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.
Regular readers will know that we love not only economics, but also history and data. We especially love it when “data heroes” take data that was difficult or impossible to access and make it easily available to everyone. The Federal Reserve Bank of Philadelphia just announced a project that brings together all of these things we love, their new Center for the Restoration of Economic Data:
Our mission is to advance research in topics related to regional economics and consumer finance by making economic data available in readily accessible, digital form. CREED combines state-of-the-art machine learning technology with deep subject matter expertise to convert natively unstructured data (information in books, images, and other undigitized formats) into readily accessible digital data.
The CREED research team shares the original analog or unstructured data as well as the code used to recover and clean these data, which are aggregated for use in novel economic research. Our collection features volumes of old, often overlooked, and frequently inaccessible data, which have been mined, restored, and converted into unstructured digital and analytically usable formats.
Their first project is to map all of the racially restrictive covenants in the city of Philadelphia. Until the U.S. Supreme Court declared such covenants to be unenforceable in 1948, they often barred properties from being sold to non-whites or non-citizens. After 1948 redlining took different forms, some of which may still persist today.
CREED shares the underlying data used to build the map here, and they say much more is one the way. I love it when economic historians (and regular historians) digitize old paper records and share the resulting data, and hope to see more examples like this to share in the coming years.
Disclaimer: I am a visiting scholar at the Federal Reserve Bank of Philadelphia but I was not involved with this project
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.
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).
Sometimes you read an academic article and the author fills in the data gaps with interpolation. That is, they assume some functional form of the data and then replace the missing values with the estimated ones. Often, lacking an informed opinion about functional form, authors will just linearly interpolate between the closest known values. Sometimes this method is OK. But sometimes we can do better.
Historical census data provides a good example because the frequency was only every ten years. Say that we want to know more about child migration patterns between 1850 and 1860. What happened in the intervening years? Who knows. Let’s look at the data.
Using data on individuals who have been linked across censuses allows us to fill in the gaps a little bit. For simplicity, let’s just look at whether a child migrant lived in an urban location and whether they lived on a farm. That means that there are 4 possible ways to describe their residence. Below is a summary of where children migrants lived at the age of zero in 1850 and where the same children lived a decade later at the age of ten in 1860 given that they moved counties.
When I’m the mean time did these children move from one place and to the other? We don’t know exactly. The popular answer is to say that they moved uniformly throughout the decade. That’s ‘fine’. But it assumes that the rate at which people departed places was rising and the rate at which they arrived places was falling. Maybe that’s true, but we don’t really know. Below-left is a graph that shows the linear interpolation.
The nice thing about linear interpolation is that everyone is accounted for at each point in time. The total number of people don’t rise or fall in the intervening interpolation period. But if we were to assume that children departed/arrived at each type of place at a constant rate (maybe a more reasonable assumption), then suddenly we lose track of people. That is, the sum of people dips below 100% as people depart faster than they arrive.
Demography is cool generally, but life tables are really cool in their elegance. Don’t know what a life table is? Let me ‘splain.
A life table uses data from private or public death registers, or even genealogical records, to identify a variety of survival and death estimates. Briefly, the tables include for each age:
Probability of death in the next year
Probability of surviving to the age
The life expectancy
There is more in the tables, but these are the big items that people often want to know. All of the various table columns can be calculated from survival rates. The US government and the UN each has created many such tables for a variety of time, locations, and development details. For example, the earliest and most dependable one is from 1901 and includes separate tables by race, sex, migrant status, urbanity, and even for some specific states.
You may have heard that there is a new viral song which deals with a few economic issues. Noah Smith has a good analysis of “Rich Men North of Richmond,” which he mostly finds to be incorrect in its analysis (for example, of welfare policy). But Smith does say that the song has a point: manufacturing wages haven’t performed well in recent years. Not only has pay for factory workers “[lagged] the national average in recent years,” for those workers in Virginia, it’s lower in real terms than in 2010.
Well that all doesn’t sound good! Smith is only going back to about 2000 with the data he shows. What if we took a longer run perspective? What if we took a really long-run perspecitive?
Here’s wages for blue-collar factor workers that goes back to 1939 in the US:
The wage data (for manufacturing production workers) is from BLS and the PCE price index is from the BEA. What do you notice as you look at the data?
First, it is true that the last 20 years or so hasn’t been great. Only about 8% cumulative growth since 2002. That’s not great!
But as you look back further, you’ll notice that gains are substantial. Compared to what some might consider the “golden age” of manufacturing wages, the early 1950s, real wages have roughly doubled. It’s true, the growth rate from 1939-1973 is much, much better than the following 50 years. Wouldn’t it be nice if that growth rate had continued! But no doubt you’ve seen many memes saying something like “in the 1950s you could support a family on one high-school graduate income, but not today!” This data suggests that view of the 1950s is a little distorted by nostalgia.
