Economics

Is the Monster Jobs Report Just a Head-Fake?

Scott Buchanan2 Comments

Financial markets have sustained themselves for nearly two years now on the hope that within 1-2 quarters, the Fed will finally relent and start lowering interest rates. This hope gets dashed again and again by data showing stubbornly persistent high employment, high GDP growth, and high inflation, but the hope refuses to die.

Long-term interest rates had been falling nicely for the last month, based on expectations of rate cuts in the fall. Then came Friday’s jobs report, and, blam, up went 10-year rates again.  The Bureau of Labor Statistics (BLS) published its “Establishment” survey of data gleaned from employers. Non-farm payrolls rose by US 272k.  This was appreciably higher than the 180k consensus expectation.

The plot below indicates that this number fits into a trend of essentially steady, fairly high employment gains (suggesting ongoing inflationary pressures):

There are fundamental reasons to take the BLS Establishment figures with a grain of salt. They have a history of significant revisions some months after first publication. Also, BLS uses a  “birth/death” model for small businesses, which can account for some 50% (!) of the job gains they report.  [1]

Another factor is that all of the net “jobs” created in recent quarters are reported to be part-time. According to Bret Jensen at Seeking Alpha, “Part-time jobs rose 286,000 during the quarter, while full-time jobs fell by just over 600,000. This is a continuation of a concerning trend where over the past year, roughly 1.5 million part-time positions were created while approximately one million full-time jobs were lost. This difference is that the BLS survey does not account for people working two or three jobs, which are now at a record as many Americans have struggled to maintain their standard of living during the inflationary environment of the past couple of years.”

It seems, then, that this week’s huge “jobs added” figure is not to be taken as indicating that the economy is overheated. However, it is still warm enough that rate cuts will be postponed yet again. A different BLS survey (“Household”) showed unemployment creeping up from 4.0% to 4.1%, which again suggests a more or less steady and fairly robust employment picture.

As far as drivers of inflation, I would look especially at wage growth. That is fitfully slowing, but not nearly enough to get us to the Fed’s 2% annual inflation target. My sense is that ongoing enormous federal deficit spending will keep pumping money into the economy fast enough to keep inflation high. High inflation will prevent significant interest rate cuts, assuming the Fed remains responsible. The interest payments on the federal debt will balloon due to the high rates, leading to even more deficit spending.  If we actually get an economic downturn, leading to job insecurity and a willingness of workers to accept slower wage growth in the private sector, the federal spending floodgates will open even wider.

This makes hard assets like gold look attractive, to hedge against inflating U.S. dollars. This is one reason China has been quietly selling off its dollar hoard, and buying gold instead.

[1] For more in-depth treatments of employment statistics, see posts by fellow blogger Jeremy Horpedahl, e.g. here.

Corporate Landlords Make Rent… Lower?

Zachary Bartsch6 Comments

Let’s keep it brief. Stick with me.

You know how perfect diversification means that one bears no idiosyncratic risk? That means that one is willing to pay more for some given return, driving up the price of assets included in such a diversified portfolio. That means that, without an informational advantage, index funds should place upward pressure on the price of assets that compose them. Anyone who invests in individual stocks, again without an informational advantage, would be priced out of the market because they bear idiosyncratic risk and would need to enjoy a risk premium that lowers the maximum price that they are willing to pay.

What about real estate?

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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.

How an All-U-Can-Eat Special Driven by a Controlling Investor Pushed Red Lobster Over the Edge

Scott Buchanan4 Comments

The Red lobster restaurant chain has historically positioned itself in what was hopefully a sweet spot between slow, expensive, full-service restaurants, and cheaper fast-food establishments. With its economies of scale, the Red Lobster franchise could engage in national advertising and improved supply contracts, giving it an advantage over small family-owned local restaurants.

The firm has been struggling for a number of years, caught between the quasi-upscaling of many fast-food chains, and the rise of fast-casual competitors like Chipotle. Also, seafood is more expensive to procure compared to chicken and beef, and the pandemic made a long-lasting dent in their revenues. That said, Red Lobster has been viable business for decades.

However, the firm has been adversely affected by financial engineering by outside companies. General Mills spun off Red Lobster to a company called Darden Restaurants in 1995. In 2014 Darden sold Red Lobster to a private equity firm called Golden Gate Capital for $2.5 billion. Golden Gate promptly plundered Red Lobster by selling its real estate out from under it. Instead of owning their own land and buildings, now the restaurants had to pay rent to landlords.  This put a permanent hurt on the restaurant chain’s profits. After this bit of financial engineering, the private equity firm in 2019 sold a 49% stake to a company called Thai Union. Thai Union bought out the rest of Red Lobster ownership from Golden Gate in 2020.

