I’ve now posted individual-level responses to the 1978-2025 Michigan Consumer Surveys to Kaggle in CSV and Stata formats. The University of Michigan’s Consumer Surveys are a widely followed source for data on consumer confidence and inflation expectations:
Their official site is good if you just want summary tables or charts like this:
But what if you want detailed crosstabs to see how sentiment differs for different groups, or microdata so that you can run regressions? With enough clicks you can get this from what UMich calls their “cross-section archive“. But it is pretty hidden, my student looking into this thought they just didn’t offer individual-level data; and even once you get their data, it is in an unlabelled CSV file with hard-to-understand variable names and codes. So I wanted to make it clear that the full data with all responses for all years is available, and if you use my Stata version it is even reasonably easy to understand (the code I adapted for labelling it is on OSF). Then you can run your regressions, or make charts like this:
The College-Only Covid Recovery
If you’re new here, a reminder that you can find other cleaned-up versions of popular datasets on my data page.
America has withdrawn aid from Ukraine. Contra the Vice-President, we could easily afford to reverse this, and I hope we will. I know we could afford it because even the much poorer Europeans can, and I think they might finally be ready to try.
Until now, Europe has been fighting with both hands tied behind their back- letting their economic growth fall far behind America’s due to poor policy, and committing only a tiny share of that economy to defense. Here’s how Polish Prime Minister Donald Tusk put it:
Europe may be significantly poorer than the US on a per-capita basis, but it has significantly more people, so the total size of their economy is almost as large as the US and over 4 times larger than Russia:
But Europe has put only a small fraction of their economy toward defense for a long time. Russia alone spends more on their military than the rest of Europe combined despite their much smaller economy, by putting a much larger fraction of their GDP toward the military:
When European countries spend so little on their own defense, they have little to share with Ukraine. Many leaders complaining about the end of US support have contributed much less themselves, even as a share of their smaller economies:
Europe can be much stronger than Russia, but only if they start trying at least half as hard as Russia. Yes, Europe has poor demographics, but Russia’s are worse; Europe has many more military-age men:
Yes, Russia has nukes, but so do Britain and France, and France might actually take advantage of this.
Economically speaking, is this a good time for Europe to rearm? To me it looks fine. The best time would have been the late 2000s to early 2010s, both because it could have been in time to dissuade Russia from starting this war, and because their economic problems then were much more about a lack of aggregate demand. But right now inflation is fine at 2.4%, NGDP growth is fine at 4.3%, and 10-year bond yields the major countries are around 3-4%. Overall this looks like AD is currently about right, but markets expected that economic growth could turn negative this year, and a burst of defense spending could head that off:
This would be especially valuable if it can be paired with the supply-side reforms that European leaders know they should to do anyway, and that would allow for more growth without pushing up inflation. Europe has fallen far behind the US in productivity, to the point that it is now a bigger issue than their higher unemployment and lower hours worked in explaining why the US is much richer:
The silver lining here is that the further behind the US they fall, the faster they could potentially grow- catchup growth is easier than frontier growth, you just need to copy the technologies and implement the strategies already figured out by the frontier economies. Europe easily has the human capital to do this, they just haven’t had the will- have preferred to regulate new technologies like fracking and AI into oblivion, along with older technologies like nuclear power. They won’t drill for oil and gas themselves in the name of decarbonization but have spent hundreds of billions on Russian oil and gas just since the war began. But if they ever decided to change their policy, their economy could rapidly improve- like letting go of the rubber band you’ve been pulling back.
European leaders appear to finally be realizing this. The European Commission just proposed a 150 billion Euro joint defense fund. This week Germany proposed spending half a trillion on infrastructure and defense, sending European stocks above their previous all-time high set in the year 2000 (!).
The EU always used to be able to excuse their economic failings by saying “at least we brought peace to a continent formerly full of war.” But this is no longer the case. If they cannot settle the war on good terms, they have no excuse. The good news is that European decline has been a choice, and it is a choice they could decide to change at any moment. Victory awaits those who will it.
The chart comes from the Atlanta Fed’s GDPNow model, which tries to estimate GDP growth each quarter as data becomes available. The sharp drops in their Q1 forecast for 2025, based on the last two data updates, look pretty shocking. Should we be worried?
