If you want to know how many pigs were killed in the United States yesterday, the USDA has the answer. But if you want to know how many humans were killed in the US this month, the FBI is going to need a year or two to figure it out. The new Real Time Crime Index, though, can tell you much sooner, by putting together the faster local agency reports:
Trends currently look good, though murders still aren’t quite back to pre-2020 levels.
In addition to graphing top-line state and national trends, the Real Time Crime Index also offers the option to download a CSV with city-level data going back to 2018. This seems like a great resource for researchers, worthy of adding to my page of most-improved datasets.
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.
I focus much of my investing energy in the “high yield” area, finding stocks that pay out highish yields (8-12%, these days). Unless the company really hits hard times and has to cut its payout, I know I will make those returns over the next twelve months. But with ordinary stocks, you cannot count on any particular returns. The price of any stock a year from now will be the earnings per share (which can be forecasted with some degree of accuracy) times the price/earnings ratio, which is largely dependent on the emotions (“animal spirits”, in the words of Keynes) of the millions of market participants. Will I find a “greater fool” to buy my Amazon stock in a year for 20% more than I paid for it??
I have never gotten really comfortable with that as an investing model, and so I have erred on the side of caution and generally held less than the recommended 60% or so of my portfolio in plain stocks. In hindsight, that was a mistake. Every $10,000 put into the plain, dumb S&P500 fund SPY twenty years ago has turned into roughly $200,000. One reason for my caution has been a steady stream of articles that always warn that stocks are overvalued; after going up so much in the past X years, surely returns will be poor for the next several years.
But I try to learn from my mistakes, and I am now forcing myself to hold more equities than I “feel” like. To support this hopefully rational behavior, I am paying more attention to articles that present bull cases for stocks. One author on the Seeking Alpha investing site who has been consistently and correctly bullish for the past two years is Lawrence Fuller. Here I will summarize his Oct 9 article with the tongue-in-cheek title Be Afraid, Be Very Afraid. (To read articles on Seeking Alpha, you may have to start a free account, where you just have to give them an email address; I use my secondary “junk” email for these sorts of applications, which tend to send a lot of junky (not malicious) notifications).
He first addressed the angst that says, “Stocks have already run up so much, they are due for a crash”, by means of this chart showing cumulative returns in preceding bull markets:
It is obvious that, compared to the average bull market, we are still in early innings with the present bull which started in Oct 2022.
Fuller also makes the case that the good news on earnings has spread recently from the so-called Magnificent Seven big tech stocks (Microsoft, Apple, Nvidia, etc.) to the broader market. This should serve to support further price rises in the broad indices:
The chart below, which shows a similar story, in terms of net income growth:
He concludes:
“It is also important to recognize that the valuation of the S&P 500 is far more reasonable when we exclude the exceedingly expensive Magnificent 7 and focus on the remaining 493. In fact, we don’t have the valuation problem that bears purport we have today. Hence, I advised investors to avoid the market-cap-weighted indexes and focus on equal weight or look at sectors that had been left behind during the bull market to date…Therefore, I suggest not succumbing to fear. Instead, focus on whether the weight of evidence suggests we should be in wealth accumulation mode or wealth preservation mode.”
In a follow-up article, Are You Worried About An Overvalued Market? , Fuller notes that small cap stocks (as defined by the Russel 2000 index, which is held by the IWM fund) are more reasonably valued than big tech, and so are likely to outperform over the next year.
Economic data will appear alarming due to hurricane impacts, but the economy is growing at 3% with strong corporate profit prospects and low recession risk.
Inflation is on track to fall to 1.8% by May, with real wage growth outpacing pre-pandemic trends. Future inflation is expected to remain stable at 2.1% over the next 30 years.
Market valuation is not as overvalued as feared, with potential for significant upside (up to 30% to 38%) based on corporate profit growth and falling interest rates.
Short-term market volatility is normal; long-term investors should embrace corrections for potential high returns as fundamentals remain strong.
The average 2-year return after a 10+% correction is 35%, meaning long-term investors should embrace corrections as wonderful buying opportunities. Buy with confidence in the face of any short-term market weakness, as long as you stick to your optimal personal asset allocation, based on your specific risk profile and financial needs.
This article has a number of interesting and informative sections, including on why cash flow/enterprise value is a better metric for assessing the valuation of a stock than price/earnings.
