BLS is slowly (actually, it probably feels very quick for those working on it!) catching up on data releases that were delayed during the federal government shutdown. This week, we saw the release of the November jobs report, which also includes data from October, even though there was no separate release for October. Well, kinda.
For the household survey (which is used to calculate the unemployment rate, among many other measures of the labor market), there is no October report. Because there is no data to be collected. Look at Table A in the employment situation report, and you will see no data in the column for October 2025. Look at the FRED page for the unemployment rate, and you will notice a gap in October. As I wrote a few weeks ago, this is not the end of the world, but it is rather sad for a gap to show up in a series that consistently ran for 933 months back to 1948.
So what is in the jobs report? Lots of new information. A few related areas that have gotten a lot of attention this week are the changes in federal government employment vs. private sector employment, and the changes in native-born vs. foreign-born employment.
Much of what economics has to say about tariffs comes from microeconomic theory. But it’s mostly sectoral in nature. Trade theory has some insights. But the effects on the whole of an economy are either small, specific to undiversified economies, or make representative agent assumptions that avoid much detail. Given that the economics profession has repeatedly said that the Trump tariffs would contribute to inflation, it seems like we should look at the historical evidence.
Lay of the Land
Economists say things like ‘competition drives prices closer to marginal cost’. Whether the competitor lives abroad is irrelevant. More foreign competition means lower prices at home. But that’s a partial equilibrium story. It’s true for a particular type of good or sector. What happens to prices in the larger economy in seemingly unrelated industries? The vanilla thinking that it depends on various elasticities.
I think that the typical economist has a fuzzy idea that the general price level will be higher relative to personal incomes in some sort of real-wages and economic growth mental model. I don’t think that they’re wrong. But that model is a long-run model. As we’ve discovered, people want to know about inflation this month and this year, not the impact on real wages over a five-year period.
Part of the answer is technical. If domestic import prices go up, then we’ll sensibly see lower quantities purchased. The magnitude depends on the availability of substitutes. But what should happen to total import spending? Rarely do we talk about the expenditure elasticity of prices. Rarely do we get a simple ‘price shock’ in a subsector. It’s unclear that total spending on imports, such as on coffee, would rise or fall – not to mention the explicit tax increase. It’s possible that consumers spend more on imports due to higher prices, or less due to newly attractive substitutes. The reason that spending matters is that it drives prices in other parts of the economy.
For example, I argued previously that tariffs reduce dollars sent abroad (regardless of domestic consumer spending inclusive of tariffs) and that fewer dollars will return as asset purchases. I further argued that uncertainty makes our assets less attractive. That puts downward pressure on our asset prices. However, assets don’t show up in the CPI.
According to the above discussion, it’s unclear whether tariffs have a supply or demand impact on the economy. The microeconomics says that it’s a supply-side shock. But the domestic spending implications are a big question mark.
What is a Tariff Shock?
That’s the title of a recent working paper from the Federal Reserve Bank of San Francisco. It’s a fun paper and I won’t review the entirety. They start by summarizing historical documents and interpreting the motivation of tariffs going back to 1870. They argue that tariffs are generally not endogenous to good or bad moments in a business cycle and they’re usually perceived as permanent. The authors create an index to measure tariff rates.
Here’s the fun part. They run an annual VAR of unemployment, inflation, and their measure of tariffs. Unemployment in negatively correlated with output and reflects the real side of the economy. Along with inflation, we have the axes of the Aggregate Supply & Aggregate Demand model. Tariffs provide the shock – but to supply or demand?. Below are the IRF results:
I said years ago on my Ideas Page that we need data and research on Benefit Cliffs:
Benefits Cliffs: Implicit marginal tax rates sometimes go over 100% when you consider lost subsidies as well as higher taxes. This could be trapping many people in poverty, but we don’t have a good idea of how many, because so many of the relevant subsidies operate at the state and local level. Descriptive work cataloging where all these “benefits cliffs” are and how many people they effect would be hugely valuable. You could also study how people react to benefits cliffs using the data we do have.
