Health

Health Insurance and Wages: Compensating Differentials in Reverse?

James Bailey9 Comments

One of the oldest theories in economics is the idea of compensating differentials. A job represents not just a certain amount of money per hour, but a whole package of positive and negative things. Jobs have more or less stability, flexibility, fun, room to grow, danger… and non-cash benefits like health insurance. The idea of compensating differentials is that, all else equal, jobs that are good on these other margins can pay lower cash wages and still attract workers (thus, the danger of doing what you love). On the other hand, jobs that are bad on these other margins need high wages if they want to hire anyone (thus, the deadliest catch)

I think this theory makes perfect sense, and we see evidence for it in many places. But when it comes to health insurance, everything looks backwards. A job that offers employer-provided health insurance is better to most employees than one that doesn’t, so by compensating differentials it should be able to offer lower wages. There’s just one problem: US data shows that jobs offering health insurance also offer significantly higher wages. The 2018 Current Population Survey shows that workers with employer-provided health insurance had average wages of $33/hr, compared to $24/hr for those without employer insurance.

All the economists are thinking now: that’s not a problem, compensating differentials is an “all else equal” claim, but not all else is equal here. The jobs with health insurance pay higher wages because they are trying to attract higher-skilled workers than the jobs that don’t offer insurance.

That’s what I thought too. It is true that jobs with insurance hire quite different workers on average:

Source: 2017 CPS analyzed here

The problem is, once we control for all the observable ways that insured workers differ, we still find that their wages are significantly higher than workers who don’t get employer-provided insurance. Like, 10-20% higher. That’s after controlling for: year, sex, education, age, race, marital status, state of residence, health, union membership, firm size, whether the firm offers a pension, whether the employee is paid hourly, and usual hours worked. I’ve thrown in every possibly-relevant control variable I can think of and employer-provided health insurance always still predicts significantly higher wages. Of course, there are limits to what we get to observe about people using surveys; I don’t get any direct measures of worker productivity. Possibly the workers who get insurance are more skilled in ways I don’t observe.

We can try to account for these unobserved differences by following the same person from one job to another. When someone switches jobs, they could have health insurance in both jobs, neither, only the new, or only the old. What happens to the wages of people in each of these situations? It turns out that gaining health insurance in a new job on average brings the biggest increase in wages:

What could be going on here? One possibility is that health insurance makes people healthier, which improves their productivity, which improves their wages. But we control for health status and still find this effect. The real mystery is that papers that study mandatory expansions of health insurance (like the ACA employer mandate and prior state-level mandates) tend to find that they lower wages. Why would employer-provided health insurance lower wages when it is broadly mandated, but raise wages for individuals who choose to switch to a job that offers it?

My current theory is that “efficiency benefits” are offered alongside “efficiency wages”. The idea of efficiency wages is that some firms pay above-market wages as a way of reducing turnover. Workers won’t want to leave if they know their current job pays above-market, and so the company saves money on hiring and training. But this only works if other firms aren’t doing it. The positive correlation of wages and insurance could be because the same firms that pay “efficiency wages” are more likely to pay “efficiency benefits”- offering unusually good benefits as a way to hold on to employees.

I still feel like these results are puzzling and that I haven’t fully solved the puzzle. This post summarizes a currently-unpublished paper that Anna Chorniy and I have been working on for a long time and that I’ll be presenting at WVU tomorrow. We welcome comments that could help solve this puzzle either on the empirical side (“just control for X”) or the theoretical side (“compensating differentials are being overwhelmed here by X”).

The Leading Causes of Death Among Elementary-Age Children

Jeremy Horpedahl7 Comments

You might have seen this chart recently. It comes from a letter published in the New England Journal of Medicine in April 2022. The data comes directly from the CDC. It shows the leading causes of death for children in the US. You will notice that firearm-related deaths have been rising for much of the past decade, and in 2020 eclipsed car accidents as the leading cause.

Many are sharing this chart in response to the recent elementary school shooting in Nashville. It’s natural to want to study these problems more in the wake of tragedies. After the Uvalde shooting last year, I tried to read as much as I could about the history of homicide and gun violence in the US, and to look at the research on what might work to reduce gun violence, which is summarized in a post I wrote last June.

That being said, I don’t think the chart above accurately characterizes the problem of elementary school shootings. It might accurately describe some broader problem, but it’s misleading with respect to the shooting we all just witnessed. The most important reason is that the definition of “children” here extends to 18- and 19-year-olds. Much of the gun-related homicides for “children” shown here are gang-related violence, not random school shootings at elementary schools. It’s not that we shouldn’t care about these deaths too — we very much should care — but the causes and solutions are entirely different from elementary school mass shootings.

