From the Maine Woods

No big post this week, I’m in the Maine Woods without reliable internet or electricity.

The one economics angle to all this is that like many seemingly ancient Maine forests, the one I’m in used to be a farm. Notice the barbed wire running right through the middle of a huge old tree; the farm was abandoned so long ago that the tree had time to grow that big around it.

Why was the farm abandoned? Maine is cold and our soil is rocky, so agriculture tends to be unproductive relative to the Midwest. Many people left their farms in the late 1800s and early 1900s for new land in the West or, more commonly, manufacturing jobs in the cities. Maine used to be half farms, but now its land is 90% forest.

Job Lock is Still Here

Most Americans are covered by employer-sponsored health insurance, either through their own job or a family member’s. This can make it difficult to switch jobs- the new job might not offer insurance, or might have a worse insurance plan or network- locking people into their current job.

Economists have documented since at least the 1980’s how our insurance system seems to reduce job mobility. Several reforms have tried to improve the situation- COBRA, HIPAA, and most recently the Affordable Care Act.

In a paper published this week, Gregory Colman, Dhaval Dave and I evaluate how the extent of “job lock” has changed over time. In short, we find that job lock remains substantial and the Affordable Care Act doesn’t appear to have done anything to improve the situation. The paper has many tables of regression results, but the pictures tell the basic story:

Trends in job mobility for those with and without employer-sponsored insurance (ESI) using Current Population Survey data

The details differ a bit depending on which dataset and identification strategy we use, but a few things are clear:

  1. Macroeconomic factors are dominant in the short run; mobility falls during recessions like 2001 and 2007, then recovers.
  2. The long run trend has been toward lower job mobility for those with AND without employer-based insurance
  3. Those without employer-based insurance are still much more likely to switch jobs (we find 25-45% more likely)
  4. To the extent that this gap has closed since the year 2000, it has come through falling job mobility for those without employer-based insurance more than rising job mobility for those with employer-based insurance

Why does the Affordable Care Act appear not to have improved things? This remains unanswered, but we conclude the paper with some hypotheses:

In fact, our point estimates suggest that job lock actually got stronger following the ACA. One possible explanation for our finding is that the ACA’s individual mandate made insurance even more desirable by fining the uninsured. Another possibility is that workers continue to value employer-provided health insurance more over time as premiums continue to rise

Notes on Austin and Health Economics

I was in Austin Texas for the first time this week for the first in-person meeting of the American Society of Health Economists since 2019. Some quick impressions on Austin:

  • Austin reminds me of many Southern cities, but Nashville most of all. Both historic state capitals that are booming, lots of people moving in and new infrastructure actually being built, forests of cranes putting up new glass towers. Both filled with bars, restaurants, and especially live music. But even with so much happening and so much being built, they don’t *feel* dense, you can always see lots of sky even downtown.
  • Austin seems to be a bizarre “pharmacy desert”, I think I walked 14 miles all through town before I saw one. Contrast to NYC with a Duane Reade on every block. In fact downtown seemed to have almost no chains of any kind, restaurants included; I wonder if this is just about consumer preferences or there’s some sort of anti-chain law.
  • Good brisket and tacos, as expected
  • Most US cities have redeveloped their waterfronts the last few decades to make them pleasant places to be, but Austin has done particularly well here, many miles of riverfront trails right downtown.

Thoughts from the conference:

Continue reading

Irish Superman: 4 Weeks of Potatoes

Back in May I mentioned that a study was recruiting participants to try a 4-week all-potato diet. What I didn’t say was that I was joining the study, and I finished this week.

I’m glad I did it; I lost 8 pounds and 2 inches of waistline, going from slightly overweight (BMI 26) to just barely not-overweight (BMI 24.9). Here are some of my notes:

Day 5: Energy boost kicked in today. Feel half my age

Day 6: Potato energy going strong. Feel like Irish Superman

Day 15: Almost too much energy, hard to sit down at a computer and work, took a break to play basketball

So like many people who previously tried this, I can add more anecdotal evidence of weight loss (despite eating all the potatoes you want) and energy. I’ll also echo people who said that “hunger feels different” and not as demanding, and that it “resets your tastebuds” so that previously bland foods taste good (I just had a turnip with zero seasoning and it was almost too intense). Now to answer your likely questions:

Q: Did you actually eat nothing but potatoes for 4 weeks?