The Iron Fist from Outside

Thai Union is a huge seafood producer, which operates massive shrimp farms in Southeast Asia and sells a lot of shrimp to Red Lobster.
Although Thai Union initially said they would not interfere in the operations of Red Lobster, that’s not how it panned out.

An article by CNN author Nathaniel Meyersohn details how Thai Union took effective control of red lobster management decisions by 2022. Given the restaurant chain’s poor financial performance, it’s understandable that Thai Union would want to shake things up, but unfortunately the hatchet men they brought in appeared to have done more harm than good. Numerous off the record conversations agreed that the outside CEO was unnecessarily rude as well as incompetent. Knowledgeable Red Lobster veterans were driven out, and morale plummeted. Per Meyersohn:


Thai Union’s damaging decisions drove the pioneering chain’s fall, according to 13 former Red Lobster executives and senior leaders in various areas of the business as well as analysts. All but two of the former Red Lobster employees spoke to CNN under the condition of anonymity because of either non-disclosure agreements with Thai Union; fear that speaking out would harm their careers; or because they don’t want to jeopardize deferred compensation from Red Lobster…

Former Red Lobster employees say that while the pandemic, inflation and rent costs impacted Red Lobster, Thai Union’s ineptitude was the pivotal factor in Red Lobster’s decline.

“It was miserable working there for the last year and a half I was there,” said Les Foreman, a West Coast division vice president who worked at Red Lobster for 20 years and was fired in 2022. “They didn’t have any idea about running a restaurant company in the United States.”

At Red Lobster headquarters, employees prided themselves on a fiercely loyal culture and low turnover. Some employees had been with the chain for 30 and 40 years.  But as Thai Union installed executives at the chain, dozens of veteran Red Lobster leaders with deep knowledge of the brand and restaurant industry were fired or resigned in rapid succession. Red Lobster ended up having five CEOs in five years…

Former Red Lobster employees describe a toxic and demoralizing environment as Thai Union-appointed executives descended on headquarters and interim CEO Paul Kenny eventually took over the chain in 2022. Kenny, an Australian-born former CEO of Minor Food, one of Asia’s largest casual dining and quick-service restaurants, was part of the Thai Union-led investor group that acquired Red Lobster.

Kenny criticized Red Lobster employees at meetings and made derogatory comments about them, according to former Red Lobster leaders who worked closely with Kenny…

At the direction of Thai Union, Kenny became interim CEO, according to Red Lobster’s bankruptcy filing.

In the months after Kenny took over, Valade’s leadership team and other veteran leaders left. In July of 2022, the chief operations officer and six vice presidents of operations overseeing restaurants were abruptly fired shortly before Red Lobster’s annual general manager conference.

Kenny appointed a Thai Union frozen seafood manager, Trin Tapanya, as Red Lobster’s chief operations officer overseeing restaurants. Tapanya had no experience running restaurants. He did not respond to CNN’s requests for comment.

Other Thai Union representatives also became more closely involved across Red Lobster’s supply chain, finance, operations and strategy teams…Thai Union took a larger role in Red Lobster’s supply chain decisions, despite pledges in 2020 that it would not interfere.

Red Lobster had spent decades developing a wide array of suppliers to buy at competitive prices and mitigate the risks of becoming too reliant on any single supplier.

Thai Union blew that up.

Red Lobster employees say they were pressured by Thai Union representatives to buy more seafood from Thai Union. Thai Union representatives also began sitting in on meetings between Red Lobster and seafood suppliers, said one of the former Red Lobster employees who witnessed these conversations. Thai Union was the direct competitor of these other seafood suppliers, and suddenly had intimate access to their products, prices and strategy. “Our suppliers were really upset that [Thai Union representatives] were in those meetings with them,” this person said.

Red Lobster now claims that Thai Union pushed out other shrimp suppliers, “leaving Thai Union with an exclusive deal that led to higher costs to Red Lobster”.

The “Endless Shrimp” Disaster
The final blow to Red Lobster was offering an every-day special of all the shrimp you can eat. The firm had historically offered occasional all you can eat specials, to draw in first-time customers. But they had learned from a disastrous extended all you can eat crab special back in 2003, that if you are not very careful, you can lose a ton of money letting people eat all they want of an expensive food item.

