Author: Jeremy Horpedahl

The Only Analysis of the Pennsylvania Senate Debate That You Need To Read

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Last night the major party candidates for Senate in Pennsylvania had their first and only debate. I didn’t watch it, since I don’t live in Pennsylvania. But judging by my Twitter feed, a lot of people did watch it, including (bizarrely to me) lots of people who don’t live in Pennsylvania. And overnight, tons of articles were written analyzing the debate, saying who “won” the debate, and so on (“5 Things You Need to Know About the Pennsylvania Senate Debate” etc.).

But this blog post is the only thing you need to read about that debate. And these charts are really all you need to look at.

These two charts come from the prediction market website PredictIt. The charts show the “odds” (more on that below) that each candidate will win the Pennsylvania Senate race, over a 90-day time horizon (first chart) and the last 24 hours (second chart). What do we see? The Democratic candidate has been leading for the entire race up until a week ago, though with his odds falling gradually over the past month or two.

Notice though the big jump last night during the debate. The Republican candidate moved up from odds of about 57% to odds of about 63%, close to where it stands as I write (67%). Based on this result, it’s safe to say that the Republican candidate “won” the debate, though not so decisively that the election is now a foregone conclusion. You don’t need to wait for the polls, which have consistently showed the Democratic candidate in the lead (though with the gap closing in recent weeks) — though of course, these betting odds could change as new polling data is released.

But where do these odds come from?

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Mortgage Affordability Since 1971

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Mortgage interest rates are climbing quickly, while housing prices are still mostly high. These factors combined means that it is much more expensive to buy a home than in the recent past. But how much more expensive? And how does this compare with the past 50 years of history?

The chart below is my attempt to answer those questions. It shows the number of hours you would need to work at the average wage to make a mortgage payment (principal and interest) on the median new home in the US.

My goal here was to provide the most up-to-date estimate of this number consistent with the historical data. Thus, I had to use average wage data rather than median wage data, since the median hourly wage data is not available for 2022 yet. But as I’ve discussed before, while median and average wages are different, their rate of increase is roughly the same year-to-year, so it would show the same trends.

The final point plotted on the blue line in the chart is for August 2022, the last month for which we have median home price data, average wage data, and 30-year mortgage rates. Mortgage rates are the yearly average (or monthly average in the case of August 2022).

You’ll also notice a red dot at the very end of the series. This is my guess of where the line will be in October 2022, once we have complete data for these three variables (right now only mortgage rates are available in October for the three series I am using). I’m doing my best here to provide as much of a real-time picture as possible, given that rates are rising very sharply right now, while still providing consistent historical comparisons. If that estimate is roughly correct, mortgage costs on new homes are now less affordable than any year since 1990.

What do you notice in the chart?

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“The central values of civilization are in danger”

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The Mont Pelerin Society was founded 75 years ago. The title of this post was the opening sentence of the Statement of Aims the new Society agreed upon. They had many concerns about what they considered “central values,” but primary among those concerns were the dangers related to market economies: “a decline of belief in private property and the competitive market” and “the growth of theories which question the desirability of the rule of law.”

How has the world done since 1947? It’s easy to point to the decline of communism and socialism, both in practice and as a dominant theory, as a victory for the goals of the Mont Pelerin Society. However, we might be concerned that in the non-communist world, economic freedom has declined even as communism has failed. Let’s dig a little deeper.

One source we can use is an extension of the Fraser Institute’s Economic Freedom of the World index. The primary index only extends back to 1970, but recently Lawson and Murphy have constructed a version of the index which goes all the way back to 1950 for some countries. As far as I’m aware, they haven’t yet perfectly mapped the pre-1970 index with the primary index that extends to the present, but I’ll make a quick comparison using the available data. The 1950 data brings us very close to the date of the first MPS meeting.