A: No, but I got reasonably close. I cooked potatoes in avocado oil and added seasonings, I drank coffee and beer, I ate other vegetables, I had some snacks. Overall I estimate I got 75-80% of my calories from potatoes.

Q: Was it hard to stick to? didn’t you get bored?

A: Being hungry or even bored weren’t really issues, all 5 times I slipped up and ate a meal that wasn’t potatoes I’d say it was for social reasons (I was at a party with great food, at a restaurant with someone, et c)

Q: What kinds of meals did you cook?

A: Lots of home fries and roast potatoes using lots of varieties of potato (russet, gold, red, purple, sweet). Mashed potatoes a few times. McCain’s craft beer fries for my birthday.

Q: Aren’t potatoes bad for you? Why didn’t this make you fat?

A: Anything can be bad for you if you deep-fry it, or otherwise smother it with fats or process it to death. This is probably how most potatoes get consumed in America, but they start as nutritious root vegetables.

Q: What about protein? Doesn’t this kill your gains?

A: This was my biggest concern going into the study. Potatoes do have more protein than I thought, enough to live on but probably not enough to make you strong. My lifts did come down a bit, though it’s unclear if that was due to the lack of protein or just the lower calories and weight loss taking some muscle along with the fat. I was eating high-protein yogurt many days to try to mitigate this.

Q: If this is so great, are you going to keep doing it?

A: No, it was great for the first 14-16 days then just ok. Most of the weight loss and energy boost happened in the first half. If I ever do this again I’m going to plan on two weeks, which I think is also what Penn Jillette suggests. I do think I’ll do potatoes for lunch a lot more often than I used to, and pivot this to a “whole foods / not-ultra-processed” diet.

Q: Is there something special about potatoes? Would any single-food diet work as well?

I’m not sure. Some of the benefit likely comes from cutting out variety, so not eating a lot just because “I need to try everything”. Some likely comes from cutting out specific categories of food, like high fat / high sugar / hyper-palatable. I don’t think that just any food would work, probably most whole foods would, but potatoes are cheap and nutritious. The potato diet leading to weight loss is consistent with many, though not all theories of obesity.

Q: Can I still sign up for the study?

A: No:

Signups are now closed, but we plan to do more potato diet studies in the future. If you’re interested in participating in a future potato diet study, you can give us your email at this link and we’ll let you know when we run the next study.

But you can always just do it yourself.

Is this the peak of inflation?

I think so, though the path back to 2% is a long one. Two months ago I wrote that “the Fed is still under-reacting to inflation“. We’ve had an eventful two months since; last Friday the BLS announced CPI prices rose 1% just in May, and that:

The all items index increased 8.6 percent for the 12 months ending May, the largest 12-month increase since the period ending December 1981

Then this Wednesday the Fed announced they were raising interest rates by 0.75%, the biggest increase since 1994, despite having said after their last meeting that they weren’t considering increases above 0.5%. I don’t like their communications strategy, but I do like their actions this month. This change in the Fed’s stance is one reason I think we’re at or near the peak.

Its not just what the Fed did this week, its the change in their plans going forward. As of April, the Fed said the Fed Funds rate would be 1.75% in December, and markets thought it would be 2.5%. But now the Fed and markets both project 3.5% rates in December.

The other reason I’m optimistic is that the days of rapid money supply growth continue to get further behind us. From March to May 2020, the M2 and M3 supply exploded, growing at the fastest pace in at least 40 years:

Rapid inflation began about 12 months later. But the rate of money supply growth peaked in February 2021, then began a rapid decline. Based on the latest data from April 2022, money supply growth is down to 8%, a bit high but finally back to a normal range. Money supply changes famously influence prices with “long and variable lags”, so its hard to call the top precisely. But the fact that we’re now 15 months past the peak of money supply growth (and have stable monetary velocity) is encouraging. Old-fashioned money supply is the same indicator that led Lars Christiansen to predict this high inflation in April 2021 after successfully predicting low inflation post-2009 (many people got one of those calls right, but very few got both).

Stocks also entered an official bear market this week (down 20% from highs), which is both a sign of excess money no longer pumping up markets, and a cause of lower demand going forward.

Markets seem to agree with my update: 5-year breakevens have fallen from a high of 3.6% back in March down to 2.9% today, implying 2.9% average inflation over the next 5 years. Much improved, though as I said at the top the path to 2% will be a long one- think years, not months. Even the Fed expects inflation to be over 5% at the end of this year, and for it to fall only to 2.6% next year.