I am part of the exodus from Twitter to Bluesky. I still maintain my Twitter account, but do not post there. I do, however, still scroll both of my feeds on occasion. I am more optimistic for the future of Bluesky for a variety of reasons, not least of which is simply that it is improving with each week. The mechanics are excellent, there is far less garbage/noise/bots, and I never feel like I am party to anything with nefarious ambitions in the long or short run. There is a problem though.
It’s still kind of… boring. The echo chamber feeling at Bluesky is stronger, born almost exclusively of the selection effects of first and second movers from Twitter. I am rarely surprised on Bluesky, I never feel terribly challenged in an exciting way, unless you count the more frequent posting of squishy academic policy affirmations. There’s plenty of (warranted) election anxiety, but there’s no oppositional forces. There’s no tension.
Which is not to say Twitter is providing any of that in spades. Quite to the contrary, it’s a shell of its former self. The heaviest posters with the biggest followings have found plenty of reasons to stay, but for every big follower account, there were hundreds of medium sized accounts that pushed and pulled the conversation in interesting directions, providing both traction and the occasional surprise. A large share of medium accounts have abandoned ship, some moving to Bluesky, but far more have just dropped out of the medium entirely (apologies for the homonyms). There are interesting people left on Twitter, but they are inundated with bots, trolls, and milquetoast careerists only hanging around because they fell ass backwards into a couple thousand followers and feel too capital committed to move elsewhere. A once rich and diverse intellectual stew has been watered down into a thin broth of increasingly questionable nutritional value. And like a lot of spicy foods, I know I used to complain about the heat while I was eating it, but damned if I don’t miss it all the same.
Bluesky has passed the proof of concept. It works. It has value. Now we just need that final cohort to make the leap and bring the heat.
UPDATE ADDENDUM (10/22/24)
So THAT happened. Most people who are going to read it have already read it, but I did want to add two notes for future reference, both of which I tacked onto bluesky threads.
I’ll admit I didn’t expect the word “tension” to get parsed as “negative approbation” , trauma, horrific violence, or hate. I could blame this on the internet, but this is my fault. I’ve been around long enough I should have know better. There is a reason why I tend to write things that come off pre-emptively defensive or as if they are equivocating. I try to prevent misinterpretation, willful or earnest, of my words. I should have done a better job here.
People are rightfully protective of Bluesky as a space separate from Twitter. That said, there are definitely a lot of trolls and bots already in place trying to turning any discourse into a hatefest.
Some highlights from reading the book What is Real? The Unfinished Quest for the Meaning of Quantum Physics*
Page 9 “The godfather of quantum physics, Niels Bohr, talked about a division between the world of big objects, where classical Newtonian physics rules, and small objects, where quantum physics reigned.”
The book has some drama, much centered around Einstein’s rejection of the Copenhagen interpretation.
The title of Chapter 2 is so excellent: “Chap 2: Something Rotten in the Eigenstate of Denmark”
Pg 37 “But Max Born had discovered a piece of the puzzle that summer. He found that a particle’s wave function in a location yields the probability of measuring the particle in that location – and that the wave function collapses once measurement happens… The measurement problem had arrived.”
Pg 56 “Einstein rejected any violation of locality, calling it “spooky action at a distance” in a letter to Max Born.”
Pg 79 “By the end of the war, the Manhattan Project had cost the nation nearly $25 billion, employing 125,000 people at thirty-one different locations across the United States and Canada. Hundreds of physicists were called away from their everyday laboratory work … After the war ended, physics research in the United States never returned to what it was… Damned by their success … military research dollars poured into physics.”
Pg 82 “Research into the meaning of quantum physics was one of the casualties of the war. With all these new students crowding classrooms around the country, professors found it impossible to teach the philosophical questions at the foundation of quantum physics.”
Joy: The politics of physics in academia was interesting to me. I recommend this book to university economists on that merit alone.
Page 100 “the photons are deliberately messing with you”
Experimentalists take note, page 104 “The story that comes along with a scientific theory influences the experiments that scientists choose to perform”
Joy: Having no internet greatly slowed down the spread of the correct ideas. However, eventually, over the course of a few decades and with a few career casualties, the more correct information did seem to influence the consensus.
Joy: I’m used to economists having very basic and sometimes heated disagreements. One might say that issues in economics are a bit more subjective than a topic in the physical sciences. However, with quantum physics turning out to be so weird, there are also heated disagreements among the physicists.
