Author: Jeremy Horpedahl

Nintendo vs Nintendo: Time Prices of Video Games in 1986 and 2024

Jeremy HorpedahlLeave a comment

For decades one of the most popular Christmas gifts for kids (and often adults) has been video game systems. And Nintendo has long been a dominant player in this market: the original NES arguably launched the modern gaming market in 1986 (even though it wasn’t the first, it was the first blockbuster) and Nintendo’s latest offering, the Switch, is now the best-selling console ever in the US.

As we often ask on this blog: has it become more or less affordable for an average worker to buy this iconic Christmas gift (or even buy one for yourself)?

When it comes to the consoles themselves, the Switch and NES are, perhaps surprisingly, equally affordable. The original NES cost $90 in 1986, while the Switch costs $300 today. Average wages in late 1986 were $9/hour and they are about $30/hour today. So in both years, it took about 10 hours of work to buy the console (alternatively, it’s about 25% of median weekly earnings in both years).

But as any serious gamer will tell you, the individual game cartridges can cost as much or more than the console if you want to play a lot of games. For example, the games available in the 1986 Sears catalog ranged from $25-$30. To buy just the 10 games in that catalog would cost $275 — over 30 hours of labor at the average wage, or about 3 hours of labor per game.

Today there is a wider range of prices for games, but the most expensive Switch games are around $60, or just 2 hours of labor at the average wage. There are also plenty of games around $30, or just 1 hour of labor.

The challenge with the comparison is that video games today are much higher quality, challenging, and advanced in so many ways. Is there any way to make a more direct comparison?

Yes. Nintendo offers an annual subscription for $20 to Nintendo Switch Online. Included in the subscription is access to nearly every NES game, plus Super Nintendo and Gameboy games. Not only do you get the 10 games from the 1986 Sears catalog, but many dozens more. All for less than $1 hour of labor at the average wage.

In other words, for 30 hours of labor today (the time to purchase those 10 original NES games), you could buy about 46 years worth of subscriptions to Nintendo online. That’s almost a lifetime of video game play, with many more advanced games.

Economic Nostalgia: 1890s Edition

Jeremy Horpedahl2 Comments

You see a lot of nostalgia for the recent past. People pining for the simpler life of the 1950s, or claims that wages have stagnated since the late 1970s or early 1980s. I’ve tried to take these arguments seriously and respond to them, such as in a paper I wrote with Scott Winship and summarized in a blog post last June. But occasionally, you find really weird economic nostalgia, like for the 1890s. Yes, the 1890s, not the 1990s.

Here’s one example: a cartoon shared on social media of workers being oppressed in the 1890s, with the caption “the problem has only gotten worse.” That post received 2 million views on Twitter, possibly because many people are criticizing it, but it also has a lot of retweets and likes.

If it was just one semi-viral social media post from an anonymous Twitter account, we could easily dismiss it. But 1890s economic nostalgia has been coming from another important place lately: President Elect Trump. Of course he is nostalgic for the policies of the 1890s. But on occasion, Trump will say things like “Go back and look at the 1890’s, 1880’s with McKinley and you take a look at tariffs, that was when we were at our richest” (emphasis added).

Really, our richest in the 1890s? Can this be true? Are the anonymous socialist Twitter accounts correct? Let’s look at the data. But the answer probably won’t surprise you: your intuition is correct, we are much better off today than the 1890s, in almost every way of looking at it economically.

Continue reading →

House Prices and Quality: 1971 vs 2023

Jeremy Horpedahl12 Comments

Last week I did a comparison of “time prices” for several goods and services in 1971 compared with 2024. For almost all goods and services, it took fewer hours of work in 2023 to purchase them. In some cases, huge increases in affordability; air travel is 79% cheaper and milk is 59% cheaper, in terms of how much time an average worker needs to labor to pay for them.

There was one major exception though: housing. Especially the cost of buying a new home. Just using the median sale price of a home, the cost (in terms of hours of work) roughly doubled between 1971 and 2024. That’s not good!

