Inflation has been constantly in the news over the past 2 years, but it has especially been in the news lately with regards to one country: Argentina. That country has been experiencing triple-digit annual inflation lately, and it has become one of the key issues in the current presidential race.
How bad is inflation in Argentina? Here’s a comparison to some other G20 countries from September 2019 through September 2023 (data from the OECD).
Cumulative consumer price inflation in Argentina over the past 4 years is over 800 percent. That means goods which cost 100 pesos in September 2019 now costs 900 pesos, on average. Well, they did in September. It’s almost November now, so if the recent inflation rates persisted, those goods are around 1,000 pesos now.
Turkey also stands out as a country with very rapid inflation the past 4 years — without Argentina on the chart, Turkey would clearly stand out from the rest. But other than Turkey, all the other countries are bunched at the bottom. Has there not been much difference among them? Not quite.
This next chart removes Argentina and Turkey:
In this second chart we see two standouts on the opposite end of the spectrum: Japan and Switzerland have had extremely low inflation, just 6 and 5 percent cumulatively since late 2019 (and this is not unusual for these two countries in recent history).
For us here in the USA, things don’t look so good. Only Brazil and the EU are higher (and the EU is mostly due to energy price inflation in Eastern Europe), so other than that we are basically tied with the UK for the worst inflation performance among very high income countries during the pandemic. That’s bad news! But perhaps one silver lining is that average wages in the US have outpaced inflation slightly: 23 percent vs 20 percent growth over this time period. That’s not much to celebrate — except relative to most of the rest of the world.
On X.com Matt Yglesias posted a chart that sparked some conversation about child safety:
One thing about the much-lamented rise of more intensively supervised childhood activities is that kids have in fact become a lot less likely to die. pic.twitter.com/MTjR7spLM8
Of course, it was probably more his comment about the “rise of more intensively supervised childhood activities” that generated the feedback and pushback. And I assume his comment was partially tongue-in-cheek, as often happens on Twitter, and designed to generate that very discussion. Still, it is worth thinking about. Exactly why did that decline happen?
I’ve posted on this topic before. In my March 2023 post, I looked at very broad categories of child death. While all death categories have declined, about half of the decrease (depending on the age group, but half is about right) is from a decline in deaths from diseases, as opposed to external causes. And fewer disease death can largely be attributed to improvements in healthcare, broadly defined. Good news!
Of course, that means that about half of the decline is from things other than diseases. What caused those declines? Let’s look into the data. Specifically, let’s look into the data on deaths from car accidents.
A few weeks ago I wrote about several measures of the labor market, and whether the labor market was actually doing well. It’s a good idea to look beyond the headline unemployment rate, but even looking at alternative unemployment rates, labor force participation, employment rates, and unemployment insurance claims, I concluded in that post that the labor market is still looking healthy.
Lately I have heard another objection to the job growth numbers: part-time employment. I’ve seen this pop-up a few times on Twitter lately and just yesterday my co-blogger Scott Buchanan (in a post primarily about excess savings) stated that “much of the jobs creation this year has been in the part-time category.”
So is the jobs recovery mostly about part-time jobs? What is going on?
First things first: most of the data on part-time employment is from the household survey. There’s already a lot of noise in the household survey, due to the sample size, and part-time workers are a small share of the workforce, so expect it to be even noisier. In short, don’t trust one-month fluctuations too much. Furthermore, most of the data folks look at is seasonally adjusted. That’s generally good practice! But again, for a small number in a small sample, the seasonal adjustment factors won’t be perfect. Don’t read too much into one or a few months of data.
Let’s get the big picture first. How much of the labor force in the US is usually working part-time (defined in most data as less than 35 hours per week)? As usual FRED is the best place to go for graphing BLS data:
Last weekend I had the opportunity to visit an arcade, but not one of those modern fancy arcades with virtual reality, laser tag, etc. This arcade specializes in having old-school games, primarily pinball, but also early video arcade games. You pay a cover charge ($5 for kids, $10 for adults), and then you use quarters to play the games. But here’s the cool part: the price of the games is the same as it was when the games were first released.
As an economist, of course, I was very interested in the prices.
They had pinball machines that dated back the 1960s, and video games from the late 1970s. Most video arcade games were around 50 cents for the early games (late 1970s and early 1980s). But the pinball machines started out at 25 cents, with the earliest game they had being a Bally Blue Ribbon machine, manufactured in 1965 (interestingly, some of the earlier machines had slots for both dimes and quarters — I assume the price was adjustable mechanically). Notably, you also got to play 5 balls for this price (3 balls seems to be standard later on).