First, it’s useful to ask: has this model been accurate recently? Yes, it has. For Q4 of 2025, the model forecast 2.27% growth — it was 2.25%. For Q3 of 2024, the model forecast 2.79% growth — it was 2.82%. Those are very accurate estimates. Of course, it’s not always right. It overestimated growth by 1 percentage point in Q1 of 2024, and it underestimated growth by 1 percentage point the quarter before that. So pretty good, but not perfect. Notable: during the massive decline in Q2 2020 at the start of the pandemic, it got pretty close even given the strange, uncertain data and times, predicting -32.08% when it was -32.90% (that’s off by almost 1 percentage point again, but given the highly unusual times, I would say “pretty good”).
OK, so what can we say about the current forecast of -2.8% for Q1 of 2025? First, almost all of the data in the model right now are for January 2025 only. We still have 2 full months in the quarter to go (in terms of data collection). Second, the biggest contributor to the negative reading is a massive increase in imports in January 2025.
To understand that part of the equation, you have to think about what GDP is measuring. It is trying to measure the total amount of production (or income) in the United States. One method of calculation is to add up total consumption in the US, including by final consumers, business investments, and government purchases and investments. But this method of calculation undercounts some US production (because exports don’t show up — they are consumed elsewhere) and overcounts some US production (because imports are consumed here, but not produced here). So to make GDP an accurate measure of domestic production, you need to add in exports, and subtract imports.
Keep in mind what we’re doing in this calculation: we aren’t saying “exports good, imports bad.” We are trying to accurately measure production, but in a roundabout way: by adding up consumption. So we need to take out the goods imported — not because they are bad, but because they aren’t produced in the US.
The Atlanta Fed GDPNow model is doing exactly that, subtracting imports. However, it’s likely they are doing it incorrectly. Those imports have to show up elsewhere in the GDP equation. They will either be current consumption, or added to business inventories (to be consumed in the future). My guess, without knowing the details of their model, is that it’s not picking up the change in either inventories or consumption that must result from the increased imports. It’s also just one month of data on imports.
As always, we’ll have to wait for more data and then, of course, the actual data from BEA (which won’t come until April 30th). More worrying in the current data, to me, is not the massive surge in imports — instead, it’s that real personal consumption expenditures and real private fixed investment are currently projected to be flat in Q1. If consumption growth is 0% in Q1, it will be a bad GDP report, regardless of everything else in the data.
Whenever researchers are conducting studies using state- or county-level data, we usually want some standard demographic variables to serve as controls; things like the total population, average age, and gender and race breakdowns. If the dataset for our main variables of interest doesn’t already have this, we go looking for a new dataset of demographic controls to merge in; but it has always been surprisingly hard to find a clean, easy-to-use dataset for this. For states, I’ve found the University of Kentucky’s National Welfare Database to be the best bet. But what about counties?
I had no good answer, and the best suggestion I got from others was the CDC SEER data. As so often, the government collected this impressively comprehensive dataset, but only releases it in an unusable format- in this case only as txt files that look like this:
I cleaned and reformatted the CDC SEER data into a neat panel of county demographics that look like this:
I posted my code and data files (CSV, XLSX, and DTA) on OSF and my data page as usual. I also posted the data files on Kaggle, which seems to be more user-friendly and turns up better on searches; I welcome suggestions for any other data repositories or file formats you would like to see me post.
This chart shows the average number of hours worked in the US, by gender, for those in their prime working ages (25-54), from 1900 to 2023. It includes both paid market work and household production (which includes activities like cooking, cleaning, shopping, and taking care of children):
A few things to notice. First, there is no data for 2020, as the ATUS didn’t publish any tables due to incomplete data from the pandemic. And even if we had data, it would have been a huge outlier year.
More importantly, there is an obvious long-term trend of declining market work and rising household production for men, and the opposite for women. In 1900 women worked over 6 times as many hours in the household as they did in the market, but by 2023 they worked almost the exact same number of hours in each sector.
