Data

Is Every Stock a Tariff Stock?

Jeremy HorpedahlLeave a comment

Not quite, at least not in the same way that every stock was a vaccine stock in 2020, as Alex Tabarrok put it.

Today the stock market does seem to move a lot on the news about Trump’s ever-evolving tariff policy. If you see the S&P 500 is up today, you can probably guess that Trump or his advisors slightly backed off some aspect of their previously announced tariff policy. And vice versa. That much is true.

But back in 2020, the implied correlation in the market was briefly over 80% in the spring of 2020, and was over 50% for almost all of the summer of 2020. Today, the correlation is closer to 40%. That’s a bit lower than 2020, but it is a significant jump from where it was 2-3 months ago.

Here is the Cboe’s implied 3-month correlation index:

In addition to the costs of tariffs themselves, investors should be worried about this correlation because “market returns are lower when correlations among assets are increasing.”

It’s the Humidity

Zachary Bartsch1 Comment

Recently, I learned what humidity is. That might sound stupid, so let me clarify. I knew that humidity is the water content of the air. I also knew that the higher the number, the more humid. Finally, I also knew that the dew point is the temperature at which the water falls out of the air. But, now I understand all of this in a way that I hadn’t previously.

First, what does it mean for there to be 70% humidity? As it turns out, it’s a moving target. There are two types of humidity: specific and relative. Specific humidity is the mass of water in, say, a kilogram of air. So, more humidity means more water. This is obvious. There’s a related concept called absolute humidity, which is more like mass of water per volume of air (sometimes used in place of specific humidity). Again, more humidity means more water. Neither of these is the way that humidity is reported on the weather channel.

Relative humidity is the number that you see in your weather app. What’s that? Relative to what? First, we need to know that warm air can hold more water than cool air. Pressure also matters, but atmospheric pressure doesn’t change enough to make its effect on humidity significant on relevant margins. So, all of this discussion, and the number in your phone, is at atmospheric pressure. Below is a graph that illustrates the maximum amount of water that can be in the air at different temperatures (red line). So, at 30 degrees Celsius (86 degrees Fahrenheit), there can be as much as 27 grams (0.95 oz or ~2 tablespoons) of water in the air.

More after the jump.

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GDPNow: Still Negative on Q1, But Less So

Jeremy Horpedahl1 Comment

Last month I wrote about the projected decline in GDP from the Atlanta Fed’s GDPNow model. Since then, they have released an alternative version of the model, which includes a “gold adjustment” to account for non-monetary gold inflows, which may be impacting the model to overstate the negative impact of imports (and it looks like this may be a permanent change to the model).

With those changes, and some more recent data, the GDPNow model is still pointing to a negative reading for Q1 of 2025, though only very slightly now: -0.1%.

It’s also worth noting that the New York Fed has a similar model, but one with very different estimates right now: about 2.6% for Q1.

We’ll still have to wait until April 30th to get the preliminary estimates from BEA.

Now published: Human capital of the US deaf Population, 1850-1910

Zachary BartschLeave a comment

Myself and a student coauthor worked hard on our article that is now published in Social Science History. It’s the first modern statistical analysis of the historical deaf population. We bring an economic lens and statistical treatment to a topic that previously included much anecdotal evidence and case study. We hope that future authors can improve on our work in ways that meet and surpass the quantitative methods that we employed.

Our contributions include:

  • A human capital model of deafness that’s agnostic about its productivity implications and treats deaf individuals as if they made decisions rationally.
  • A better understanding of school attendance rates and the ages at which they attended.
  • Deaf children were much more likely to be neither in school nor employed earlier in US history.
  • The negative impact of state ‘school for the deaf’ availability on subsequent economic outcomes among deaf adults. We speculate that they attended schools due to the social benefits of access to community.
  • Deaf workers did not avoid occupations where their deafness would be incidentally detectable by trade partners, implying that animus discrimination was not systemically important for economic outcomes.
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Get your HHS Data Ahead of Cuts

James BaileyLeave a comment

The US Department of Health and Human Services has announced it is cutting 10,000 of its 82,000 jobs and restructuring:

As part of the restructuring, the department’s 10 regional offices will be cut to five and its 28 divisions consolidated into 15, including a new Administration for a Healthy America, or AHA, which will combine offices that address addiction, toxic substances and occupational safety into one central office.

