The tax code is complex. That’s not news. The US federal tax code is also very progressive. Apart from that, the tax code pushes social or other policy goals. The Earned Income Tax Credit, for example, acts as a negative income tax and increases after-tax wages for those who can claim it. The idea is to incentivize earnings.
Economists tend to really like lump-sum taxes (in theory). But, despite the profession’s influence, almost nobody supports them. First, what is a lump-sum tax? It’s a tax that ignores any activities of the target. A per capita lump-sum tax would target the young, the old, the indigent, the working, the rich, the disabled… everyone. The idea is that no behaviors, aside from breathing, incur or disqualify a person from owing the tax.
Economists like them because they don’t change the relative price of labor and leisure. Whereas a marginal tax rate reduces a worker’s effective wage, a lump sum tax leaves it unaffected. People aren’t disincentivized from working/earning. Using jargon, we say that a lump-sum tax is non-distortionary.
In the simple two-good model of consumption and leisure, marginal tax rates reduce the amount of consumption that one can afford with each hour of work, making leisure relatively more attractive. Lump-sum taxes reduce the affordable amount of both leisure and consumption. Affording less leisure is the same as saying that people work more hours. It happens for two reasons. 1) Poorer people must work enough to pay the inevitable tax bill and also reach an income level of sustenance. However much work sustenance entails, it’s surely more when there is a tax. 2) Since working and earning itself is not taxed, people at all levels of income decide to work more because their after-tax wage is higher relative to the case of a marginal income tax.
At this point someone gets what I call the “French” idea. The French idea is that if we provide a lump-sum subsidy, then we can all leisure more and consume less – the opposite of a lump sum tax. What a life! We can avoid the prisoner dilemma problem where we can’t credibly commit to shirking together or actually taking a lunch. By forcing a lump-sum subsidy on everyone, we’d work a little less and do it voluntarily. We can sit outside a cafe, enjoying our coffee, baguette, and cigarette without having to worry about our neighbor with their “go get’em” attitude making us look bad.
The US Federal government has been considering major reforms like the REINS Act, which would require Congressional approval of major regulations proposed by executive branch agencies, or bringing back the “two in one out” rule from the first Trump administration. What would these do?
Right now it’s hard to say much for sure. But similar reforms have already been implemented in the states; as usual, the states provide a laboratory for investigating how policies work and whether they deserve broader adoption. It’s especially valuable to inform the debate over reforms like the REINS act that are still being considered at the federal level. Even for federal reforms that have already happened, it can be easier to evaluate the state version, since states make better control groups for each other than other countries do for the US.
But so far we’ve mostly been ignoring our laboratory results from recent state regulatory reforms. For instance, Broughel, Baugus, and Bose (2022) released a dataset that could be used to evaluate state regulatory reforms, but it has only been cited 3 times. This is why I’m adding this to my ideas page as a good subject for future academic research. Do state REINS or Red Tape Reduction Acts actually reduce either the stock or flow of regulation? If so, which types of regulations are affected, and does this have any effect on downstream measures like economic growth or new business formation?
Any research along these lines could help inform policy debates in the states, as well as for a new Presidential administration coming in with hopes of boosting economic growth through deregulation.
When you see prices from the past, especially the distant past, your normal reaction is perhaps one of envy or nostalgia. Take for example the Thanksgiving menu from the Plaza Hotel in New York in 1899. As you browse the menu, note that the prices are in cents, not dollars.
The most expensive items on the menu are only a few dollars, while many items can be had for around 50 cents. But hopefully your nostalgia will soon fade when you recall that wages were probably lower back then.
But how much lower?
According to data from MeasuringWorth.com (an excellent resource affiliated with the Economic History Association), the average wage for production workers in manufacturing was 13 cents per hour in 1899. From this we can immediately see that a dish such as Ribs of Prime Beef (60 cents) would take about 4.5 hours of work for a production worker to purchase.
How can we compare these prices and wages from 1899 to today?