How should we think about that 25 cents? A standard reaction is to adjust the number for inflation. Using the CPI-U as the inflation index, that means the 25 cents from 1965 is “worth” about $2.40 now. That’s interesting, but I don’t think it really provides the relevance that we want today.
An alternative is to calculate the “time price” of playing the game. Using the average hourly wage of $2.67 in December 1965, we can calculate that it would take about 5.5 minutes of work to pay for that game — a game which probably only lasts about 5.5 minutes, unless you are really good at it!
Another comparison we could do is with the cost of video games today compared with wages today. But that’s not really a fair comparison — video games are much more advanced today. We would need to do some sort of quality adjustment, which is overly complicated.
But, at least in my case, there is no need to do the quality adjustment — I can play the exact same game as 1965. In fact, I did (several times). There was also that $10 cover charge that I mentioned, and if I spread that fixed cost over 40 games, it cost me about 50 cents per play (including the 25 cents to start the machine) to play the 1965 Bally’s Blue Ribbon Pinball machine. At the average wage today of $29 per hour, it takes about 1 minute to afford a play of that same game. In other words, my Blue-Ribbon-Pinball standard of living is about 5.5 times greater than in 1965.
Now this isn’t to say we are 5.5 times better off overall than 1965. Prices don’t stay constant for most goods! But hopefully it is a useful way to think about that 25 cent price tag from the past, and how to compare it to today.
The Federal Reserve has released the latest update to their Distributional Financial Accounts data, which the data underlying several of my past posts on generational wealth. With that recent data, I have updated the chart of wealth for Baby Boomers, Generation X, and Millennials.
The data is shown on a log scale to better show growth rates and allow for easier visual comparisons. But if you are interested in the more precise numbers, in the most recent quarter (2023q2) Generation X has, on average about $620,000 in net wealth, which compares favorably with Baby Boomers at about the same age (in 2006) with about $539,000 in net wealth per person. That’s about 20 percent more.
Millennials have about $115,000 in net wealth on average, which also compares favorably with Baby Boomers, who had slightly more at about the same age (in 1990) with $121,000 in net wealth on average. Given the uncertainties of all the data that goes into this, I’d say those are roughly equal. Gen X had a bit more around the same age (in 2007) with $149,000, but that fell significantly the next two years during the Great Recession.
(For more detail on my approach to creating the chart, see the linked post above, but in short I’m using the Fed DFA data for wealth, Census Bureau data by single year of age for population, and the Personal Consumption Expenditures price index for inflation adjustments (I also have a chart with the CPI-U — it’s not much different). Wealth data is for the 2nd quarter in each year (to match 2023), except for 1989 since the 3rd quarter is the first available.)
Given how much wealth can fluctuate based on housing values (see above for Gen X from 2007-2009), it might be useful to look at the data with housing. Housing is also a weird kind of wealth — for the most part, you can’t access it without selling (other than certain home equity loans), and when you do sell, unless your home appreciated more than average, you just have to move to another home that also appreciated.
Here’s the chart excluding housing value and mortgage debt:
The chart… doesn’t change much. The values are all lower, of course, but the comparisons across generations look pretty similar. Gen X right now is 17 percent wealthier than Boomers at the same age. And if we look at all three generations around the median age of 35, they are pretty close: Gen X with $123,000 (but slipping over the next few years), Boomers with $99,000, and Millennials with $90,000.
Last week I wrote about median income in the US, and how it had declined since 2019 and 2021 through 2022 (inflation adjusted, of course). The big story is that median income (both for households and families) has been falling in recent years. While there are some silver linings when looking at subgroups, such as Black families, the overall data isn’t good.
But while that is true for the US overall, it’s not true for every state. In fact, it’s not even true for most states! From 2019 to 2022, there were 29 states that saw their median family incomes rise! That’s adjusted for inflation (I’m using the C-CPI-U, which is Census’s preferred inflation measure for this data). The income data in this post all comes from the Census ACS 1-year estimates.
Here’s a map showing the states that had increases in median family income (green) and those that had decreases (in red). (This is my first time experimenting with Datawrapper maps, feedback appreciated!)
Some states had pretty robust growth, with New Mexico and Arizona leading the way with around 5 percent growth. There is substantial variation across US states, including with big declines like Wyoming at -5 percent, and Oklahoma and Illinois are -3 percent.
A few weeks ago I also wrote about the richest and poorest MSAs in the US. But what about the richest and poorest states in the US? The following map shows that data.