Apparently, Thai Union pressured Red Lobster into offering an every-day “Endless Shrimp” special starting in June, 2023. Old guard Red Lobster management tried to push back, but were overruled. For Thai Union, this was of course a chance to sell more shrimp. But it led to huge losses on the part of Red Lobster. Internet personalities boosted their viewings by wolfing down plate after plate after plate of expensive shrimp:

The deal quickly went viral on social media. People started posting videos on Tik Tok showing how many shrimp they could eat. It became something of a challenge where people would try to eat as many shrimp as possible to gain social media clout. For example, a YouTuber called The Notorious Bob ate 31 plates of shrimp. Each plate has six shrimp so he ate 186 shrimp in total … another YouTuber called Sir Yacht stayed at Red Lobster for 10 hours and ate 200 shrimps throughout the day.

Red Lobster has now filed for Chapter 11 bankruptcy protection from its creditors, while it further downsizes to try to stay afloat. Thai Union has written down its investment in Red Lobster to the tune of $540 million, and its creditors now own the company.

The various actors in our current financial system played their usual roles here: General Mills spun off a non-core business; a private equity firm plundered its acquisition and then dumped it, presumably making gobs of money in the process for its partners; a supplier acquired a downstream company to develop a more integrated business line; a venerable American brand simply lost ground (think: Sears) in the competitive market place as tastes and competition changed over time, with vicious cost-cutting unable to save it.

This story is somewhat tragic, but I’m not sure there are any real villains, apart from the obnoxious outside CEO. Thai Union is a powerhouse seafood supplier, but they simply did not understand the American restaurant business and could not come up with a viable plan to fix Red Lobster. The now-unemployed restaurant workers may be victims, but the cooks and wait staff and store managers who worked extra hard, short-handed to keep serving their customers well despite horrible upper management – – to me, those are the heroes here.

Taxes & Unemployment – Know Your Bias?

Zachary Bartsch2 Comments

Say that there is a labor market and that there is no income tax. If an income tax is introduced, then what should we expect to happen? Specifically, what will happen to employment, the size of the labor force, and the number of people unemployed? Will each rise? Fall? Remain unchanged? Change ambiguously? Take a moment and jot down a note to test yourself.

As it turns out, what your answer is depends on what your model of the labor market is. Graphically, they are all quantities of labor. The size of the labor force is the quantity of labor supplied contingent on some wage that workers receive. It’s the number of people who are willing to work. Employment is the quantity of laborers demanded by firms contingent on to wage that they pay. Finally, the quantity of people unemployed is the difference between the size of the labor force and the quantity of workers employed (Assuming that the labor force is greater than or equal to employment).

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Prediction Markets As Investments

James Bailey4 Comments

Supporters of prediction markets tend to emphasize how they are great tools for aggregating information to produce accurate forecasts. If you want to know e.g. who is likely to win the next election, you can watch every poll and listen to pundits for hours, or you can take ten seconds to check the odds. This is great for people who want information- but how do prediction markets fare as investments for their actual participants?

Zero Sum

The big problem with prediction markets as investments is that they are zero sum (or negative sum once fees are factored in). You can’t make money except by taking it from the person on the other side of the bet. This is different from stocks and bonds, where you can win just by buying and holding a diversified portfolio. Buy a bunch of random stocks, and on average you will earn about 7% per year. Buy into a bunch of random prediction markets, and on average you will earn 0% at best (less if there are fees or slippage).

Low Liquidity

Current Kalshi order book for “Will June 2024 be the hottest June ever“. Betting $200 on either outcome could move the price by 5 cents (so move the estimated probability by 5pp).

This zero sum problem is close to inevitable based on how prediction markets work. They currently have one other big problem, though it is not inevitable, and is getting better as they grow: liquidity. There are some stocks and bonds where big institutions can buy or sell millions of dollars worth without moving the price. But in markets like Kalshi or PredictIt, I personally move prices often by betting just hundreds, or sometimes even just tens, of dollars. Buying at scale means getting worse prices, if you can even buy at all. PredictIt has a bet limit of $850 per contract for regulatory reasons. This definitely excludes institutional investors, but even for individuals it can mean many markets aren’t worthwhile. Say an outcome is already priced at 90 cents, the most you can make by betting it happens is about $94. That’s not nothing but its also not enough to incentivize lots of in-depth research, especially given the risk of losing the $850 if you are wrong and the opportunity cost of investing the money in stocks or bonds. Kalshi in theory allows bets up to $25k, but most of their markets haven’t had the liquidity to absorb a bet anywhere near that (though this could be changing).

Easy Alpha

Given these negatives, why would anyone want to participate in prediction markets, except to gamble or to generously donate their time to create information for everyone else? Probably because they think they can beat the market. Compared to the stock market, this is a fairly realistic goal. Perhaps because the low liquidity keeps out institutional investors, it isn’t that hard for a smart and informed investor to find mispricings or even pure arbitrages in prediction markets. This seems to be especially true with political prediction markets, where people often make bets because they personally like or dislike a candidate, rather than based on their actual chances of winning; that is exactly the kind of counterparty I want to be trading with.