What am I still worried about? The Producer Price Index is still growing at 20%. The Fed is raising rates quickly now but their balance sheet is still over twice its pre-Covid level and is shrinking very slowly. The Russia-Ukraine war drags on, keeping oil and gas prices high, and we likely still have yet to see its full impact on food prices. Making good predictions is hard.

While I’m sticking my neck out, I’ll make one more prediction, though this one is easier- Dems are in for a bad time in November. A new president’s party generally does badly at his first midterm, as in 2018 and 2010. But this time the economy will be a huge drag on top of that. November is late enough that the real economy will be notably slowed by the Fed’s inflation-fighting effects, but not so late that inflation will be under control (I expect it to be lower than today but still above 5%). Markets currently predict a 75% chance that Republicans take the House and Senate in November, and if anything that seems low to me.

Cost Plus Drugs

A new online pharmacy funded by Mark Cuban promises to sell prescription drugs at a fixed markup, 15% over cost plus a $3 flat fee. What’s the catch?

As far as I can tell, there are two- they only sell generics, and they don’t take insurance. But I think this will still save many people a lot of money.

The most expensive drugs get that way because they are sold by monopolies, almost always because they were invented less than 20 years ago and are still on-patent. But it’s still possible for older drugs to be sold at huge markups, as Martin Shkreli could tell you now that he’s out of prison (Shkreli’s case is supposedly what inspired Dr. Alex Oshmyansky to start this pharmacy). Sometimes you can still blame these markups on monopolies, just induced by the FDA instead of patents. But even for generic drugs with competitive manufacturing, you still sometimes see large and variable markups at the pharmacy level. So I think there’s still huge value in a pharmacy offering a low and stable markup on generics.

What about not taking insurance? First of all, lower cash prices obviously still benefit the 28 million Americans who don’t have health insurance. But even for those with insurance, it’s surprisingly common throughout health care to find cash prices lower than their copay. I have relatively good insurance but when I checked Cost Plus Drugs for the last two prescriptions my family got, I found that one was 80% cheaper than our copay (the other was about the same as our copay, so we’d only come out even, though we’d presumably save our insurer a lot).

Cost Plus Drugs originally wanted to also work through insurance as a Pharmacy Benefit Manager, but seems to have pivoted to being an “unPBM” that just offers generics to employers to supplement their existing plans. They also want to manufacture some of their own drugs, which seems on track to happen. They were started as a Public Benefit Corporation, so while they are for-profit this lends credibility to the idea that they really do want to keep prices down, not just start with low prices to make a name for themselves. Anyway, this seems like a worthy experiment and I encourage anyone with an expensive prescription to see if you can get it cheaper here.

Sick of high drug prices? Try some low-price anti-nausea mediation

Unfashionable Investing

Investors such as mutual funds, index funds, and hedge funds tend to pick a particular strategy or asset type and stick with it. It’s what they know, it’s what they’re known for, and making major changes would often create legal difficulties; something marketed as a bond fund can’t suddenly switch to stocks even if they think stocks would do much better. Other types of investors like pension funds, endowments and individuals have more flexibility to change their strategies. These investors tend to chase performance, allocating to types of investments that have performed well recently. This can create fashions, types of investment strategies that become more popular for a few years.

These strategies might involve focus on a certain asset class (stocks / bonds / commodities / private equity / real estate / et c), a certain sector or region within an asset class, a certain factor (value, growth, momentum), et c. It seems like institutional incentives, trend chasing, and FOMO lead people and institutions to over-allocate to strategies that have been successful the last 1-5 years and under-allocate to those that haven’t. Everyone sees something has recently been successful, so they pile into it, which drives up prices and makes it look even more successful for a while; but eventually this drives things to be so clearly over-valued that there’s a crash, and the crash scares people away for years until it becomes clearly undervalued. Most recently 2020-2021 saw people pile into growth/tech stocks and alternatives like SPACs/crypto, but the beginning of Fed rate hikes was the signal that the party is over and people (over?)react by pulling out.