Many who commented on the post mentioned that houses are much bigger today, and I noted that in the post but still claimed this is a worrying trend: “since 1971 you can’t really argue the quality improvements make up for the increase. Yes, houses are much bigger (about double in size), but that’s not clearly driven by consumer demand (more so by zoning and other laws). The 1971 house also had indoor plumbing (but maybe not air conditioning).”

Furthermore, housing is the largest expense for most families, both today and in 1971. In the early 1970s it was 30.8% of consumer spending, and in 2023 it was slightly higher at 32.9%. Given all this, it is worth investigating further.

First, let’s consider the size of a typical house. For most of the 1971 data, I will use this HUD report on new single-family homes. And I will use the similar Characteristics of New Housing report for 2023 (the latest year available) to compare.

Are houses bigger today? Yes, but not nearly enough to account for the decreasing affordability I showed in the previous post. In 1971, the median new home had 1,400 square feet of floor space. In 2023, it was 2,286. That’s a big increase (over 60%), but let’s now do the time-price affordability calculation, which I show in the table below.

Continue reading →

“Time Prices” Today Compared With 1924 and 1971

Jeremy Horpedahl4 Comments

I’ve written before on this blog about “time prices”: the amount of time it takes at a particular wage to buy a specific product. Time prices are especially useful for making historical comparisons of the real price of a good or service. Rather than adjusting historical prices for inflation (which only tells you whether they have increased faster or slower than average prices), time prices give you a real comparison of whether a good has become more or less affordable.

Antony Davies recently did a 100-year comparison of time prices for an average worker in the US. He compared prices in 1924 for several common food items, gasoline, electricity, movie tickets, airline tickets, an automobile, and several measures of housing costs to the best comparable thing in 2024. This following table shows his results:

You will notice a few things here. For the median worker, most things are much more affordable in 2024. Some things are dramatically so! For many items, the median worker in 2024 is similar to someone in the top 1% in 2024. Huge improvements in the standard living.

It will probably not surprise you that one major exception is housing. For renters, things are not obviously worse, but they are not better, depending on what size of city you are in (renters also have lower incomes, but that would be true in both time periods). However compared to the average home price, things look much worse in 2024. You can reasonably reply that the home is much larger and better quality in 2024 (as late as 1940, barely half of homes had complete indoor plumbing!), and this is all true. Still, an average house today is much better, but also much less affordable.

Despite the high cost of housing, the average worker today is much better off than 1924. It’s hard to deny it.

But what about more recent times? As a recurring meme likes to date it, what about since 1971?

Continue reading →

Don’t Let Nominal Prices Fool You (Thanksgiving Edition)

Jeremy Horpedahl1 Comment

When you see prices from the past, especially the distant past, your normal reaction is perhaps one of envy or nostalgia. Take for example the Thanksgiving menu from the Plaza Hotel in New York in 1899. As you browse the menu, note that the prices are in cents, not dollars.

The most expensive items on the menu are only a few dollars, while many items can be had for around 50 cents. But hopefully your nostalgia will soon fade when you recall that wages were probably lower back then.

But how much lower?

According to data from MeasuringWorth.com (an excellent resource affiliated with the Economic History Association), the average wage for production workers in manufacturing was 13 cents per hour in 1899. From this we can immediately see that a dish such as Ribs of Prime Beef (60 cents) would take about 4.5 hours of work for a production worker to purchase.

How can we compare these prices and wages from 1899 to today?

Continue reading →

Thanksgiving Meal Continues to Get Cheaper

Jeremy Horpedahl1 Comment

Farm Bureau has released their annual data on the cost of a Thanksgiving meal. The headline is that this meal has declined, in nominal terms, for 2 years in a row — back-to-back years of roughly a 5 percent decline. That’s good news for consumers. But they note it’s not all good news, because the meal is still 19 percent higher than 2019, “which highlights the impact inflation has had on food prices – and farmers’ costs – since the pandemic.”