In March and April of this year, I moaned and groaned here in blogland, chronicling my attempts to recover my funds from an interest-bearing account at crypto firm BlockFi.
Back in 2021, interest rates had been so low for so long that that seemed to be the new normal. Yields on stable assets like money market funds were around 0.3% (essentially zero, and well below inflation), as I recall. As a yield addict, I scratched around for a way to earn higher interest, while sticking with an asset where (unlike bonds) the dollar value would stay fairly stable.
It was an era of crypto flourishing, and so I latched onto the notion of decentralized finance (DeFi) lending. I found what seemed to be a reputable, honest company called BlockFi, where I could buy stablecoin (constant dollar value) crypto assets which would sit on their platform. They would lend them out into the crypto world, and pay me something like 9 % interest. That was really, really good money back then, compared to 0.3%.
On this blog, I chronicled some of my steps in this journal. First, in signing up for BlockFi, I had to allow the intermediary company Plaid complete access to my bank account. Seriously, I had to give them my username and password, so they could log in as me, and not only be able to withdraw all my funds, but see all my banking transactions and history. That felt really violating, so I ended up setting up a small auxiliary bank account for Plaid to use and snoop to their heart’s content.
BlockFi assured me that they only loaned my assets out to “Trusted institutional counterparties” with a generous margin of collateral. What could possibly go wrong??
What went wrong is that BlockFi as a company got into some close relationship with Sam Bankman-Fried’s company, FTX. Back in 2021-2022, twenty-something billionaire Sam Bankman-Fried (“SBF”) was the whiz kid, the visionary genius, the white knight savior of the crypto universe. In several cases, when some crypto enterprise was tottering, he would step in and invest funds to stabilize things. This reminded some of the role that J. P. Morgan had played in staving off the financial panics of 1893 and 1907. SBF was feted and lauded and quoted endlessly.
For reasons I never understood, BlockFi as a company was having a hard time turning a profit, so I think the plan was for FTX to acquire them. That process was partway along, when the great expose’ of SBF as a self-serving fraudster occurred at the end of 2022. FTX quickly declared bankruptcy, which forced BlockFi to go BK as well. SBF was eventually locked up, but so were the funds I had put into BlockFi. The amount was not enough to threaten my lifestyle, but it was enough to be annoying.
BlockFi Assets Begin to Thaw
I got emails from BlockFi every few months, assuring customers that they would do what they could to return our assets. Their bankruptcy proceedings kept things locked, but eventually they started to return some money.
As I noted in a blog post, in April, 2024, I was able to recover about 27% of my account. At the time, there was no clear prospect of getting the rest. Along the way, I clicked on a well-camouflaged scam email link, which gave me some heartburn but fortunately no harm came of it.
And now, hooray, they have finally returned it all, following their successful claw-back of assets from SBF’s organization(s). This vindicates my sense that the BlockFi management was/is fundamentally honest and good-willed, and was just a victim of SBF’s machinations.
Some personal takeaways from all this:
Keep allocations smallish to outlier investments
Sell out at the first serious signs of trouble
Triple-check before clicking on any link in an email
Having been forced to engage in opening crypto wallets and transferring coins, I have a better feel for the world of crypto which had seemed like a black box. It does not draw me like it does some folks, but if circumstances ever require me to deal in crypto (relocate to Honduras?), I could do it.
The COVID-19 pandemic, war in Ukraine, simmering US-China tensions, and rising global populism have led to globalization facing renewed attention-and criticism-from politicians and pundits across the political spectrum. Like any market phenomenon, the free movement of people, things, money, and ideas across natural or political borders is imperfect and often disruptive. But it has also produced undeniable benefits-for the United States and the world-that no other system can match. And it’s been going on since the dawn of recorded history.