Male hours in market work declined by about 16 hours per week (using 10-year averages, as there is a slight business-cycle effect on hours), but the total number of hours they worked declined much more modestly, by about 3 hours per week (note: these numbers include all men, whether they are working or not). Women saw similar changes, but in the opposite direction, with total hours worked only falling by about 4 hours per week, even though hours working at home fell by almost 22 hours.
Americans do have more leisure time than in 1900, but not dramatically so: perhaps 3-4 hours per week. This is an improvement, but less of an improvement than you might suspect by looking at hours of market work alone.
Ramey and Francis do try to carefully distinguish between household production and leisure. For example, yardwork and changing diapers are household production, while gardening and playing with your children are leisure. For some respondents to surveys, they may feel differently about whether gardening is “really” work or not, and some may enjoy changing baby’s diapers, but in general their distinctions seem reasonable to me.
Finally, we can say pretty confidently with this data that women have almost always worked more hours than men — the one exception in the 20th century being WW2 — and the gender gap was about 4 hours per week in both the early 1900s and the most recent decade (though it did fluctuate in between).
I was on a panel of economists last night at an event titled “The Economic Consequences of President Trump”. We each gave a 5-minute summary from our area of expertise and then opened up the floor for questions. This is a truncated summary of my talk. Since the panel included an investor, two industry economists, and another macro economist, I wanted to discuss something that was distinct from their topics. I’ve published a paper and refereed many articles concerning economic policy uncertainty (EPU) and asset volatility. I wanted to look at the data concerning President Trump – especially in contrast to Presidents Obama and Biden.
EPU matters because uncertainty can cause firms and individuals to delay investment and hiring decisions. Greater uncertainty can also cause divergent views concerning forecasted firm profitability. The result is that asset prices tend to become more volatile when EPU rises. One difficulty is that uncertainty occurs in our heads and concerns our beliefs, making it hard to measure. We try to get at it by measuring how often news media articles include the terms related to uncertainty, policy, and the economy. Since news content tends to report what is interesting, relevant, or salient to customers, there’s good reason to think that the EPU index is a decent proxy.
Using the Obama years as a baseline, the figure below simply charts out EPU. It was relatively low during Trump’s first term and then it was higher during Biden’s term – even after accounting for the Covid spike. The sharp increase toward the end is after Trump won the 2024 election. The EPU series conflicts with my perception of social media and media generally. My experience was that the media was far more attentive to the uncertainty that Trump caused. But, it may just be that the media outlets had plenty to report on rather than it being particularly indicative of EPU. After all, if the president exercises his power, then there is a certain swift decisiveness to it.
But if we look at a couple of particular policy areas, Trump’s administration faired worse. Specifically, Trump caused a ruckus concerning trade policy and immigration. Remember when Biden continued the aggressive trade policy that Trump had adopted? That’s consistent with lower EPU. Similarly, Biden made the immigration process much easier and faster while Trump’s deportation haranguing results in a somewhat stochastic means by which people are deported. Again, that spike at the end is after Trump won the 2024 election.
Are foreigners taking the jobs of native-born Americans? The fear that foreigners are displacing domestic workers has long been feared, and remains one of the major economic objections to immigration. And recently there seems to be some evidence this is happening in the US, with almost all net job creation in the US in the past 5 years going to foreigners, while native-born employment has been flat.
But this is not evidence that foreigners are taking our jobs, as I explain in my latest piece for the Cato Institute. The reason is simple: the native-born, working-age population hasn’t been growing. If we looking at the employment-rate of native-born Americans, it is higher than it has ever been, and higher than for the foreign-born population:
After raising rates in 2022 to belatedly combat inflation, the FOMC was feeling successful in 2024. They were holding the line and remaining steadfast while many people were getting all in a tizzy about pushing us into a recession. People had been predicting a recession since 2022, and the Fed kept the federal funds rate steady at 5.33% for an entire year. Repeatedly, in the first half of 2024, betting markets were upset that the Fed wasn’t budging. I had friends saying that the time to cut was in 2023 once they saw that Silicon Valley Bank failed. I remained sanguine that rates should not be cut.
I thought that rates should have been higher still given that the labor market was strong. But, I also didn’t think that was going to happen. My forecasts were that the Fed would continue to keep rates unchanged. At 5.33%, inflation would slowly fall and there was plenty of wiggle room for unemployment.
