to remain high for a time after that. But recent data, especially homicide data compiled by AH Datalytics, suggest that crime is falling. When measured by homicide rates, the worst of crimes and the least likely to be underreported, homicide rates across 272 major cities in the US is down 17.6% in 2024 compared with the same period in 2023. And among the 20 cities with the most homicides in 2023, just one (Birmingham, the 20th on the list) saw an increase from 2023 to 2024.
But is this just coming down from a relative high? Are homicide rates still elevated from pre-pandemic? I went through the cities with the most homicides on the AH Datalytics list, and for those where I could find comparable data pre-pandemic, I created the following charts. As you will see, lots of these cities are down to or below pre-pandemic levels (for the period in 2024 that is comparable to prior years). Not every single city, of course, but most are close to 2019 or prior years.
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.
Economist Writing Every Day 