Here’s a list of countries relevant to the discussion at MPS in 1947. The list includes countries where attendees came from, as well as other countries of interest to the discussion, such as China and Russia (I’m using the list from Caldwell’s recent edited transcripts of the 1947 meeting). Caveat: this isn’t a chain-linked index, so the 1950 and 2020 numbers are perfectly comparable. Also, the 2020 number only includes Areas 1-4 of the index, since that’s what the pre-1970 data contains.

The table above should give us some optimism about the state of market economies in the world from the perspective of 1947. Not only have China and Russia, clearly improved their economic freedom scores, but all of the Western market economies have as well. Again, exercise caution in interpreting these, since it’s not a chain-linked index, and it excludes one area of economic freedom (regulation, which surely has grown substantially since 1947). Despite those cautions, the picture in 2020 looks pretty good compared with 1950.

But what of other liberal institutions? While the MPS statement of aims doesn’t specifically mention democratic institutions, the threat to democracy seems to clearly be a concern in 1947 (“extensions of arbitrary power” and “freedom of thought and expression”).

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Norway is a Wealthy Country (even after making the appropriate adjustments)

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I have previously wrote about living standards in Ireland, and how GDP per capita overstates typical incomes because of a lot of foreign investment.

This is not to say that foreign investment is bad — to the contrary! But standard income statistics, such as GDP, aren’t particularly useful for a country like Ireland.

Norway has a similar challenge with national income statistics, but a different reason: Oil. Norway has a very large supply of oil revenues relative to the size of the rest of its economy, and oil revenues are counted in GDP. But those oil revenues don’t necessarily translate into higher household income or consumption.

Using World Bank data, Norway appears to be very rich: GDP per capita in nominal terms was about $90,000 in 2021. Compare that with $70,000 in the US, which is a very rich country itself. Sounds extremely wealthy!

Of course, by that same statistic, average income in Ireland is $100,000. But after making all the proper adjustments, as we saw in my prior post, Ireland is right around the EU average in terms of what individuals and households actually consume.

What if we make similar adjustments for Norway?

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Millennials Have Caught Up to Boomers: Generational Wealth Update (2022q2)

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Last week I wrote about wealth growth during the pandemic, but my favorite way to look at wealth data is comparing different generations. Last September I wrote a post comparing Boomers, Gen Xers, and Millennials in wealth per capita at roughly the same age. At the time, Millennials were basically equal to Gen X at the same age, and we were a year short of having comparable data with Boomers.

What does it look like if we update the chart through the second quarter of this year?

I won’t explain all of the data in detail — for that see my post from last September. I’ll just note a few changes. We now have single-year population estimates for 2020 and 2021, so I’ve updated those to the most recent Census estimates for each cohort. Inflation adjustments are to June 2022, to match the end of the most recent quarter of data from the Fed DFA. We still have to use average wealth rather than median wealth for now, but the Fed SCF is currently in progress so at some point we’ll have 2022 median data (most recent currently is 2019, and there’s been a lot of wealth growth since then).

What do we notice in the chart? First, we now have one year of overlap between Boomers and Millennials. And it turns out… they are pretty much at the same level per capita! Millennials have also now fallen slightly behind Gen X at the same time, since they’ve had no wealth growth (in real, per capita terms) since the end of 2021 to the present.

But Millennials have fared much better in 2022 with the massive drop in wealth: about $6.6 trillion in total wealth in the US was lost (in nominal terms) from the first to the second quarter of 2022. None of that wealth loss was among Millennials, instead it was roughly evenly shared among the three older generations (Boomers hid hardest). This difference is largely because Millennials hold more assets in real estate (which went up) than in equities (which went way down). The other generations have much more exposure to the stock market at this point in their life.

You can clearly see that affect of the 2022 wealth decline if you look at the end of the line for Gen X. You can’t see the effect on Boomers, since I cut off the chart after the last Gen X comparable data, but they saw a big decline since 2021 as well: about 6% per capita, along with 7% for Gen X. Even so, Gen X is still about 18% wealthier on average than Boomers were at the same age.

Of course, even since the end of the second quarter of 2022, we’ve seen further declines in the stock market, with the S&P 500 down about 4%. And who knows what the next few months and quarters will bring. But as of right now, Millennials don’t seem to be doing much worse than their counterparts in other generations at the same age.