But it turns out* that the Atlanta Fed has now done the big project I’d hoped some big institution would take on and put together the data on benefits cliffs. They even share it with an easy-to-use tool that lets you see how this applies to your own family. Based on your family’s location, size, ages, assets, and expenses, you can see how the amount of public assistance you are eligible for varies with your income:
Then see how your labor income plus public assistance changes how well off you are in terms of real resources as your labor income rises:
For a family like mine with 3 kids and 2 married adults in Providence, Rhode Island, it shows a benefit cliff at $67,000 per year. The family suddenly loses access to SNAP benefits as their labor income goes over $67k, making them worse off than before their raise unless their labor income goes up to at least $83,000 per year.
I’ve long been concerned that cliffs like this in poorly designed welfare programs will trap people in (or near) poverty, where they avoid taking a job, or working more hours, or going for a promotion, or getting married, in order to protect their benefits. This makes economic sense for them over a 1-year horizon but could keep them from climbing to independence and the middle-class in the longer run. You can certainly find anecdotes to this effect, but it has been hard to measure how important the problem is overall given the complex interconnections between federal, state, and local programs and family circumstances.
I look forward to seeing the research that will be enabled by the full database that the Atlanta Fed has put together, and I’m updating my ideas page to reflect this.
*I found out about this database from Jeremy’s post yesterday. Mentioning it again today might seem redundant, but I didn’t want this amazing tool to get overlooked for being shared toward the bottom of a long post that is mainly about why another blogger is wrong. I do love Jeremy’s original post, it takes me back to the 2010-era glory days of the blogosphere that often featured long back-and-forth debates. Jeremy is obviously right on the numbers, but if there is value in Green’s post, it is highlighting the importance of what he calls the “Valley of Death” and what we call benefit cliffs. The valley may not be as wide as Green says it is and it may be old news to professional tax economists, but I still think it is a major problem, and one that could be fixed with smarter benefit designs if it became recognized as such.
Last week I wrote a fairly long post in response to an essay by Michael Green. His essay attempted to redefine the poverty line in the US, by his favored calculation up to $140,000 for a family of four. That $140,000 number caught fire, being covered across not only social media and blogs, but in prominent places such as CNN and the Washington Post. That $140,000 number was key to all of the headlines. It grabbed attention and it got attention. So it’s useful to devote another post this week to the topic.
And Mr. Green has written a follow-up post, so we have something new to respond to. Mr. Green has also said a lot of things on Twitter, but Twitter can be a place for testing out ideas, so I will mostly stick to what he posted on Substack as his complete thoughts. I am also called out by name in his Part 2 post, so that’s another reason to respond (even though he did not respond directly to anything I said).
Once again, I’ll have 3 areas of contention with Mr. Green:
As with last week, I maintain that $140,000 is way too high for a poverty line representing the US as a whole (and Mr. Green seems to agree with this now, even though $140,000 was the headline in all of the major media coverage)
There are already existing alternative measures of what he is trying to grasp (people above the official poverty line but still struggling), such as United Way’s ALICE, or using a higher threshold of the poverty rate (Census has a 200% multiple we can easily access)
His idea of the “Valley of Death” is already well-covered by existing analyses of Effective Marginal Tax Rates, and tax and benefit cliffs. This isn’t to say that more attention is warranted, but Mr. Green doesn’t need to start his analysis from scratch. And this “Valley” is probably narrower than he thinks.
What do portfolio managers even get paid for? The claim that they don’t beat the market is usually qualified by “once you deduct the cost of management fees”. So, managers are doing something and you pay them for it. One thing that a manager does is determine the value-weights of the assets in your portfolio. They’re deciding whether you should carry a bit more or less exposure to this or that. This post doesn’t help you predict the future. But it does help you to evaluate your portfolio’s past performance (whether due to your decisions or the portfolio manager).
Imagine that you had access to all of the same assets in your portfolio, but that you had changed your value-weights or exposures differently. Maybe you killed it in the market – but what was the alternative? That’s what this post measures. It identifies how your portfolio could have performed better and by how much.
I’ve posted several times recently about portfolio efficient frontiers (here, here, & here). It’s a bit complicated, but we’d like to compare our portfolio to a similar portfolio that we could have adopted instead. Specifically, we want to maximize our return given a constant variance, minimize our variance given a constant return or, if there are reallocation frictions, we’d like to identify the smallest change in our asset weights that would have improved our portfolio’s risk-to-variance mix.
I’ll use a python function from github to help. Below is the command and the result of analyzing a 3-asset portfolio and comparing it to what ‘could have been’.
