Data

What does the Department of Education even do?

Zachary Bartsch4 Comments

If you follow libertarian media such as Reason Magazine or its ancillaries, then you are well acquainted with the humdrum of “it goes without saying that most US programs should be ended“. They kind of just say this and then continue with their news. One of the favorites is to say that we should get rid of the Department of Education (ED). After all, 90% of K-12 education is paid for by states and localities. Here I was thinking “what does the Department of Education even do”?

Agreement is different from trust. I trust the Brookings Institute. They have a nice explainer on what ED does. It’s a quick overview and has plenty of the appropriate citations. I learned that most of what ED does concerns K-12 and is achieved through grants that have strings attached. Funding primarily goes to serving “educationally disadvantaged” communities (that have a high poverty rate). Funding also goes to programs for disabled children, minority education programs (like Howard University), and Indian tribes. They also administer Pell Grants and fund & regulate college loans (which are privately administered).

ED’s appropriated budget is online for anyone to see and includes pretty good detail about costs. The total discretionary cost of FY 2024 was $79 billion. The “mandatory” spending, which does not need to be voted on by congress every year, was $45 billion. For context, the entire federal FY 2024 expenditure was $6.75 trillion. So, eliminating the department of education *and* it’s responsibilities (an unpopular position) would reduce federal expenditures by 1.8%. For even more context, the budget deficit is $1.83 trillion or 27.1% of total federal expenditures. Eliminating ED and consolidating its responsibilities to other departments would save $0.6 billion. That assumes eliminating program administration, the ED office of civil rights, and the ED office of the inspector general.

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County Demographic Data: A Clean Panel 1969-2023

James Bailey2 Comments

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.

HT: Kabir Dasgupta

Women Have Always Worked More Than Men: Hours of Work Since 1900

Jeremy Horpedahl3 Comments

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):

Most of the data (from 1900-2005) comes from a 2009 paper by Valerie Ramy and Neville Francis, which looks at lots of trends in work and leisure in the twentieth century. I extend the data past 2005 using an update from Ramey through 2012, and then attempting to replicate their methods using the CPS (for market work) and the BLS ATUS (for home production).

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).

Trump’s Economic Policy Uncertainty

Zachary Bartsch3 Comments

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.

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Hospitals Remain Full Even as Covid Subsides

James Bailey1 Comment

The average hospital is now 3/4 full- more full than during much of the worst of the Covid pandemic, and well above the 2/3 occupancy rate that prevailed during the 2010s. This is according to a study out yesterday in JAMA Open:

This seems to be due to a reduction in bed supply, rather than an increase in demand:

The number of staffed hospital beds declined from a prepandemic steady state of 802 000 (2009-2019 mean) to a post-PHE steady state of 674 000, whereas the mean daily census steady state remained at approximately 510 000

To me this is one more reason to reform Certificate of Need laws that put barriers in the way of hospitals opening or adding beds. Luckily I see a lot of momentum for CON reform this legislative season, including the highest-occupancy state, Rhode Island:

Forecasting the Fed: Description Vs Prescription

Zachary BartschLeave a comment

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.

https://fred.stlouisfed.org/graph/?g=1DIsX

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.

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National Survey of Children’s Health Backup

James BaileyLeave a comment

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.

2024 Labor Market: Not the Greatest Ever, But Pretty, Pretty Good

Jeremy Horpedahl1 Comment

At the end of 2023 I asked: was 2023 the greatest labor market in US history? I presented some data to suggest that, yes, maybe, probably, it was the greatest labor market in US history.

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.

RGDP Underestimates Welfare

Zachary BartschLeave a comment

Like many Principles of Macroeconomics courses, mine begins with an introduction to GDP. We motivate RGDP as a measure of economic activity and NGDP as an indicator of income or total expenditures. But how does more RGDP imply that we are better off, even materially? One entirely appropriate answer is that the quantities of output are greater. Given some population, greater output means more final goods and services per person. So, our real income increases.  But what else can we say?

First, after adjusting for price changes, we can say that GDP underestimates the value that people place on goods and services that are transacted in markets. Given that 1) demand slopes down and 2) transactions are consensual, it stands to reason that everyone pays no more than their maximum value for things. This implies that people’s willingness to pay for goods surpasses their actual expenditures. Therefore, RGDP is a lower bound to the economic benefits that people enjoy. Without knowing the marginal value that people place on all quantities less than those that they actually buy, we have no idea how much more value is actually provided in our economy.

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Triumph of the Data Hoarders

James Bailey3 Comments

Several major datasets produced by the federal government went offline this week. Some, like the Behavioral Risk Factor Surveillance Survey and the American Community Survey, are now back online; probably most others will soon join them. But some datasets that the current administration considers too DEI-inflected could stay down indefinitely.

This serves as a reminder of the value of redundancy- keeping datasets on multiple sites as well as in local storage. Because you never really know when one site will go down- whether due to ideological changes, mistakes, natural disasters, or key personnel moving on.

External hard drives are an affordable option for anyone who wants to build up their own local data hoard going forward. The Open Science Foundation site allows you to upload datasets up to 50 GB to share publicly; that’s how I’ve been sharing cleaned-up versions of the BRFSS, state-levle NSDUH, National Health Expenditure Accounts, Statistics of US Business, and more. If you have a dataset that isn’t online anywhere, or one that you’ve cleaned or improved to the point it is better than the versions currently online, I encourage you to post it on OSF.

If you are currently looking for a federal dataset that got taken down, some good places to check are IPUMS, NBER, Archive.org, or my data page. PolicyMap has posted some of the federal datasets that seem particularly likely to stay down; if you know of other pages hosting federal datasets that have been taken down, please share them in the comments.