Then, we had a few months of lower inflation. It even went slightly negative in June 2024. Some people were starting to talk about overshooting and the impending recession. I documented my position in August of 2024. Two weeks later, Jerome Powell gave a victory lap of a speech. He said that “The time has come for policy to adjust”. Instead of discerning whether the FOMC would cut rates, the betting markets switched to specifying whether the cut would be 0.25% or 0.5%. The Fed chose the latter, followed by two more cuts by the end of the year.
I was wrong about the Fed’s policy response function. But why? Was the FOMC worried about the downward employment revisions? That was big news. Did they think that they had inflation whipped? I’m not sure. There was a lot of buzz about having stuck the soft landing. In late 2024, I leaned toward the theory that the Fed was concerned about employment. Like, they thought that we had been doing better until then.
The NSCH is the latest casualty of the new administration taking down major datasets from government websites. Between Archive.org and what I had downloaded for old projects, I was able to get all the 2016-2023 topical NSCH files and post them on an Open Science Foundation page.
I took this as a chance to improve the data- the government previously only made the topical Public Use Files available in SAS and Stata formats one year at a time, so I added a merged version for all available years in both Stata and Excel formats.
I hope and expect that the National Survey Children’s Health will be back up at official websites soon. But I expect that other datasets will be taken down permanently, so now is the time to download what you think you might need and add it to your data hoard– especially if you want anything from the Department of Education.
That post was partly inspired by critics of the unemployment rate as a broad measure of labor market utilization. Yes, the UR isn’t perfect, and it misses some things. But other measures of labor force performance tend to move with the UR, and so it’s still a useful measure. 2023 saw not only some of the lowest unemployment rates in US history (rivaling the late 1960s), but also some of the highest employment rates (only beat by the late 1990s). Wage growth was also robust. And other measures of unemployment, such as the much broader U-6 rate and the Insured Unemployment Rate, were also at record low levels (though the data doesn’t go back as far).
Today I learned about a very interesting, though I think probably confusing, measure called the “true unemployment rate.” Produced by the Ludwig Institute, it uses the same underlying data source (the CPS) that the BLS uses to calculate the unemployment rate and other measures mentioned above. This “true” rate is definitely intended to shock you: it suggests that 25 percent of the workforce is “unemployed.”
But they aren’t actually measuring unemployment. What they are doing, in a sense, is combining a very broad measure of labor underutilization (like the U-6 rate mentioned above) with a measure that is similar to the poverty rate (but not exactly). They count people as unemployed if they are part-time workers, but would like to work full-time (U-6 does this). But they also count you as unemployed if you earn under $25,000 per year. Or if you don’t work at all, you are counted as unemployed — even if you aren’t trying to find a job (such as being a student, a homemaker, disabled, etc.). The entire working age population (ages 16+, though they don’t tell us the upper limit, we can probably assume 64) is the denominator in this calculation.
So again, this is attempting to combine a broad measure of employment with a poverty measure (though here poverty is defined by your own wage, rather than your household income). So of course you will get a bigger number than the official unemployment rate (or even the U-6 rate).
But here’s the thing: even with this much broader definition, the US labor market was still at record lows in 2023! Given this new information I learned, and that we are now through 2024, I decided to update the table from my previous post:
From this updated table, we see that by almost every measure, 2023 was an excellent year for the US labor market. The only measure where it slightly lags is the prime-age employment rate, which was a bit higher in the late 1990s/2000. Real wage growth was also quite strong in 2023, despite still having some lingering high inflation from the 2021-22 surge.
How about 2024? By almost all of these measures, 2024 was slightly worse than 2023. And still, 2024 was a good year. A pretty, pretty good year for the labor market. And while the UR ticked up in the middle of the year, it has since come back down a bit and is now right at 4%. As for the “true” unemployment rate, it followed a similar pattern, ticking up a bit in mid-2024, but by December it was back slightly below the level from December 2023.
Alternative “true” measures of the economy rarely give us any additional information than the standard measures — other than a shocking, but confusing, headline number.
Economist Writing Every Day 