The original essays compiled in this volume offer a diverse range of perspectives on globalization-what it is, what it has produced, what its alternatives are, and what people think about it-and offer a strong, proactive case for more global integration in the years ahead. Covering the basic economic and political ideas and historical facts underlying globalization, rebutting the most common arguments against globalization today, and educating readers on the intersection of globalization and our societies and cultures-from where we live to what clothes we wear and what foods we eat-Defending Globalization demonstrates the essential humanity of international trade and migration, and why the United States and the rest of the world need more of it.
It takes all of us to be rich. We need “a great multitude that no one could count, from every nation, tribe, people and language,” so to speak.
Two years ago, on Twitter, I summarized my contribution as follows, in the form of a dialogue:
Person from the Past: “So, how is it with 8 billion people?”
Me Today: “It’s bad. We have too many clothes.”
Person from the Past: “Right. With 8 billion you wouldn’t have enough clothes for everyone.”
Me Today: “Too many.”
I made it to the book launch event in D.C. near the Capitol.
Some people still have not heard of “fast fashion.” Maybe you heard it here first: New legislation is likely coming to regulate the clothing industry. It might start at the state level, in progressive places like California or Seattle. Demands include making information about supply chains more transparent and taxing the clothing companies in order to pay for trash disposal. For example, you can read about the New York Fashion Act. Similar to the way the food companies have to provide clear information about calories, clothing retailers might have to provide more information about chemicals, labor, and disposal issues.
Plastic fibers making new clothing cheap. I sometimes hate the flood of cheap products that American families are drowning in. Plastic products are so cheap to stamp out and give to kids. Some days you’ll find me grumpy about the latest bag of plastic swag and candy my kids came home with. There are some negative externalities to consuming tons of plastic items and tossing them out.
It’s a privilege to have this problem. Perhaps we are overindulging in clothing abundance and need some modern solutions to modern problems. We also need to figure out how to stop getting obese off of food abundance. (Hello, Ozempic.) But let’s still be grateful for the abundance, on this Thanksgiving week. My controversial take is that it’s good for the cost of clothing to be low. We don’t want to regress. We don’t want to make clothing scarce again.
If you were to want to cite my work on fashion and globalization, then you could use something like this:
Buchanan, Joy. “Fast Fashion, Global Trade, and Sustainable Abundance” (2024) In S. Lincicome, & C. Packard (Eds.), Defending Globalization: Facts and Myths about the Global Economy and Its Fundamental Humanity, Cato Institute, (pp. 367 – 380).
Public choice economists emphasize the process by which we select political leaders. Electoral and voting rules influence the type of leaders we get. Institutional economists agree and go one step further. Who we choose matters less than the environment we place them in. Leaders, regardless of their personal qualities, respond to the incentives that surround them. The ultimate policies, therefore, largely conform to those incentives. From this perspective, it’s important to adopt institutional incentives for leaders to promote policies oriented toward economic growth and provide the option to flourish.
The same principle applies to the private economy. Productivity is crucial, and higher IQ often correlates with greater productivity. Yet, genetic endowment—including IQ—is beyond individual control. Many other determinants of productivity are not exogenous when we can affect policy. Let’s adopt policies that allow individuals with lower IQ to act productively as if they had higher IQ. Protecting the freedom to contract and private property rights creates conditions whereby even those at the lower end of the cognitive ability distribution can thrive. These principles expand their opportunities. Market signals give them valuable feedback on their activities and enable them to contribute to the economy.
I offer a cleaned version of the state-level NSDUH in Stata .dta and Excel .xlsx formats here.
The NSDUH is mostly quite good as government datasets go- they share individual-level data in many formats and with the option to get most years together in a single file. But due to privacy concerns, the individual-level data doesn’t tell you what state people live in, which means it can’t be used to study things like state policy. SAMHSA does offer a state-level version of their data, but it is messy and only available in SAS format. So I offer the 1999-2019 state-level NSDUH Small Area Estimation Dataset in Stata .dta and Excel .xlsx formats here.
If you have Stata I recommend using that version, since the variables are labelled, making it much easier to understand what they represent.