However, the news is even better than they say. If we compare the price of this meal to median earnings, it is actually cheaper than it was in 2019. It’s now the second most affordable Thanksgiving on record, and the only lower year was 2020 — an anomalous year for many reasons (prices fell, due to decreased demand, while median wages were artificially lifted by lower-wage workers losing their jobs).

As a percent of median weekly earnings for full-time workers, the Farm Bureau Thanksgiving meal will cost just 5 percent of weekly earnings (note: I use 3rd quarter earnings for each year, since it is the latest available for 2024). In 2020 it was only 4.7 percent, but other than that 2024 is lower than all other years for which we have data, which goes back to the mid-1980s, when it took 6-8 percent of earnings to buy this meal.

Last year I also said that this meal was the second cheapest ever — if you ignore the weird years of the pandemic. But now if you ignore those years, it is the most affordable it has ever been.

That’s something to be thankful for next week, but also every time you go to the grocery store. Since October 2019, average wages have increased more than prices at the grocery store — not by much, but still better than you might suspect (and yes, I have checked my receipts). If we go back to the 1980s, wages beat inflation by a much larger margin.

Did Inflation Make the Median Voter Poorer?

Jeremy Horpedahl4 Comments

A new essay by J. Zachary Mazlish answers the title question in the affirmative: yes, inflation made the median voter poorer. The post is data-heavy, with lots of charts and different ways of slicing the data, which is great! But since I am called out by name (or rather, my evil twin, Jeremy Horpendahl), I want to respond specifically to the claim about my data, but also I’ll make a few broader points.

Here’s the Tweet of mine that he links to:

https://twitter.com/jmhorp/status/1854548669317455894

Regular readers will recognize the chart in that Tweet comes from an EWED post from April 2024. Mazlich says that my chart and others like it are “misleading for understanding the election because a) they compare wages now versus January 2020, rather than January 2021.”

Fair enough, but if you read my Tweet you will see that I am specifically responding to an NPR story which said, “if you look at the difference between what… groceries cost in 2019 and what it costs today, and what wages looked like in 2019 and today, the gap is really gigantic.” So, they are specifically using 2019 as a baseline in that story, and my chart specifically used that as the baseline too! That’s why I thought that chart was relevant.

It’s true, of course, that if you want to understand median voter sentiment about the Biden administration, you should probably start the data at the beginning of the Biden administration. But I was responding to the more general claim people make, that they are worse off than in 2019.

With that clarification out of the way, what does Mazlich’s broader post say?

Continue reading →

Big Win for Prediction Markets

Jeremy Horpedahl1 Comment

Last night was a big win for Trump, but it was also a big win for prediction markets. In January 2024, I suggested that one of the best ways to follow the election was by following prediction markets. That prediction turned out to be correct!

Before any polls had closed, prediction markets had Trump with about 60% odds of winning. That’s far from a sure thing, but it’s much better than many prediction models, which all had the race as basically a 50-50 toss-up with a very slight edge to Harris (though one simple model that I wrote about two weeks ago had Harris slightly losing the popular vote, a good call in hindsight). So going into the election results, you would have been more confident that a Trump win was a real possibility if you watched predictions markets

Last night after the results started coming in, the average over five different prediction markets from Election Betting Odds put Trump at over 90% odds by 11:00pm Eastern Time. By about 12:45am, he was already over 95%. These aren’t absolutely certain odds, but if you were watching the election night news coverage, they were still treating this as essentially a toss-up in the battleground states.

The Associated Press hadn’t even called Georgia, the second of the battleground states, by the time prediction markets were over 95% for the overall race! Decision Desk HQ, which is a very good source for calling races in real time, didn’t declare Trump the winner until 1:21am, when they called Pennsylvania (they also have a nice explanation of how they made the call). The AP didn’t declare Trump the winner until 5:34am, when they called Wisconsin.

Polymarket is the largest of the five markets in the Election Betting Odds average, and they are also a good source because they have markets for all of the battleground states (here’s the market for Michigan, which still hasn’t been called as of 11:30am on Wednesday by most news sources!). This table shows when the 90% and 95% thresholds were permanently crossed on Polymarket odds for each of the 5 early battleground states, in comparison with the DDHQ and the AP.