AHA will include the Office of the Assistant Secretary for Health, the Health Resources and Services Administration, the Substance Abuse and Mental Health Services Administration, Agency for Toxic Substances and Disease Registry, and the National Institute for Occupational Safety and Health.

These divisions do many different jobs, but as usual what stands out to me is their data- both because it is what I have found directly useful in the past, and because it is what I still have some control over now. Writing your Representatives or writing an op-ed has a minuscule chance of changing Federal policy, but if you download data, you definitely have that data.

What worries me here is that some of the agencies being consolidated might discontinue some of their data products going forward, or even pull some of what they have already created offline. I don’t think this is farfetched given what has happened so far, and given that even in good times these agencies pull down data they painstakingly prepared. For instance, HRSA only publicly posts the State- and County-level Area Health Resources File back to 2019, even though they have annual data going back to 2001.

Probably all 13 of the reorganizing divisions have data worth looking into, and given the staff cuts, even data products in the other divisions could be at risk. But my plan is to focus on the two reorganizing divisions whose data I have previously found useful- HRSA and SAMHSA. HRSA has a nice data download page with 16 different datasets, including the Area Health Resources File, which offers detailed information on the health care workers and facilities in each US county. SAMHSA offers the National Substance Use and Mental Health Services Survey, the Treatment Episode Data Set, and the National Survey of Drug Use and Health. I have previously cleaned and archived the state-level version of the NSDUH, but not the individual-level version that is for now still available from SAMHSA.

All of these datasets are easy to download now, and some will probably become very hard to access later, so now is a good time to take a few minutes and save whatever you think you might need.

Messy Disability Records in the Historical Censuses

Zachary Bartsch1 Comment

The historical US Census roles of disability among free persons are a mess. Specifically for the 1850-1870 censuses, the census bureau was not professionalized and the pay was low (a permanent office wasn’t founded until 1902). So, the enumerators were temporary employees and weren’t experts of their art. To boot, their handwriting wasn’t always crystal clear. Second, training for disability enumeration was even less complete and enumerators did their best with whom they encountered and how they understood the instructions. Finally, the digitized data in IPUMS doesn’t perfectly match the census reports. What a mess.

Guilty by Association

Disabled people and their families often misreported their status out of embarrassment or shame. Given that enumerators had quotas to fill, they were generally not inclined to investigate claimed statuses strenuously. Furthermore, disabled people were humans and not angels. Sometimes they themselves didn’t want to be associated with other types of disabled people. In particular, the disability designation in question (13) on the 1850 census questionnaire asked  “Whether deaf and dumb, blind, insane, idiotic, pauper or convict”. Saying “yes” may put you in company that you don’t prefer to keep.

Summer censuses also sometimes missed deaf students who were traveling to or from a residential school.

Enumerator Discretion

The enumerator’s job was to write the disability that applied. What counts as deaf and dumb? That’s largely at the enumerator’s discretion. Some enumerators wrote ‘deaf’ even though that wasn’t an option. Was that shorthand for ‘Deaf and Dumb’? Or were they specifying that the person was deaf only and not dumb? We don’t know. But we do know that they didn’t follow the instructions. What if a person was both insane and blind? Then what should be written? “Blind/Insane” or “Blind and Insane” or “In-B” and any number of combinations were written. Some of them are easier to read than others.

Data Reading Errors

IPUMS is the major resource for using census data. The historical data was entered by foreign data-entry workers who didn’t always speak English. So, the records aren’t perfect. Some of the records are corroborated with Optical Character Recognition (OCR), but the historical script is sometimes hard to read. Finally, the fine folks at familysearch.org and Brigham Young University have used Church of Latter Day Saints (LDS) volunteers to proof data entries. Regardless, we know that the IPUMS data isn’t perfect and that the disability data is far from perfect. Usually, reports don’t dwell on it. They simply say that the data is incomplete.

The disability data is incomplete for a lot of reasons related to the respondent, the enumerator, the instructions, and the digital data creation. What a mess.

Home Health Certificate of Need

James Bailey1 Comment

Certificate of Need laws require many types of health care providers to obtain the permission of a state board before they are allowed to open or expand in many US states. But there is a lot of variation from state to state in which types of providers are covered by these laws. I put together this map to show the 15 states that require new home health care agencies to obtain a Certificate of Need:

Source: My map based on data from National Conference of State Legislatures

CON states see reduced competition, which tends to be bad news for patients and new entrants, but good for existing providers and the private equity firms considering buying them.