An equivalent book for economics might be Grand Pursuit by Sylvia Nasar.
Pg 108: “Bohm’s theory had also appeared during the height of Zhdanovism, an ideological campaign by Stalin’s USSR to stamp out any work that had even the faintest whiff of a conflict with the ideals of Soviet communism.”
Pg 124: “This universal wave function, according to Everett, obeyed the Schrödinger equation at all times, never collapsing, but splitting instead. Each experiment, each quantum event… creating a multitude of universes…”
*Thanks to Josh Reeves and Samford University for buying me the book.
I don’t like to follow politics, much less politics in another country. Policy on the other hand? I’m always hooked.
Most of us have heard of President Javier Milei by now. He became Argentina’s president in December of 2023. Prior, he had been in charge of a private pension company, a university professor who taught macroeconomics, had hosted a radio show, and has written several books. See his Wikipedia entry for more.
What makes him worth talking about is that he appears a little… unique. He’s boisterous and rattles off economic stories and principles like he wants you to get up and do something about it. To anyone in the US, he looks and behaves like a weird 3rd-party candidate – sideburns and all. He’s different. Here he is bombastically identifying which government departments he would eliminate:
I’ve enjoyed the spectacle, but haven’t paid super close attention. I know that he is libertarian in political outlook, drops references to Austrian economists and their ideas by the handful, and doesn’t mince words. Here he is talking at the Davos World Forum (English & Dubbed).
So what?
Argentina has a long history of high inflation and debt defaults. Every president always says that they’ll fix it, and then they don’t. There have been periods of lower inflation, but they don’t persist. Among Milei’s stated goals was to end that cycle and bring down inflation. His plan was to substantially reign in deficit spending by eliminating entire areas of government. We’re now approaching a year since Milei took office, and I thought that I would check in. Below is the CPI for Argentina since 2018. As soon as Milei took office prices spiked, but have started coming down more recently. Similarly, the Argentine Peso has fallen in value by 50% since he’s taken office. Ouch!
Kalshi just announced that they will begin paying interest on money that customers keep with them, including money bet on prediction market contracts (though attentive readers here knew was in the works). I think this is a big deal.
First, and most obviously, it makes prediction markets better for bettors. This was previously a big drawback:
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).
This big problem just went away, at least for election markets (soon to be all markets) on Kalshi. But the biggest benefit could be how this improves the accuracy of certain markets. Before this, there was little incentive to improve accuracy in very long-run markets. Suppose you knew for sure that the market share of electric vehicles in 2030 would over 20%. It still wouldn’t make sense to bet in this market on that exact question. Each 89 cents you bet on “above 20%” turns into 1 dollar in 2030; but each 89 cents invested in 5-year US bonds (currently paying 4%) would turn into more than $1.08 by 2030, so betting on this market (especially if you bid up the odds to the 99-100% we are assuming is accurate) makes no financial sense. And that’s in the case where we assume you know the outcome for sure; throwing in real-world uncertainty, you would have to think a long-run market like this is extremely mis-priced before it made sense to bet.
But now if you can get the same 4% interest by making the bet, plus the chance to win the bet, contributing your knowledge by betting in this market suddenly makes sense.
This matters not just for long-run markets like the EV example. I think we’ll also see improved accuracy in long-shot odds on medium-run markets. I’ve often noticed early on in election markets, candidates with zero chance (like RFK Jr or Hillary Clinton in 2024) can be bid up to 4 or 5 cents because betting against them will at best pay 4-5% over a year, and you could make a similar payoff more safely with bonds or a high-yield savings account. Page and Clemen documented this bias more formally in a 2012 Economic Journal paper:
We show that the time dimension can play an important role in the calibration of the market price. When traders who have time discounting preferences receive no interest on the funds committed to a prediction-market contract, a cost is induced, with the result that traders with beliefs near the market price abstain from participation in the market. This abstention is more pronounced for the favourite because the higher price of a favourite contract requires a larger money commitment from the trader and hence a larger cost due to the trader’s preference for the present. Under general conditions on the distribution of beliefs on the market, this produces a bias of the price towards 50%, similar to the so-called favourite/longshot bias.
We confirm this prediction using a data set of actual prediction markets prices from 1,787 market representing a total of more than 500,000 transactions.