The immediate fact which will jump out at you is that the lowest income US states are almost all located in the South. This will probably not surprise most of us, although it probably is a bit surprising since the data is adjusted for differences in the cost of living (using the BEA RPP data). Even after making these adjustments, the South is still clearly the poorest region (and it definitely was the poorest without the adjustments).
Among the higher income states, they are distributed pretty well across the rest of the non-South. There are 16 states (plus DC) that have median family incomes over $100,000 (again, cost of living adjusted), and while many of these are in New England and the Mid-Atlantic, there area still a few in the Midwest, Great Plains, and the West. Utah and New Jersey have similar incomes, as do Virginia and Rhode Island.
The highest income states are Massachusetts and Connecticut, with over $112,000 in median family income, while the lowest are Mississippi and West Virginia, both under $78,000. Median family income in Massachusetts is 46 percent higher than Mississippi. And that’s after adjusting for differences in the cost of living.
This week the Census Bureau released their annual update on “Income, Poverty and Health Insurance Coverage in the United States.” This release is always exciting for researchers, because it involves as massive release of data based on a fairly large (75,000 household) sample with detailed questions about income and related matters. For non-specialists, it also generates some of the most commonly used national data on income and poverty. Have you heard of the poverty rate? It’s from this data. How about median household income? Also from this data.
I’ll focus on income data in this post, though there is a lot you could say about poverty and health insurance too. The headline result on median income is, once again, a dismal one. Whether you look at median household income (very commonly reported, even though I don’t like this measure) or median family income (which I prefer), both are down from 2021 to 2022 when adjusted for inflation. Both are still down noticeably from the pre-pandemic high in 2019 (though both are also above 2018 — we aren’t quite back to the Great Depression or Dark Ages, folks!).
These headline results are bad. There is no way to sugarcoat or “on the other hand” those results. And these results are probably more robust and representative than other measures of average or median earnings, since they aren’t subject to “composition effects” — when those with zero wages in one period don’t show up in the data. I will note that these results are for 2022, and we are highly likely to see a turnaround when we get the 2023 data in about a year (inflation has slowed to less than wage growth in 2023).
But given that obviously bad headline result, was there any good data? As I mentioned above, a ton of data, sliced many different ways, is released with this report. Some of it also gives us consistent data back decades, in some cases to the 1940s. What else can we learn from this data release?
Median Income by Race
When we look at median income by race, there are a few silver linings. The headline data from Census tells us that only the drop in household income for White, Non-Hispanics was statistically significant. For other races and ethnicities, the changes were not statistically significant from 2021 to 2022 — and some of those changes were actually positive. We shouldn’t dismiss White, Non-Hispanics — they are the largest racial/ethnic group! — but it is useful to look at others.
Black household and families are the most interesting to look at in more detail, especially because they are the poorest large racial group in the US. Black household and family income increased from 2021 to 2022, although the increase was small enough that we can’t say it is statistically significant (remember, this is a sample, not the universe of the decennial Census).
But what’s more important is that median Black household income is now at the highest level it has ever been (adjusted for inflation, as always). Median Black household income is about $1,000, or around 2 percent higher than in 2019 — the peak date for overall median income. Two percent growth over 3 years is nothing to shout from the rooftops, but it is very different from White, Non-Hispanic households, which are down over 6 percent since 2019.
Median black family income is roughly flat since 2019, but it is up about 1.5 percent in the past year — not quite as robust, but still better than the overall numbers.
Historical Income Data
The other silver lining I always like to mention is the long-run historical data. This data often gets overlooked in the obsessive focus on the most recent changes, so it’s useful to sit back and look at how far we have come. Let’s start where we just left off, with Black families. I wrote a post back in February about Black family income, which had data current through 2021, but it’s useful once again to look at the data with another year (plus they have updated the inflation adjustments for 2000 onward).
The chart shows the percent of Black families that are in three income groups, using total money income data. The data is adjusted for inflation. The progress is dramatic. In 1967, the first year available, half of Black families had incomes under $35,000. By 2022 that number had been cut in half to just one quarter of families (the 2022 number is the lowest on record, even beating 2019). Twenty-five percent is still very high, especially when compared to White, Non-Hispanics (it’s about 12 percent), but it’s still massive progress. It’s even a 10-percentage point drop from just 10 years ago. And Black families haven’t just moved up a little bit: the “middle class” group (between $35,000 and $100,000) has been pretty stable in the mid-40 percentages, while the number of rich (over $100,000) Black families has grown dramatically, from just 5 percent to over 30 percent.