Wealth Growth During the Pandemic

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In the US wealth distribution, which group has seen the largest increase in wealth during the pandemic? A recent working paper by Blanchet, Saez, and Zucman attempts to answer that question with very up-to-date data, which they also regularly update at RealTimeInequality.org. As they say on TV, the answer may shock you: it’s the bottom 50%. At least if we are looking at the change in percentage terms, the bottom 50% are clearly the winners of the wealth race during the pandemic.

chart created at https://realtimeinequality.org/

Average wealth of the bottom 50% increased by over 200 percent since January 2020, while for the entire distribution it was only 20 percent, with all the other groups somewhere between 15% and 20%. That result is jaw-dropping on its own. Of course, it needs some context.

Part of what’s going on here is that average wealth at the bottom was only about $4,000 pre-pandemic (inflation adjusted), while today it’s somewhere around $12,000. In percentage terms, that’s a huge increase. In dollar terms? Not so much. Contrast this with the Top 0.01%. In percentage terms, their growth was the lowest among these slices of the distribution: only 15.8%. But that amounts to an additional $64 million of wealth per adult in the Top 0.01%. Keeping percentage changes and level changes separate in your mind is always useful.

Still, I think it’s useful to drill down into the wealth gains of the bottom 50% to see where all this new wealth is coming from. In total, there was about $2 trillion of nominal wealth gains for the bottom 50% from the first quarter of 2020 to the first quarter of 2022. Where did it come from?

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Grocery Prices and Wages, in the Short Run and the Long Run

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From the recent CPI inflation report, one of the biggest challenges for most households is the continuing increase in the price of food, especially “food at home” or what we usually call groceries. Prices of Groceries are up 13.5% in the past 12 months, an eye-popping number that we haven’t seen since briefly in 1979 was only clearly worse in 1973-74. Grocery prices are now over 20% greater than at the beginning of the pandemic in 2020. Any relief consumers feel at the pump from lower gas prices is being offset in other areas, notably grocery inflation.

The very steep recent increase in grocery prices is especially challenging for consumers because, not only are they basic necessities, if we look over the past 10 years we clearly see that consumer had gotten used to stable grocery prices.

The chart above shows the CPI component for groceries. Notice that from January 2015 to January 2020, there was no increase in grocery prices on average. Even going back to January 2012, the increase over the following 8 years was minimal. Keep in mind these nominal prices. I haven’t made any adjustment for wages or income! (If you know me, you know that’s coming next.) Almost a decade of flat grocery prices, and then boom!, double digit inflation.

But what if we compare grocery prices to wages? That trend becomes even more stark. I use the average wage for non-supervisory workers, as well as an annual grocery cost from the Consumer Expenditure Survey (for the middle quintile of income), to estimate how many hours a typical worker would need to work to purchase a family’s annual groceries. (I’ve truncated the y-axis to show more detail, not to trick you: it doesn’t start at zero.)

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The Cost of Raising a Child

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Raising kids is expensive. As an economist, we’re used to thinking about cost very broadly, including the opportunity cost of your time. Indeed, a post I wrote a few weeks ago focused on the fact that parents are spending more time with their kids than in decades past. But I want to focus on one aspect of the cost, which is what most “normal” people mean by “cost”: the financial cost.

Conveniently, the USDA has periodically put out reports that estimate the cost of raising a child. Their headline measure is for a middle-income, married couple with two children. Unfortunately the last report was issued in 2017, for a child born in 2015. And in the past 2 years, we know that the inflation picture has changed dramatically, so those old estimates may not necessarily reflect reality anymore. In fact, researchers at the Brookings Institution recently tried to update that 2015 data with the higher inflation we’ve experienced since 2020. In short, they assumed that from 2021 forward inflation will average 4% per year for the next decade (USDA assumed just over 2%).