UPDATE: Michael Green has written a follow-up post which essentially agrees that $140,000 is not a good national poverty line, but he still has concerns. I have written a new response to his post.
A recent essay by Michael W. Green makes a very bold claim that the poverty line should not be where it is currently set — about $31,200 for a family of four — but should be much higher. He suggests somewhere around $140,000. The essay was originally posted on his Substack, but has now gone somewhat viral and has been reposted at the Free Press. (Note: that actual poverty threshold for a family of four with two kids is $31,812 — a minor difference from Mr. Green’s figure, so not worth dwelling on much, but this is a constant frustration in his essay: he rarely tells us where his numbers come from.)
I think there are at least three major errors Mr. Green makes in the essay:
He drastically underestimates how much income American families have.
He drastically overstates how much spending is necessary to support a family, because he uses average spending figures and treats them as minimum amounts.
He obsesses over the Official Poverty Measure, since it was originally based on the cost of food in the 1960s, and ignores that Census already has a new poverty measure which takes into account food, shelter, clothing, and utility costs: the Supplement Poverty Measure.
I won’t go into great detail about the Official Poverty Measure, as I would recommend you read Scott Winship on this topic. Needless to say, today the OPM (or some multiple of it) is primarily used today for anti-poverty program qualification, not to actually measure how well families are doing today. If we really bumped the Poverty Line about to $140,000, tons of Americans would now qualify for things like Medicaid, SNAP, and federal housing assistance. Does Mr. Green really want 2/3 of Americans to qualify for these programs? I doubt it. Instead, he seems to be interested in measuring how well-off American families are today. So am I.
Tomorrow, the Bureau of Labor Statistics is set to release the first major report of economic data that was delayed by the federal government shutdown: the September 2025 employment situation report. It’s good that we will get that information, but notice that we’re now in the middle of November and we’re just now learning what the unemployment rate was in the middle of September — 2 months ago (you can see their evolving updated release calendar at this link). This is less than ideal for many reasons, including that the Federal Reserve is trying to make policy decisions with a limited amount of the normal data.
What about the October 2025 unemployment rate? Early indications from the White House are that we just will never know that number. Why? Because the data likely wasn’t collected, due to the federal government shutdown. There was some confusion about this recently, with many people asking why they don’t just release it. Well, that’s because they can’t release what they don’t collect: the unemployment rate comes from the Current Population Survey, a joint effort of the BLS and Census where they interview 60,000 households every month. The survey was not done in October. It would not be impossible to do this retroactively, but the data would be of lower quality and, again, quite delayed. That gap in a series that goes back to 1948 wouldn’t be the end of the world, but it is symbolic of the disfunction of our current political moment.
What about GDP? We are now over half way through the 4th quarter of the year, and… we still don’t know what happened with GDP in the third quarter of 2025. BEA is in the process of revised their release calendar too, but they haven’t yet told us when 3rd quarter GDP will be released. In this case, the data was likely collected, but there is a certain amount of processing that needs to be done. Sure, we have estimates from places like the Atlanta Fed’s GDPNow model, but the trouble is… many of the inputs it uses are government data which haven’t been released yet for the last month of the quarter.
Eventually, all will mostly be well and back to normal, even if there are a few monthly gaps in some data series. The temporary data darkness may be coming to an end soon, but I fear it will not be the last time this happens.
Previously, I plotted the possible portfolio variances and returns that can result from different asset weights. I also plotted the efficient frontier, which is the set of possible portfolios that minimize the variance for each portfolio return.* In this post, I elaborate more on the efficient frontier (EF).
To begin, recall from the previous post the possible portfolio returns and variances.
From the above the definitions we can see that the portfolio return depends on the asset weights linearly and that the variance depends on the asset weights quadratically because the two w terms are multiplied. Since the portfolio return can be expressed as a function of the weights, this implies that the variance is also a quadratic function of returns. Therefore, every possible portfolio return-variance pair lies on a parabola. So, it follows that every pair along the efficient frontier also lies on a parabola. Not every pair lies on the same parabola, however – the efficient frontier can be composed on multiple parabolas!
I’ll use the same 3 possible assets from the previous post, below is the image denoting the possible pairs, the EF set, and the variance-minimizing point.