But some CON states like Rhode Island have proposed reforms that would exempt home health agencies from the CON process, putting them in line with the majority of states that put new entrants on an even footing with incumbent providers.

Optimal Protein Consumption in the 21st Century: A Model

Zachary Bartsch1 Comment

I’ve discussed complete proteins before. I’ve talked about the ubiquity of protein, animal protein prices, vegetable protein prices, and a little but about protein hedonics. My coblogger Jeremy also recently posted about egg prices over the past century. Charting the cost of eggs is great for identifying egg affordability. But a major attraction of eggs is that they are a ‘complete protein’. So how much of that can we afford?

Here I’ll outline a model of the optimal protein consumption bundle. What does this mean? This means consuming the quantities of protein sources that satisfy the recommended daily intake (RDI) of the essential amino acids and doing so at the lowest possible expenditure. Clearly, this post includes a mix of both nutrition and economics.  Since a comprehensive evaluation that includes all possible foods would be a heavy lift, here I’ll just outline the method with a small application.

Consider a list of prices for 100 grams of Beef, Eggs, and Pork.* We can also consider a list that identifies the quantity that we purchase in terms of hundreds of grams. Therefore, the product of the two yields the total that we spend on our proteins.

Of course, not all proteins are identical. We need some characteristics by which to compare beef, eggs, and pork. Here, I’ll use the grams of essential amino acids in 100 grams of each protein source. Because there are different RDIs for each amino acid, I express each amino acid content as a proportion of the RDI (represented by the standard molecular letter).

Then, we can describe how much of the RDI of each amino acid that a person consumes by multiplying the amino acid contents by the quantities of proteins consumed.

Our goal is to find the minimum expenditure, B, by varying the quantities consumed, Q, such that the minimum of C is equal to one. If the minimum element of C is greater than one, then a person could consume less and spend less while still satisfying their essential amino acid RDI. If the minimum element is less than one, then they aren’t getting the minimum RDI.

How do we find such a thing? Well, not algebraically, that’s for sure. I’ll use some linear programming (which is kind of like magic, there’s no process to show here).

The solution results in consuming only 116.28 grams of Pork and spending $1.093 per day. The optimal amino acid consumption is also below. Clearly, prices change. So, if eggs or beef became cheaper relative to pork, then we’d get different answers.

In fact, we have the price of these protein sources going back almost every month to 1998. While pork is exceptionally nutritious, it hasn’t always been most cost effective. Below are the prices for 1998-2025. See how the optimal consumption bundle has changed over time – after the jump.

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The Price of Eggs: Long-Run Perspective

Jeremy Horpedahl2 Comments

Everyone is talking about the price of eggs. Even the President. That’s despite the fact eggs, on average, constitute about 0.1% of consumer spending (according to the Consumer Expenditure Survey for 2023). Even so, economists always get excited when people talk about prices.

On prices at the current moment, I wrote a blog post for the Cato Institute looking at the relevant supply and demand factors, and trying to explain why wholesale egg prices are falling so quickly. When will these falling wholesale prices translate into lower retail prices? The NY Times asked this question, and I tried to answer it for them (answer: perhaps in a few weeks).

But let’s step back from the current moment and take a longer-term perspective on egg prices. This chart shows the long-run real price of eggs, measured in terms of how much time an average worker would need to work to afford 1 dozen eggs:

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A Forgotten Data Goldmine: Foreign Commerce and Navigation Reports

Zachary BartschLeave a comment

Economists rely on trade data. The historical Foreign Commerce and Navigation of the United States reports detailed monthly figures on imports, exports, and re-exports. This dataset spans decades, providing a crucial resource for researchers studying price movements, consumption patterns, and the effects of war on global trade.

The U.S. Department of Commerce compiled these reports to track the nation’s commercial activity. The data cover a vast range of commodities, including coffee, sugar, wheat, cotton, wool, and petroleum. Officials recorded trade flows at a granular level, enabling economists to analyze seasonal fluctuations, wartime distortions, and postwar recoveries. Their inclusion of re-export figures allows for precise estimates of domestic consumption. Researchers who ignore re-exports risk overstating demand by treating imports as goods consumed rather than goods in transit.

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