Notice that the 90% threshold consistently beats DDHQ by at least an hour (the one exception is North Carolina, where DDHQ called it very early — they are very good at what they do!). And the 90% threshold is consistently beating the AP by at least 3 hours.

None of this should be read as a criticism of the Associated Press. They should be cautious about predictions! But if you want to know things fast (or, before your bedtime in this case), prediction markets are clearly worth following.

How can prediction markets be so far ahead of media sources? Because there is a strong incentive to be right early: that’s how you make money in these markets! How exactly this is done is unclear, since the traders are all anonymous and we generally can’t ask them. But likely they are doing a similar analysis of counties results compared to the 2020 election, as DDHQ told us they did after the fact, just quicker (indeed, if you were watching news coverage, they were doing the same thing, just in an ad hoc way, and much more slowly).

Federal Spending in 2024 was $2.3 Trillion More Than 2019

Jeremy HorpedahlLeave a comment

In Fiscal Year 2019, the US federal government spent $4.45 trillion dollars. In Fiscal Year 2024, spending was $6.75 trillion, or an increase of $2.3 trillion dollars. If you adjusted the 2019 number for inflation with the CPI, it would only be about $1 trillion more. Where did that additional $2.3 trillion go?

It will probably not surprise you that most of the increase in spending went to the largest categories of spending. Historically these have been health, Social Security, and defense, but now we must also include interest spending (roughly equal in size to defense and Medicaid in 2024). Indeed, with these areas of spending, 72 percent of the increase is accounted for. Add in the next three functions, and we’ve already accounted for over 90 percent of the increase.

Importantly, most of these categories are outside of the annual federal budget process, meaning that Congress does not need to approve new spending each year (Congress could change them, just as it could change any law, but it’s not part of the annual budgeting process). The “mandatory” categories, as they are called in federal law, are shaded red. I’ve striped with red and blue the health and income security functions, because some of this is subject to the annual budget process, but most of it is not. For example, Medicaid is not subject to the budget process (biggest part of the “health” function) and SNAP is not subject to the budget process (a big part of income security — it is set by the Farm Bill, usually on a five-year cycle).

So, when we talk about the $2 trillion increase since 2019, or the roughly $2 trillion cuts that would be needed to balance the budget, keep in mind that most of this is not subject to the annual budget process. It would require Congress to consider them specifically to enact cuts — though some big categories, such as Social Security, would be automatically cut under current law once their trust funds are exhausted (coming up on about a decade for the Social Security Old-Age Trust Fund).

A Simple Presidential Election Model, Using Three Economic Variables

Jeremy Horpedahl2 Comments

As the presidential race finishes out the last two weeks, it’s clearly a close race. In the past I have recommended prediction markets, and right now these are giving Trump about 60% odds. There have been lately a few big bettors coming into the markets and primarily betting on Trump, so there has been speculation of manipulation, but even at 60-40 the race is pretty close to a toss-up.

Another tool many use to follow the election are prediction models, which usually incorporate polling data plus other information (such as economic conditions or even prediction markets themselves). One of the more well-known prediction models is from Nate Silver, who right now has the race pretty close to 50-50 (Trump is slightly ahead and has been rising recently).

But Silver’s model, and many like it, is likely very complicated and we don’t know what’s actually going into it (mostly polls, and he does tell us the relative importance of each, but the exact model is his trade secret). I think those models are useful and interesting to watch, but I actually prefer a much simpler model: Ray Fair’s President and House Vote-Share Models.

The model is simple and totally transparent. It uses just three variables, all of which come from the BEA GDP report, and focuses on economic growth and inflation (there are some dummy variables for things like incumbency advantage). Ray Fair even gives you a version of the model online, which you can play with yourself. Because the model uses data from the GDP report, we still have one more quarter of data (releasing next week), and there may be revisions to the data. So you can play with it (and one of the variables uses the 3 most recent quarters of growth), but mostly these numbers won’t change very much.

What does the model tell us?

Continue reading →