Data

Young Americans Continue to Build Wealth, Across the Distribution

Jeremy Horpedahl7 Comments

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 past posts. 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:

Future Consumption Has Never Been Cheaper

Zachary Bartsch1 Comment

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!

https://fred.stlouisfed.org/graph/?g=1p0C7

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.

Continue reading →

Predicting College Closures

James Bailey7 Comments

This week the University of the Arts in Philadelphia announced they were closing effective immediately, leaving students scrambling to transfer and faculty desperate for jobs. U Arts now joins Cabrini University and Birmingham-Southern as some the 20 US colleges closing or being forced to merge so far this year. This trend of closures is likely to accelerate given falling birth rates that mean the number of college-age Americans is set to decline for decades; short-term issues like the FAFSA snafu and rising interest rates aren’t helping either.

All this makes it more important for potential students and employees to consider the financial health of colleges they might join, lest they find themselves in a UArts type situation. But how do you predict which colleges are at significant risk of closing? One thing that jumps out from this year’s list of closures is that essentially every one is a very small (fewer than 2000 undergrad) private school. Rural schools seem especially vulnerable, though this year has also seen plenty of closures in major cities.

Source

There appear to be a number of sources tracking the financial health of colleges, though most are not kept up to date well. Forbes seems to be the best, with 2023 ratings here; UArts, Cabrini, and Birmingham-Southern all had “C” grades. If you have access to them, credit ratings would also be good to check out; Fitch offers a generally negative take on higher ed here.

In a 2020 Brookings paper, Robert Kelchen identified several statistically significant predictors of college closures:

I used publicly available data compiled by the federal government to examine factors associated with college closures within the following two to four years. I found several factors, such as sharp declines in enrollment and total revenue, that were reasonably strong predictors of closure. Poor performances on federal accountability measures, such as the cohort default rate, financial responsibility metric, and being placed on the most stringent level of Heightened Cash Monitoring, were frequently associated with a higher likelihood of closure. My resulting models were generally able to place a majority of colleges that closed into a high-risk category

The Higher Learning Commission reached similar conclusions. Of course, there is a danger in identifying at-risk colleges too publicly:

Since a majority of colleges identified of being at the highest risk of closure remained open even four years later, there are practical and ethical concerns with using these results in the policy process. The greatest concern is that these results become a self-fulfilling prophecy— being identified as at risk of closure could hasten a struggling college’s demise.

Still, would-be students, staff and faculty should do some basic research to protect themselves as they considering enrolling or accepting a job at a college. College employees would also do well to save money and keep their resumes ready; some of these closures are so sudden that employees find out they are out of a job effective immediately and no paycheck is coming next month.

2023 Jobs Data

Jeremy HorpedahlLeave a comment

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.

Grocery Price Nostalgia: 1980 Edition

Jeremy Horpedahl8 Comments

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.

Income Growth Since 1966 in the US

Jeremy Horpedahl1 Comment

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!

Almost Observable Human Capital

Zachary BartschLeave a comment

I’ve written about IPUMS before. It’s great. Among individual details are their occupations and industry of their occupation. That’s convenient because we can observe how technology spread across America by observing employment in those industries. We can also identify whether demographic subgroups differed or not by occupation. There’s plenty of ways to slice the data: sex, race, age, nativity, etc.

But what do we know about historical occupations and what they entailed? At first blush, we just have our intuition. But it turns out that we have more. There is a super boring 1949 report published by the Department of Labor called the “Dictionary of Occupational Titles”. The title says it all. But, the DOL published another report in 1956 that’s conceptually more interesting called “Estimates of Worker Trait Requirements for 4,000 Jobs as Defined in the Dictionary of Occupational Titles: An Alphabetical Index”.  The report lists thousands of occupations and identifies typical worker aptitudes, worker temperaments, worker interests, worker physical capacities, and working conditions. Below is a sample of the how the table is organized:

Continue reading →

Where’s the Deflation?

Jeremy HorpedahlLeave a comment

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.

Not Crazy: Insurance Premiums

Zachary Bartsch4 Comments

Higher homeowner’s insurance premiums have been in the news. But are we just hearing about the extreme cases? This post is inspired by the FRED Blog post about property and casualty (P&C) insurance premium producer price indices. I dive a little deeper.

The insurance premium data is composed of seven components:

  1. Private passenger auto insurance
  2. Homeowner’s insurance
  3. Commercial auto insurance
  4. Non-auto liability insurance
  5. Commercial multiple peril insurance
  6. Worker’s compensation insurance
  7. Other property and casualty insurance

Non-auto liability insurance is further split up into A) medical malpractice insurance and B) other non-auto liability insurance.*

Continue reading →

What’s Killing Girls Ages 10-14?

Jeremy HorpedahlLeave a comment

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.