We saw earlier that progress for White, Non-Hispanics has stumbled in the past 3 years, but the long run data is much more optimistic (this data starts in 1972).
The progress here should be evident too, but let me highlight one thing for emphasis: as far back as 1999, the largest of these three groups was the “rich” (over $100,000 group). And since 2017, the upper income group has been the majority, with median White Non-Hispanic family income surpassing $100,000 in 2017, up from $70,000 at the beginning of the series in the early 1970s (all inflation adjusted, of course).
The next question I often get with this historical data is: How much of this increase is due to the rise of two-income households. Well, this same data release allows us to look at that data too! This final chart shows median family income for families with either one or two earners (there are families with zero earners or more than two, but these two categories make up the bulk of families). This data is pretty cool because it goes all the way back to 1947.
This chart doesn’t look so good for one-earner families. After growing along with two-earner families in the 1950s and 1960s, it basically stagnates from the early 1970s until the late 2010s. Then you get a little growth. Not good!
I think more investigation is needed here, but the share of families that have two earners has grown dramatically, from 26 percent of families in 1947 to 42 percent in 2022. Single earner families shrunk from 59 percent to 31 percent, and dual-income families have been the most common family type since the late 1960s. There are some important compositional differences here in what types of families only have one earner. If we imagine some alternate history where, by law, only one spouse was allowed to work, certainly the single earner line would have risen more. And many of the single earner families today are single mothers, who for a variety of reasons have much lower earning potential than the fathers heading married couples in the 1950s and 1960s. So the numbers aren’t perfectly comparable.
Still, even for single earner families, real median income has more than doubled since 1947 — though most of that growth had happened by the early 1970s.
As we make our way through a challenging economic time following the pandemic and 2 years of unusually high inflation, hopefully we can look forward to a future of resuming the upward trajectory of incomes for all kinds of families.
1996 was a big year for minivans. While modern minivans had been around for about a decade by that point, 1996 marked a turning point. That year Dodge introduced what is referred to as the “third generation” of its Caravan, and it won Motor Trend’s car of the year award. That’s the first, and only time, that a minivan ever won this award. If you drive a minivan today or see one on the road, you are seeing the look, style, and features that were first introduced in 1996 (interestingly, that year also seems to have marked the peak in sales for the Chrysler family of minivans).
If you wanted to buy the cheapest possible Dodge Caravan in 1996, you would have paid about $18,500. You could always pay more for more features, as with any car, but if you wanted this “car of the year,” and you wanted it new and cheap, that was what you paid.
Dodge continued to produce the Caravan for the US market until 2020, when it was discontinued in favor of other nameplates (though it still lived on in Canada). In 2020, the base model Caravan was about $29,000 (and now only available in the “Grand” version, an upgrade in 1996).
Income varies a lot across the US. So does the cost of living. Does it mostly wash out when you adjust incomes for the costs of living? No, not even close. Apples-to-apples comparisons are always hard, but it’s still worth making comparisons.
Let’s use some data that Ryan Radia put together that I really like, for several reasons. He uses the 100 largest MSAs — these comprise about 2/3 of the US population. He uses median income, so outliers shouldn’t effect the income data. He uses median family income, since the more common median household income is, in my opinion, very difficult to interpret (5 college students living together are a household, and so is one elderly person living alone). And Ryan also limits it to non-elderly, married couples, and then separates the data by the employment status of each member of the couple.
As an illustration, let’s use the data for married couples with only one spouse working full-time (I have played around with the data for other working statuses, and the results are similar). Before adjusting for the cost of living, here are the top MSAs with the highest median incomes:
The answer to that question is, of course, “no.” No one number can alone tell us the whole story, whether we are talking about the economy, health, education, population, or any other social statistic. But when you look at other measures of the health of the labor market, you usually find that they tell a similar story to the unemployment rate.
My goal in this post is to dive a little deeper into the data on the labor market, but really the goal is broader: to give you a little insight about how to interpret data. Some rules of thumb, perhaps. But really there is One Big Rule: numbers need context. A number on its own doesn’t tell us much of anything. How does it compare to the past? How does it compare to other places?
With the unemployment rate at historic lows for both the US and many states, I’ve started to see many people saying that, not only doesn’t the unemployment rate give us the full story, but many other indicators point in the opposite direction. Is this true? Let’s dig into the data. Here’s one example of someone saying this for Arkansas. I’ll focus on Arkansas, since that’s where I live and I pay attention to the economic data here pretty closely, but I’ll also refer to national data where appropriate.
Economist Writing Every Day 