Hopefully the introduction of interest will correct this, other markets like PredictIt and Polymarket will feel competitive pressure to follow suit, and we’ll all have more accurate forecasts to consult.
You may have heard that there are roughly 7 million men of working age that are not currently in the labor force — that is, not currently working or looking for work. The statistic has been produced in various ways using slightly different definitions by different researchers, but the most well-known is from Nicholas Eberstadt who uses the age cohort of 25-55 years old and gets about 7 million (in 2015). More recently and perhaps more prominently is from Senator JD Vance, and as with almost all issues he has tied this to illegal immigration.
The 7-million-men statistic is true enough, and if we limit it to native-born American men with native-born parents (I assume this is the group Vance is concerned about), we can get right at 7 million non-working men in 2024 by expanding the age cohort slightly to 20-55 year olds.
Why are these men not working? According to what they report in the CPS ASEC, here are the reasons broken down by 5-year age cohort (I drop 55-year-olds here to keep the group sizes equal, which shrinks the total to 6.7 million men):
By far the largest reason given for not working is illness or disability, which is 42% of the total for all of these men, the largest reason for every age group except 20-24 (who are mostly in school if they aren’t working), and it’s the majority for workers ages 30-54 (about 56% of them report illness or disability). Slightly less than 10% report “could not find work” as the reason they weren’t working, which is about 650,000 men in this age group (and are native-born with native-born parents). And over half of those reporting that they couldn’t find work are under age 30 — for those ages 30-54, it’s only about 7% of the total.
More men report that they are taking care of the home/family (800,000) than report not being able to find work (650,000). And a lot more report that they are currently in school — almost 1.5 million, and even though they are mostly concentrated among 20–24-year-olds, about one-third of them are 25 or older.
It’s certainly true that the number of working age men in the labor force has fallen over time. In 1968, 97% of men ages 20-54 had worked at some point in the past 12 months (that’s for all men regardless of nativity, which isn’t available back that far in the CPS ASEC). In 2024, that was down to about 87%. But even if we could wave a magical wand and cure all of the men that are ill or disabled, this would add less than 3 million people to the labor force, not nearly enough to make up for all of the immigrants that Vance and others are suggesting have taken the jobs of native-born Americans.
Last week as the U.S. National Weather Service was warning Florida residents of incoming 120 mile/hour winds, folks in the U.K were informed by the BBC weather app that the wind speed outside was – – wait for it – – 18,995 mph. Hurricane force winds were displayed for many cities worldwide. Not to mention temperatures of around 400 F for Nottingham, New York, and Sydney.
The BBC of course issued retractions and apologies, blaming a third-party data supplier. Said supplier, meteorological forecasting company DTN has apologized for what it called a “technical error”.
I had my Brave browser do an AI search/summary on the topic, “What measures does the BBC take to prevent or mitigate the impact of third-party supplier issues on its weather forecasting app?”. Here is what it told me:
Based on the provided search results, the BBC takes the following measures to prevent or mitigate the impact of third-party supplier issues on its weather forecasting app:
Diversified data sources: The BBC uses multiple data suppliers, including MeteoGroup, to ensure that its forecasts are accurate and reliable. This diversification helps to reduce dependence on a single supplier and minimizes the impact of issues with a particular supplier.
Real-time updates: The BBC’s weather app provides real-time updates, which enable it to quickly respond to and correct any errors or inaccuracies caused by third-party supplier issues.
Error detection and correction: The BBC’s weather experts closely monitor forecasts and are able to detect and correct errors or inaccuracies caused by third-party supplier issues.
Technical redundancy: The BBC has implemented technical redundancy measures, such as backup systems and fail-safes, to minimize the impact of third-party supplier issues on its weather forecasting app.
Collaboration with other organizations: The BBC collaborates with other organizations, such as the Met Office and Scottish airline Loganair, to improve forecast accuracy and mitigate the impact of third-party supplier issues.
Investment in technology and AI: The BBC invests in advanced technology and artificial intelligence to improve the accuracy and reliability of its weather forecasts, reducing the reliance on third-party suppliers and minimizing the impact of issues.
Monitoring and maintenance: The BBC regularly monitors and maintains its weather forecasting app and underlying systems to identify and address potential issues before they become critical.
These measures demonstrate the BBC’s proactive approach to mitigating the impact of third-party supplier issues on its weather forecasting app, ensuring that users receive accurate and reliable forecasts.