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Life Expectancy By State 1990-2019

James Bailey3 Comments

I’m making a panel of historical life expectancy data by state available here:

Life Expectancy By State 1990-2019

It covers the years 1990 to 2019 for every US state, and has life expectancy at birth, age 25, and age 65. It includes breakdowns by sex and by race and ethnicity, though the race and ethnicity breakdowns aren’t available for every state and year.

This is one of those things that you’d think would be easy to find elsewhere, but isn’t. The CDC’s National Center for Health Statistics publishes state life expectancy data, but only makes it easily available back to 2018. The United States Mortality DataBase has state life expectancy data back to 1959, but makes it quite hard to use: it requires creating an account, uses opaque variable names, and puts the data for each state into a different spreadsheet, requiring users who want a state panel to merge 50 sheets. It also bans re-sharing the data, which is why the dataset I present here is based on IHME’s data instead.

The IHME data is much more user-friendly than the CDC or USMDB, but still has major issues. By including lots of extraneous information and arranging the data in an odd way, it has over 600,000 rows of data; covering 50 states over 30 years should only take about 1,500 rows, which is what I’ve cleaned and rearranged it to. IHME also never actually gives the most basic variable: life expectancy at birth by state. They only ever give separate life expectancies for men and women. I created overall life expectancy by state by averaging life expectancy for men and women. This gives people any easy number to use, but a simple average is not the ideal way to do this, since state populations aren’t exactly 50/50, particularly for 65 year olds. If you’re doing serious work on 65yo life expectancy you probably want to find a better way to do this, or just use the separate male/female variables. You might also consider sticking with the original IHME data (if its important to have population and all cause mortality by age, which I deleted as extraneous) or the United States Mortality DataBase (if you want pre-1990 data).

Overall though, my state life expectancy panel should provide a quick and easy option that works well for most people.

Here’s an example of what can be done with the data:

If states are on the red line, their life expectancy didn’t change from 1990 to 2019. If a state were below the red line, it would mean their life expectancy fell, which done did (some state names spill over the line, but the true data point is at the start of the name). The higher above the line a state is, the more the life expectancy increased from 1990 to 2019. So Oklahoma, Mississippi, West Virginia, Kentucky and North Dakota barely improved, gaining less than 1.5 years. On the other extreme Alaska, California, New York improved by more than 5 years; the biggest improvement was in DC, which gained a whopping 9.1 years of life expectancy over 30 years. My initial thought was that this was mainly driven by the changing racial composition of DC, but in fact it appears that the gains were broad based: black life expectancy rose from 65 to 72, while white life expectancy rose from 77 to 87.

You can find other improved datasets on my data page, and once again this life expectancy data is here: Life Expectancy By State 1990-2019

Don’t Look Back

Scott BuchananLeave a comment

On the Positivity Blog are no less than “67 Don’t Look Back Quotes to Help You Move on and Live Your Best Life”. Some of these sayings from notable folks include:

“Never look back unless you are planning to go that way.”
– Henry David Thoreau

“If you want to live your life in a creative way, as an artist, you have to not look back too much. You have to be willing to take whatever you’ve done and whoever you were and throw them away.”
– Steve Jobs

“There are far, far better things ahead than any we leave behind.”
– C.S. Lewis

“Don’t cry because it’s over, smile because it happened”  

– attributed to Dr. Seuss, though that attribution is heavily disputed

The Random Vibez offers another “60 Don’t Look Back Quotes To Inspire You To Move Forward”’ including “Don’t look back. You’ll miss what’s in front of you” and “I tend not to look back. It’s confusing”.   The Bible would add sayings such as, “Let your eyes look straight ahead; fix your gaze directly before you” (Proverbs 4:25); Paul wrote to the Philippians, “One thing I do: Forgetting what is behind and straining toward what is ahead, I press on toward the goal to win the prize for which God has called me”.

The Landy-Bannister Statue

What put me in mind of this whole theme of not looking back was seeing a bronze statue involving Roger Bannister. Sports buffs, and most educated people who are over 60, will know that he was the first man to break the four-minute mile. During many previous decades of trying, no human had been able to run that fast that long: that is a velocity of 15 miles per hour, sustained for a full four minutes. That is like a full sprint for most people, or a moderate bicycling speed. 