One final thing to note: we might think that one big change in recent decades is that a lot more compensation goes to benefits, rather than wages. There’s actually a total compensation series for blue-collar workers going all the way back to 1790:
The total compensation data, as well as the CPI data that I used to inflation-adjust the figures (to 2022 dollars), comes from the fantastic resource Measuring Worth. This is a total compensation measurement, so it includes benefits, but the source data tells us that up until the late 1930s, it’s really just a wage measure. So potentially we could splice this together with the above chart, to get a “wage only” series covering the entire history of the US.
However, when we look at total compensation, we still see the post-1970s stagnation. Real compensation is roughly the same as about 1977. Yikes! Note here that we’re using the CPI, since the PCE index only goes back to 1929, and the CPI tends to overstate inflation (yes, that’s right, sorry CPI truthers). Still, it’s not the most optimistic picture.
Or isn’t it? With all of the automation and global competition in manufacturing coming on board in the past 50 years, perhaps our baseline is that things could have been much worse. In any case, if we look at total compensation, it’s currently about double what it was in the post-WW2 era. That’s even with the dip in 2022 due to high CPI inflation.
Wages and compensation of blue-collar productions workers have indeed been growing slowly for the past few decades. That much is true. On the other hand, they are still among the highest they have ever been in history, over 50 times (not 50%, 50 times!) higher than at the birth of this nation. This ranks them as probably the highest wages anywhere in world history for an occupation that doesn’t require an advanced degree. That history is worth knowing.
I was reading “The Ultimate Guide to Barbie” the other day, and I noticed an interesting piece of data towards the end of the magazine: the original Barbie doll in 1959 retailed for $3. Today, according to the magazine, a Barbie costs around $14-19. And they further told us that adjusted for inflation, that $3 original Barbie is about $24 today.
I’m not sure exactly where they got that number. Using the BLS CPI tool, it’s more like $31.50. And while I appreciate the attempt to give us historical context, I think for the typical reader will still be a bit perplexed. What does it mean to say $3 in 1959 is equal to $24 (or $31.50) today? Well, it means that the price of Barbie dolls has risen more slowly than other goods and services (quality adjusted). But I think we can do better on the context.
Here’s my best attempt to give context:
The chart shows the number of minutes of work that the median woman would need to work to purchase a Barbie doll for her daughter. In 1959, it took almost 2 hours of work. Today, it takes only about a half hour (I’m using the lower range from the magazine, $14 for a Barbie today, although there are plenty of $10-11 Barbies on Amazon).
Another way of thinking about it: with the same amount of work, a working mother today could buy her daughter 3-4 times as many Barbies as her counterpart in 1959.
I deliberately used median female wages here to make another historical comparison. Women’s earnings have increased much more than men’s since 1959. Back then, median female earnings for full-time, year round workers was only 61% of male earnings. Today, it is close to 85%. True, that’s still not parity. And for those that know the history, you will also know that the closing of that gap has stagnated in recent years. But this is still some major progress during the Barbie Era.
Finally, as I have emphasized before, looking too much at the cost of one product over time has limits. What about other goods and services? A toy, even a well-known brand like Barbie, is a tradable good that can be manufactured anywhere in the world (it looks like Indonesia is where many Barbies are made today). So it wouldn’t be surprising that it has got cheaper over time. But what about all goods and services?
Here’s where inflation adjustments are most useful. Not for individual goods and services, but for looking at incomes over time. How much stuff can a given income purchase compared to the past? That’s what inflation adjustments are for. And this chart shows male and female median earnings in 1959 and 2023, with the 1959 figures adjusted to 2023 dollars using the PCE price index.
When we adjust for changes in all prices, not just Barbies, we can see that median female earnings have roughly doubled between 1959 and 2023. That’s not quite as robust as the “Barbie standard of living,” which allows you to purchase 3-4 times as many dolls. But 2 times as much stuff is pretty good. It’s especially good when compared with male earnings growth, which grew about 44 percent.
It should be obvious here that these are just the raw medians, not controlling for anything like education, experience, or occupational choice. Controlling for those will shrink the gap a bit more. But the gains for women in the labor market since the introduction of Barbie are large and worth celebrating.
We have all heard of the prohibition era. Popularly, it refers to the period from 1920-1933 during which it was illegal to sell, transport, and import alcohol in the US. National prohibition was enacted by the 18th amendment and repealed by the 21st amendment. That’s the basic picture.
But did you know that there were state alcohol prohibitions prior to the national one? In fact, there were 3 major waves of state alcohol prohibitions. The first was in the 1850s, the 2nd was in the 1880s, and then the 3rd preceded the 18th amendment. The image below illustrates the number of states that had statewide dry policies. You can see the first two waves and then the tsunami just prior to 1920.
Economist Writing Every Day 