One way to find the EF is to calculate every possible portfolio variance-return pair and then note the greatest return at each variance. That’s a discrete iterative process and it definitely works. One drawback is that as the number of assets can increase the number of possible weight combinations to an intractable number that makes iterative calculations too time consuming. So, we can instead just calculate the frontier parabolas directly. Below is the equation for a frontier parabola and the corresponding graph.
Notice that the above efficient frontier doesn’t appear quite right. First, most obviously, the portion below the variance-minimizing return is inapplicable – I’ve left it to better illustrate the parabola. Near the variance-minimizing point, the frontier fits very nicely. But once the return increases beyond a certain level, the frontier departs from the set of possible portfolio pairs. What gives? The answer is that the parabola is unconstrained by the weights summing to zero. After all, a parabola exists at the entire domain, not just the ones that are feasible for a portfolio. The implication is that the blue curve that extends beyond the possible set includes negative weights for one or more of the assets. What to do?
As we deduced earlier, each pair corresponds to a parabola. So, we just need to find the other parabolas on the frontier. The parabola that we found above includes the covariance matrix of all three assets, even when their weights are negative. The remaining possible parabolas include the covariance matrices of each pair of assets, exhausting the non-singular asset portfolios. The result is a total of four parabolas, pictured below.
In addition to questions about inflation adjustments and general disbelief, one of the more common questions about this data is how much of it is driven by rising dual-income families, where both the husband and wife work (for purposes of this post, I will look only at opposite-sex couples, since going back to the 1960s this is the only way we can really make consistent comparisons).
In short: most of the growth of high-income families can not be explained by the rise of dual-income families. The basic reason is that the growth in dual-income families had mostly already occurred by the 1980s or 1990s (depending on the measure). So the tremendous growth since about 1990, when just about 15 percent of families were above $150,000 (in 2024 dollars), is better explained by rising prosperity, not a trick of more earners.
You can see this in a number of ways. First, here is the share of married couples where both spouses are working. I have presented the data including all married couples (blue line), as well as only married couples with some earners (gold line), since the aging of the population is biasing the blue-line downwards over time.
All of us have assets. Together, they experience some average rate of return and the value of our assets changes over time. Maybe you have an idea of what assets you want to hold. But how much of your portfolio should be composed of each? As a matter of finance, we know that not only do the asset returns and volatilities differ, but that diversification can allow us to choose from a menu of risk & reward combinations. This post exemplifies the point.
1) Describe the Assets
I analyze 3 stocks from August 1, 2024 through August 1, 2025: SCHG (Schwab Growth ETF), XLU (Utility ETF), and BRK.B (Berkshire Hathaway). Over this period, each asset has an average return, a variance, and co-variances of daily returns. The returns can be listed in their own matrix. The covariances are in a matrix with the variances on the diagonal.
The return of the portfolio that is composed of these three stocks is merely the weighted average of the returns. In particular, each return is weighted by the proportion of value that it initially composes in the portfolio. Since daily returns are somewhat correlated, the variance of the daily portfolio returns is not merely equal to the average weighted variances. Stock prices sometimes increase and decrease together, rather than independently.
Since the covariance matrix of returns and the covariance matrix are given, it’s just our job to determine the optimal weights. What does “optimal” mean? This is where financiers fall back onto the language of risk appetite. That’s hard to express in a vacuum. It’s easier, however, if we have a menu of options. Humans are pretty bad at identifying objective details about things. But we are really good at identifying differences between things. So, if we can create a menu of risk-reward combinations, then we’re better able to see how much a bit of reward costs us.
2) Create the Menu
In our simple example of three assets, we have three weights to determine. The weights must sum to one and we’ll limit ourselves to 1% increments. It turns out that this is a finite list. If our portfolio includes 0% SCHG, then the remaining two weights sum to 100%. There are 101 possible pairs that achieve that: (0%, 100%), (1%,99%), (2%,98%), etc. Then, we can increase the weight on SCHG to 1% for which there are 100 possible pairs of the remaining weights: (0%,99%), (1%, 98%), (2%, 97%), etc. We can iterate this process until the SCHG weight reaches 100%. The total number of weight combinations is 5,151. That means that there are 5,151 different possible portfolio returns and variances. The below figure plots each resulting variance-return pair in red.
Economist Writing Every Day 