Given this, the ideal strategy is to show up right before the party starts, then leave right at the peak; but no one can time it that well. The possibly realistic alternative is to show up early when no one’s there, then leave right when the party’s getting good (Punchbowl Capital?). Timing and identifying which strategies are too hot and which cold enough (Glacier Capital? Cryo Capital?) is the biggest practical question in how to pull this off. The simplest/dumbest way to do it is to avoid timing decisions entirely and just invest fixed proportions into all strategies; when they’re over-valued your fixed investment doesn’t buy many shares, when they’re under-valued it buys lots. This actually sounds like a decent way to go, but its more buying into the Efficient Market Hypothesis than beating it, can we do better? Here are the types of meta-strategies I’m planning to look into:

  • How variable is the timing of strategy boom/busts? Could you possibly just use fixed numbers of months/years- if a strategy’s been hot this long get out, if its been cold this long get in?
  • Use market share numbers, get in when something gets below a certain % of the market and out when it gets above
  • Use valuation numbers like P/E ratios (seems to work well for the overall stock market, may be harder to measure for some strategies/classes)
  • Flow of funds- is there a rate of change that works as a trigger?
  • Proportion of major institutions allocating to each strategy
  • What looks promising right now along these lines (May 2022)? Without looking at the numbers, the perennial strategies that have been out-of-favor a few years seem like value, emerging markets, and commodities (though commodities might be too hot again just now). These (along with real estate; right now homes seem expensive but homebuilders are cheap and I think commercial is too) all did well after the 2000 tech crash

I’m obviously not the first person to think along these lines; the concepts of the commodity cycle and Shiller’s CAPE are related, and Global Macro and Multistrategy funds do some of this. In the latest AER: Insights, Xiao Yan and Zhang echo Robert Shiller and Paul Samuelson that predicting big things like this is actually easier than predicting little things like the valuation of a specific stock:

Samuelson’s Dictum refers to the conjecture that there is more informational inefficiency at the aggregate stock market level than at the individual stock level. Our paper recasts it in a global setup: there should be more informational inefficiency at the global level than at the country level. We find that sovereign CDS spreads can predict future stock market index returns, GDP, and PMI of their underlying countries. Consistent with the global version of Samuelson’s Dictum, the predictive power for both stock returns and macro variables is almost entirely from the global, rather than country-specific, information from the sovereign CDS market

Ungated version here

But I haven’t actually heard of any fund focused on “unfashionable investing” that considers all asset classes and strategies like this. What institution out there would be capable of saying in 2021 “growth stocks are at bubbly levels, we’re switching to commodities”, or saying in 2022 “commodities are high and growth stocks crashed, we’re switching back”? Please let me know if such an institution does exist, or what else to read along these lines.

Get rich or get famous? Edward Thorp vs Myron Scholes

When finance professors publish papers claiming to find inefficiencies in asset markets, my initial reaction is skepticism. The odds are stacked against them to start since asset markets are mostly efficient. Then even if the inefficiency they found is real, shouldn’t they keep that fact to themselves and get rich trading on it?

But listening to a recent interview with Edward Thorp, I realized I shouldn’t entirely discount the possibility that someone would publish a real inefficiency, even a tradeable one. After all, Myron Scholes and Fischer Black did just that when they published the Black-Scholes model in the Journal of Political Economy. This made them famous on Wall Street and in econ/finance academia, and won Scholes the 1997 Nobel Memorial Prize in Economics.

Thorp explained that he had come up with a similar model years earlier, but instead of publishing it, he started a hedge fund and got rich. He says it makes sense that he didn’t share the Nobel Prize, partly because the Black-Scholes model was better than his, but mostly because you should need to publish and share your ideas with the world to get scientific credit for them; his prize was 20% annual returns at his hedge fund.

Why do some opt to get rich, and others to get famous? I’d say academics’ first instinct is to publish everything rather than put it into practice. But Thorp was also an academic, a math professor. Thorp was already famous for publishing a book about how to beat the house at blackjack by counting cards (which is what I knew him for before this interview), so perhaps he valued additional fame less. But he was also already rich from winning at blackjack and from book sales.

Putting ideas into practice can also bring up unanticipated difficulties. When Myron Scholes finally did start working at a hedge fund in 1994 he saw initial success, but by 1998 it had become an embarrassing blunder that inspired the book “When Genius Failed: The Rise and Fall of Long-Term Capital Management”. Scholes may have been better off sticking to academic fame.

Black-Scholes formula for options pricing. The Efficient Markets Hypothesis says that markets instantly incorporate all public information, but original research like this isn’t public until you publish it, and even then it can take years for market participants to fully incorporate it

Why Many Substance Use Treatment Facilities Don’t Take Insurance

According to the latest data, about one in four facilities doesn’t accept private insurance or Medicaid, and more than half don’t accept Medicare. This makes substance use treatment something of an outlier, since 91% of all US health spending is paid for through insurance. Still, there are many reasons to prefer being paid in cash: insurance might reimburse at low rates, impose administrative hassles, and generally try to tell you how to run things.