Doing so, of course, will raise the nominal cost of raising a child. And that’s what their report shows: in nominal terms, the cost of raising a child born in 2015 will now be $310,605 through age 17, rather than $284,594 as the original report estimated. The original report also has a lower figure: $233,610. That’s the cost of raising that child in 2015 inflation-adjusted dollars.

As I’ve written several times before on this blog, adjusting for inflation can be tricky. In fact, sometimes we don’t actually need to do it! To see if it is more or less expensive to raise a child than in the past, what we can do instead is compare to the cost to some measure of income. I will look at several measures of income and wages in this post, but let me start with the one I think is the best: median family income for a family with two earners. Why do I think this is best? Because the USDA and Brookings cost estimates are for married couples who are also paying for childcare. To me, this suggests a two-earner family is ideal (you may disagree, but please read on).

Here’s the data. Income figures come from Census. Child costs are from USDA reports in 1960-2015, and the Brookings update in 2020.

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“Superabundance” Review

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Are resources becoming scarcer as world population increases and per capita consumption increases? Are basic goods becoming more expensive relative to wages in the face of potential resource shortages? These are some of the main questions that are addressed in the just released book Superabundance by Marian Tupy and Gale Pooley. The authors were kind enough to provide me with an advance copy, which is why I’m already able to review this book on its release date (I’m not really that fast of a reader).

The author take a very optimistic view of the issues surrounding those opening questions. Properly measured (one of the key tasks of their work), resources are becoming more abundant, not more scarce. And properly measured, almost all consumer goods are becoming cheaper relative to wages.

The authors use the approach of “time prices” throughout the book. They are not the first to use this approach. Julian Simon (their inspiration for this project) used it in various places in his work. William Nordhaus famously used it is in paper on the history of the price of lighting. And Michael Cox and Richard Alm have used the time-price approach in many of their writings, from the 1997 Dallas Fed annual report, to a full-length book a few years later, as well as updates to the original 1997 report. And if you follow me on Twitter, I like to use this approach too.

In short, “time prices” tell us how many hours of work it takes to purchase a given good or service at different points in time. How many hours would you have to work to buy a pound of ground beef? A square foot of housing? An hour of college tuition? It’s the superior method when you are looking at the price of a particular good or service over time, compared with a naïve inflation adjustment, which only tells you if the price of that good/service rose faster or slower than goods or services in general, not if it’s become more affordable. Inflation adjustments are really only useful when you are trying to compare income or wages to all prices, to see if and how much incomes have increased over time. Of course, which wage series you choose is important (and you need to have a consistent series over time, or at least the end points), but as the authors point out (which they learned from me!), if you looking at wages after 1973, the wage series you use doesn’t matter much. Median wages, average wages, wages of the “unskilled” — these all give you the same trend since 1973. We don’t have all of these back earlier (especially median wages), but there’s not much reason to believe they’ve diverged that much. And the authors also present their data using multiple wage series in many of the charts and tables.

What do the authors find?

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Are Teacher Salaries Held Back by “Bloat” in K-12 Schools?

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In the past 20 years in the US, per pupil spending in K-12 schools has increased by about 20%. That’s in CPI-U inflation-adjusted dollars. What’s the cause of this increase? Higher teacher salaries? Administrative bloat? Something else?

Here’s a chart you may have seen floating around the internet. It shows the growth in the number of employees at K-12 public schools.

This looks like a lot of administrative bloat! The source of the data is the National Center for Education Statistic’s Digest of Education Statistics, Table 213.10.

But hold on, here’s another chart, showing the percent of employees in each of these same categories.

The numbers don’t add up to 100% because I’ve left off a few categories (the biggest one is “support staff,” which was 30-31% of the total throughout the time period). But overall, this chart appears to show much less bloat. Instructional staff (including aides) were by far the biggest category of employees in both categories in both time periods. Administrative staff at the district level did grow, but only by 1 percentage point of the total.

What’s the source of this data? Well, it’s a little trick I played. The source is the National Center for Education Statistic’s Digest of Education Statistics, Table 213.10. It’s the exact same data.

How is this possible?

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