That all sounds great in theory, but in practice something failed last week. I guess there is so far no fool-proof substitute for having a pair of human eyeballs look at material before it is flung out into cyberspace. But that might cost money.
Nobody is saying, but it would not surprise me if the BBC adds yet another layer of auto-checking to its robo-forecasts, to at least flag physically unrealistic numbers.
Robert Nozick and John Rawls were intellectual rivals, friendly colleagues, and even members of the same reading group. Their conversations, at least the ones we were privy to through their iterations of published work, were dedicated to reconciling the role of the state in manifesting the best possible world. Nozick, it can be said in a gratuituous oversimplification, favored a minimal government while Rawls, similarly oversimplified, favored a larger, wider reaching set of government institutions. Both were well aware of the risks and rewards of concentrating power within government institutions, they simply arrived at different conclusions based on risks each wanted to minimize versus those they were willing to incur.
My mental model of the evolution of government (influenced heavily by Nozick and refined towards the end by Rawls) goes something like this:
100,000 years ago roving bands of humans grow to thrive in their environment by solving collective action problems, largely through familial relations. Larger groups have more success hunting, foraging, and protecting themselves from predators.
Eventually some groups get so good at collective action that they begin to prey on other smaller groups. These “bandits” gain more through resources taken by force than they would strictly producing resources through hunting and foraging.
This creates an arms race in group size, with bigger groups having the advantage while facing the diminishing marginal returns imposed by difficultings in maintaining the integrity of collective action in the face of individual incentives to free ride i.e. its hard to get people to pull their weight when their parents aren’t watching.
Some groups mitigate these difficulties, growing larger still. At some threshold of group size, the rewards to mobilitity are overtaken by the rewards to maintaining institutions and resources (freshwater, shelter, opportunities for agriculture), leading to stationary groups.
These stationary groups begin to act as “stationary bandits”, extracting resources from both outsiders for the benefit of their group and from their members for the benefit of their highest status members.
Differing institutions evolve across groups, varying the actions prescribed and proscribed for leaders, members (citizens), and non-members. Some groups are highly restrictive, others less so. Some groups are more extractive, funneling resources to a select minority. Some groups redistribute more , others less.
Democracy evolves specifically as an institution to replace hereditary lines, a deviation from the familial lines that sat the origin of the state all the way back at step 1. Its correlation with other institutions is less certain, though it does seem to move hand-in-hand with personal property rights and market-based economies. Democracies begin to differentiate themselves based on the internal, subsidiary institutions they favor and instantiate.
A lot of my political leanings can be found not in favoring Nozick or Rawls, but in the risk immediately preceding their point of divergence. When I look at well-functioning modern democracies, I see an exception to the historical rule. I see thousands of years of stationary bandits voraciously extracting resources while high status members taking desperate action to maintain power in a world where property rights are weak and collective action is tenuous. Rawls saw a growing state as a opportunity to create justice through fairer, more equitable outcomes. Nozick saw a growing state as a further concentration of power that, no matter how potentially benevolent today, would eventually attract the most selfish and venial, leading to corruption and return to the purest stationary bandit, only now with the newfound scale.
Both strike as me as perfectly reasonable concerns about very real risks. Which do I believe the greater risk? Depends on the news and what I had for breakfast that day. In the current political context, both in the US and several other democracies, I am of the growing opinion they would be in broad agreement that the biggest risk is not the perversion of democracy from suboptimal policies and subsidiary institutions (step 7), but rather a disastrous reversion to the pre-democratic institutions (step 6).
The most underrated aspect of democracy may very well be its fragility. While historical rarity may not be undeniable evidence of inherent fragility, but it would certainly suggest that once achieved it is worth the overwhelming dedication of resources, including the sacrifice of welfare optimality, to ensure its perserverance.
It cost a lot to get here. A lot. Sacrifices that are hard to even conceive of, let alone empathize with, while living within the profound luxury of modern life. I have no doubt that many of us will find ourselves underwhelmed with the policy platforms of the full menu of viable candidates made available to voters at every level of national and local office in a few weeks. So take this little scribbling for exactly what it is: an argument to vote againstcandidatesthat reduce the probability of our constitutional republic remaining intact. By comparison, all the other differences add up to a historical rounding error.
Economist Writing Every Day 