This is the latest addition to my data page, where you can find cleaned/improved versions of other government datasets.
Farm Bureau has released their annual data on the cost of a Thanksgiving meal. The headline is that this meal has declined, in nominal terms, for 2 years in a row — back-to-back years of roughly a 5 percent decline. That’s good news for consumers. But they note it’s not all good news, because the meal is still 19 percent higher than 2019, “which highlights the impact inflation has had on food prices – and farmers’ costs – since the pandemic.”
However, the news is even better than they say. If we compare the price of this meal to median earnings, it is actually cheaper than it was in 2019. It’s now the second most affordable Thanksgiving on record, and the only lower year was 2020 — an anomalous year for many reasons (prices fell, due to decreased demand, while median wages were artificially lifted by lower-wage workers losing their jobs).
As a percent of median weekly earnings for full-time workers, the Farm Bureau Thanksgiving meal will cost just 5 percent of weekly earnings (note: I use 3rd quarter earnings for each year, since it is the latest available for 2024). In 2020 it was only 4.7 percent, but other than that 2024 is lower than all other years for which we have data, which goes back to the mid-1980s, when it took 6-8 percent of earnings to buy this meal.
Last year I also said that this meal was the second cheapest ever — if you ignore the weird years of the pandemic. But now if you ignore those years, it is the most affordable it has ever been.
That’s something to be thankful for next week, but also every time you go to the grocery store. Since October 2019, average wages have increased more than prices at the grocery store — not by much, but still better than you might suspect (and yes, I have checked my receipts). If we go back to the 1980s, wages beat inflation by a much larger margin.
It seems to be an accepted fact that there is a momentum effect with stock prices: a stock which has done well over the past 6-12 months is likely to continue to do better than average over the next six months or so. A number of funds (ETFs) have been devised which try to take advantage of this factor.
On the other hand, sometimes trends reverse, and stock that was hot twelve months ago has now run up in price, and may be due for a pause.
Here we will compare several momentum ETFs against the plain S&P 500 fund, SPY. In order to make it an apples-to-apples comparison, I am looking mainly at momentum funds that primarily draw from the S&P 500 large cap universe of stocks, excluding small-cap or tech only funds. [1] These large cap momentum funds are MTUM, JMOM, and SPMO. These funds all select stocks according to various rules. Besides trying to identify stocks with raw price momentum, these rules typically aim to minimize risk or volatility. I added one outlier, GMOM, that is very diversified. This fund does not hold individual stocks. Rather, it draws on some 50 different ETFs, including funds that focus on fixed income, commodities, or international or small cap as well as large cap US stocks, seeking to hold funds that show good relative momentum.
A plot of total returns over the past three years for these funds is shown below. It can be seen that plain SPY (orange line) beat all of the momentum funds except for SPMO (green line) in this timeframe. This is partly explained by the fact that SPY itself is a sort of momentum fund: the more a given stock’s price goes up, the bigger its representation in this capital-weighted fund. Also, over the past ten years or so, simply the biggest companies (the big tech quasi-monopolies like Google, Microsoft, etc.) have been generating more and more earnings, leaving the traditional auto and oil companies and banks, etc., in the dust.
By not focusing on U.S. large cap stocks, the diversified GMOM (marked with purple highlighter line) is less volatile. Its price did not drop nearly as much as the other funds in 2022, but it missed out on the great 2023-2024 stock run-up. SPMO (marked with green highlighter) really took off in that 2023-2024 big tech fiesta, by virtue of being concentrated in stocks like Nvidia, which went up roughly 10X in this timeframe. But this outperformance may be something of a one-off lucky strike. SPMO is still about the best of the momo funds, normally at least keeping up with SPY, but it does not consistently outperform it.