Providers generally put up with the hassles of insurance because they see the alternative as not getting paid. But if demand for their services gets high enough that they can stay busy with patients paying cash, they will often try going cash-only. Some try to generate high demand by providing excellent service. Sometimes high demand comes from a growing health crisis, as with opioids.

Demand can also be high relative to supply because supply is restricted. US health care is full of supply restrictions, but in this case I wondered if Certificate of Need laws were playing a role. As we’ve written about previously, CON laws require health care providers in 34 states to get the permission of a government board to certify their “economic necessity” before they can open or expand. But there’s a lot of variation from state to state in what types of services are covered by this requirement; acute hospital beds and long-term care beds are most common. 23 states require substance use treatment facilities to obtain a CON before opening or expanding.

States with Substance Use–Treatment CON Laws in 2020. Created using data from Mitchell, Philpot, and McBirney

How do these laws affect substance use treatment? We didn’t really know- only one academic article had studied substance use CON, finding it led to fewer facilities in CON states. But I’ve studied other types of CON, so I joined forces with Cornell substance use researcher Thanh Lu and my student Patrick Vogt to investigate. The resulting article, “Certificate-of-need laws and substance use treatment“, was just published at Substance Abuse Treatment, Prevention, and Policy. Here’s the quick summary:

We find that CON laws have no statistically significant effect on the number of facilities, beds, or clients and no significant effect on the acceptance of Medicare. However, they reduce the acceptance of private insurance by a statistically significant 6.0%.

Overall I was surprised that CON didn’t significantly affect most of the outcomes we looked at, and appears to be far from the main reason that treatment facilities don’t take insurance. Still, repealing substance use CON would be a simple way to improve access to substance use treatment, particularly since CON doesn’t appear to bring much in the way of offsetting benefits.

Going forward I aim to investigate how these laws affect health outcomes like overdose rates, and to dig more into the text of state laws and regulations to determine exactly what is covered by substance use CON in different states. As the article explains, we identified several errors in the official data sources we were using. This makes me worry there are more errors we didn’t catch, and there are certainly things the sources just don’t specify, like in which states the laws apply to outpatient facilities. So I hope we (or someone else) will have even better work to share in the future, but for now this article is as good as it gets, and we share our data here.

College Major, Marriage, and Children

The American Community Survey began in 2000, and started asking about college majors in 2009, surveying over 3 million Americans per year. This has allowed all sorts of excellent research on how majors affect things like career prospects and income, like this chart from my PhD advisor Doug Webber:

See here for the interactive version of this image

But the ACS asks about all sorts of other outcomes, many of which have yet to be connected to college major. As far as I can tell this was true of marriage and children, though I haven’t searched exhaustively. I say “was true” because a student in my Economics Senior Capstone class at Providence College, Hannah Farrell, has now looked into it.

The overall answer is that those who finished college are much more likely to be married, and somewhat more likely to have children, than those with no college degree. But what if we regress the 39 broad major categories from the ACS (along with controls for age, sex, family income, and unemployment status) on marriage and children? Here’s what Hannah found:

Every major except “military technologies” is significantly more likely than non-college-grads to be married. The smallest effects are from pre-law, ethnic studies, and library science, which are about 7pp more likely to be married than non-grads. The largest effects are from agriculture, theology, and nuclear technology majors, each about 18pp more likely to be married.

For children the story is more mixed; library science majors have 0.18 fewer children on average than non-college-graduates, while many majors have no significant effect (communications, education, math, fine arts). Most majors have more significantly more children than non-college graduates, with the biggest effect coming from Theology and Construction (0.3 more children than non-grads).

In this categorization the ACS lumps lots of majors together, so that economics is classified as “Social Sciences”. When using the more detailed variable that separates it out, Hannah finds that economics majors are 9pp more likely than non-grads to be married, but don’t have significantly more children.

I love teaching the Capstone because I get to learn from the original empirical research the students do. In a typical class one or two students write a paper good enough that it could be published in an academic journal with a bit of polishing, and this was one of them. But its also amazing how many insights remain undiscovered even in heavily-used public datasets like the ACS. We’ve also just started to get good data on specific colleges, see this post on which schools’ graduates are the most and least likely to be married.