I’ve been on PredictIt since 2018 and earned a 16% total return after fees; this was on hundreds of separate trades so I think it is mostly skill, not luck. Of course, even with this alpha, 16% total (not annual) return over 6 years is not great compared to stocks. On the other hand, I tended to put money in right before big elections and take it out after, so the money is mostly not tied up in PredictIt the whole time; the actual IRR is significantly better, though harder to calculate. On the other other hand, the actual dollar amount I made is probably not great compared to the time I put in. On yet another hand, the time isn’t a big deal if you are already following the subject (e.g the election) anyway.

Uncorrelated Alpha

The other big positive about prediction markets is that there is no reason to expect your returns there are correlated with your returns in traditional markets. Institutional investors are often looking for investments that can do well when stocks are down, and are willing to sacrifice some expected returns to get it. In fact, there may be ways to get a negative correlation between your prediction market returns and your other returns, hedging by betting on outcomes that would otherwise harm you. For instance, you can hedge against inflation by betting it will rise, or hedge against a recession by betting one happens. If you are right, you make some money by winning the bet; if you are wrong, you lose money on the bet but your other investments are probably doing well in the low-inflation no-recession environment.

Going Forward

Prediction markets have long been in a regulatory grey area in the US, but with the emergence of Kalshi and the current CFTC, everything may soon be black and white. Kalshi has won full approval from the CFTC for a variety of markets, but the CFTC is moving to completely ban betting on elections (you can comment on their proposal here until July 9th).

One great place to discuss the future of prediction markets will be Manifest, a conference hosted by play-money market Manifold in Berkeley, CA June 7-9th. It features the founders of most major US predictions markets and many of the best writers on prediction markets. I’ll be there, and as I write tickets are still available.

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.

Hotel Taxes and Quality: Why Georgia Sucks (Value)

Zachary Bartsch4 Comments

Every year my family travels from SW Florida to the mid Atlantic area. Without stops it takes 16-17 hours. With small children, it’s definitely a two day trip. We find that they handle it better if we leave super early, take a longer leg on the first day, then stop at a hotel midway and get the kids in the pool to help burn off some energy. We also rent a suite whenever possible.

We’ve made this trip many times. I use the Bonvoy app which is for Marriott hotels. We even have a particular hotel that we prefer: The Fairfield Inn in Santee, SC. It’s clean, spacious, the employees are welcoming and kind, the breakfast includes cooked items that aren’t bad, it’s within walking distance of a grocery store, and the price isn’t bad at all. Fairfield Inns are generally a great price per quality…. But not in Georgia.

I’ve stopped at several Fairfield Inns in GA: near Atlanta, near Savannah, and we’ve been disappointed. Every. Single. Time. All the margins on which the Fairfield in Santee is great are the same margins on which Georgia ones are poor. I’m sure that there is not just one reason. Maybe there is a bad regional manager or bad assistant to the regional manager. That’s not my primary hypothesis though.

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Childhoods of exceptional people

James Bailey2 Comments

Henrik Karlsson read lots of biographies of geniuses and tried to sum up the things their childhoods had in common here. Some highlights:

At least two-thirds of my sample was home-educated (most commonly until about age 12), tutored by parents or governesses and tutors. The rest of my sample had been educated in schools (most commonly Jesuit schools).

As children, they were integrated with exceptional adults—and were taken seriously by them.

They had time to roam about and relied heavily on self-directed learning

A common theme in the biographies is that the area of study which would eventually give them fame came to them almost like a wild hallucination induced by overdosing on boredom. They would be overcome by an obsession arising from within.

They were heavily tutored 1-on-1

An important factor to acknowledge is that these children did not only receive an exceptional education; they were also exceptionally gifted.

There is lots of discussion of John Stuart Mill and John Von Neumann, who each had major contributions to economics:

When they were done, James Mill took his son’s notes and polished them into the book Elements of Political Economy. It was published the year John Stuart turned fifteen….

There is a moving scene in John Stuart Mill’s biography, when John Stuart is about to set out into the world and his father for the first time lets him know that his education had been . . . a bit particular. He would discover that others his age did not know as much as he did. But, his father said, he mustn’t feel proud about that. He’d just been lucky.

Let’s make more people lucky.

Other nice posts along similar lines are Erik Hoel’s “How Geniuses Used to Be Raised” (linked in Karlsson’s piece), and Scott Alexander’s review of Laszlo Polgar’s book “Raise a Genius” (about raising his 3 daughters to be chess grandmasters). Karlsson’s post, worth reading in full, is here.