The five-year plot below illustrates similar trends, though it is a bit harder to read. Again, SPMO (green highlighter) largely keeps up with SPY, with a big outperformance spurt at the end. And GMOM is pretty flat; that really hurt it in the big 2020-2021 runup of big tech stocks. Over this five-year timeframe, JMOM kept up with SPY, and actually edged a bit ahead. MTUM, like most of the stock momo funds, actually ran ahead of SPY in the 2020-2021 runup, but fell somewhat more in 2022, and then got left in the dust in 2023. It is likely that it fell prey to trend reversal, which is a constant hazard for momentum funds. For most of 2022, the “best” stocks were dull value stocks, while tech stocks did terribly. Thus, a plain momentum algo fund would come into 2023 loaded with non-tech stocks. I suspect that is what happened to MTUM.
It happens that the SPMO algo has features that try to protect it from loading up on non-growth stocks during a bear market. So, it seems to be the best general momentum stock fund. It selects stocks which have shown positive momentum over the past twelve months, with the most recent month excluded (so as not to discriminate against a stock which had a temporary drop). Its chief vulnerability is that it only updates its holdings once every six months (mid-March and mid-September), so it is often acting on very old information. (Supposedly, it is better to update a momentum fund every three months).
How does SPMO compare to a top actively-managed fund like FFLC or plain growth stock fund SCHG? The three-year plot below shows that FFLC (blue line, 63% total return) beat SPMO (green line, 50% return). Although SPMO had an impressive surge in the past year, FFLC just kept steadily outperforming SPY over the whole three-year period. This suggests that having good human judgement at the helm, able to adapt to differing market environments (2022 bear vs. 2023-2024 tech bull) can do better than a single, focused algorithm. I prefer a fund which keeps steadily outperforming “the market” (i.e., S&P 500) rather than one which only occasionally has moments of glory, so I hold more FFLC than SPMO.
In the plot above, the growth fund SCHG suffered more in 2022 when the tech high-flyers fell to earth, but made up for it in 2023-2024, to end up matching SPY over three years. On longer time-frames, SCHG handily beats SPY, as we noted in an earlier article on growth stocks.
[1] See this Insider Monkey article for a listing of ten best U.S. stock momentum funds. Some of these focus on small cap, mid cap, or technology stocks.
Can they all be true? No, but also yes. At least, that’s where my thinking is at the moment. What all of the preceding pieces seem to do is acknowledge the benefits and costs of remote work while also emphasizing any one particular cost or benefit that generates their preferred lede.
Remote workforces bring lower labor efficiency (e.g. more distractions, less monitoring, more shirking) and greater labor flexibility (e.g. larger labor pools to select from, faster labor turnover) to a firm or industry. It gives firms the ability to pay employees in something other than money, such as schedule flexibility and locational choice, capturing some of the rents from those compensating wage differentials for themselves in the form of lower labor costs. It also means firing and hiring people with greater efficiency and lower costs when each subsequent wave of technological obsolescence hits, more effectively curating your labor force to fit the newest technological opportunities and needs.
How this story plays out will be bespoke to every firm, industry, and sector, but one broad trend I’m looking for is how industries separate by technological turnover. Industries differentiate by the historical rate of technological upheaval. Construction is different today than it was 25 years ago, but that amount of change is almost trivial compared to the televison entertainment industry. I expect that firms industries that reward “nimbleness” in the adoption of and adaption to new technologies will embrace work from home in far greater numbers. This will, in turn, shrink the “periodicity” of industry business cycles. Industires with high remote work labor forces will both more quickly collapse to a dominant set of firms when excludable technology gives them and advantage. They will also, however, more quickly reinflate to a more competitive landscape from new firm entry enter as remote work allows rivals to rapidly update their labor force to match the newest technological landscape. I expect applied micro work on remote work preferences and theoretic work on the consequences of search costs for competition to find each other atop the empire state building and yield the kind of policy recommendations that would make Nora Ephron proud.
This is just one of many broad trends to look for as remote work evolves. The complexity of interacting forces makes forecasting both a fool’s errand and palm reading. All of the forecasts will be internally logical, collectively incompatible, partially correct, and completely wrong.
Economist Writing Every Day 