Bannister found that he was naturally a fast runner, and he employed scientific principles in his training. (He was a medical student at the time, and went on to become a noted research neurologist).  On May 6, 1954 Bannister finally cracked the four-minute mile, with a 3:59.4 time. As may be imagined, the crowd went wild.

Records, however, are made to be broken, and just 46 days later a rival runner, John Landy, ran the mile in just 3:57.9 to become the world’s fastest man. A few months after that Bannister and Landy ran head-to-head in the August, 1954 Commonwealth games in Vancouver. Landy was in the lead nearly the whole way, with a ten-yard lead by the end of the third lap. Bannister then started his signature kick and managed to catch up with Landy on the final bend. Landy must have heard footsteps, and at the end of the race glanced over his left shoulder to gauge Bannister’s position. That distraction slowed him just enough to allow Bannister to power past him on his right side. Landy’s time was still a respectable 3:59.6, but Bannister won with 3:58.8. Both runners later agreed that Landy would have won if he had not looked back. More on that race, including link to video of it, here.

This finish of this “Miracle Mile” race was immortalized by a larger-than-life bronze statue by Vancouver sculptor Jack Harman. Landy later quipped, “”While Lot’s wife was turned into a pillar of salt for looking back, I am probably the only one ever turned into bronze for looking back.”

Excess Mortality and Vaccination Rates in Europe

Jeremy HorpedahlLeave a comment

Much ink has been spilled making cross-country comparisons since the start of the COVID-19 pandemic. I have made a few of these, such as a comparison of GDP declines and COVID death rates among about three dozen countries in late 2021. I also made a similar comparison of G-7 countries in early 2022. But all such comparisons are tricky to interpret if we want to know why these differences exist between countries, which surely ultimately we would like to know. I tried to stress in those blog posts that I was just trying to visualize the effects, not make any claims about causation.

Here’s one more chart which I think is a very useful visualization, and it may give us some hint at causation. The following scatterplot shows COVID vaccination rates and excess mortality for a selection of European countries (more detail below on these measures and the countries selected):

The selection of countries is based on data availability. For vaccination rates, I chose to use the rate for ages 60-69 at the end of 2021. Ages 60-69 is somewhat arbitrary, but I wanted a rate for an elderly age group that was somewhat widely available. There is no standard source for an international organization that published these age-specific vaccine rates (that I’m aware of), but Our World in Data has done an excellent job of compiling comparable data that is available.

Note: I’m using the data on at least one dose of the vaccine. OWID also has it available by full vaccine series, and by booster, but first dose seemed like a reasonable approach to me. Also, I could have used different age groups, such as 70-79 or 80+, but once you get to those age groups the data gets weird because you have a lot of countries over 100%, probably due to both challenging denominator calculations and just general challenges with collecting data on vaccination rates. By using 60-69, only one country in my sample (Portugal) is over 100%, and I just code them as 100%. Using the end of 2021, rather than the most current data, is a bit arbitrary too, but I wanted to capture how well early vaccination efforts went, though ultimately it probably wouldn’t have mattered much.

Also: dropping the outliers of Bulgaria and Romania doesn’t change things much. The second-degree best fit polynomial still has an R2 over 0.60 (for those unfamiliar with these statistics, that means about 60% of the variation is “explained” in a correlational sense).

The excess mortality measure I use comes from the following chart. In fact, this entire post is inspired by the fact that this chart and others similar to it have been shared frequently on social media.

The chart comes from a Tweet thread by Paul Collyer. The whole thread is worth reading, but this chart is the key and summary of the thread. What he has done is shown the average and range of a variety of ways of calculating excess mortality. Read his thread for all the details, but the basic issues are what baseline to use (2015-2019 or 2017-2019? A case can be made for both), how to do the age-standardized mortality, and other issues. I won’t make a claim as to which method is best, but averaging across them seems like a fine approach to me.

For the y-axis in my chart, I just used the average for each country from Collyer’s chart. There are 34 countries in his chart, but in the OWID age-specific vaccination rates, only 22 countries were available the overlapped with his group. Unfortunately, this means we drop major countries like Italy, Spain, the UK, and Germany, but you work with the data you have.

For many sharing this and similar chart (such as charts with just one of those methods), the surprising (or not surprising) result to them is that Sweden comes out with almost the lowest excess mortality rate. Some approaches even put Sweden as the very lowest. Sweden!

Why is Sweden so important? Sweden has been probably the most debated country (especially by people not living in the country in question) in the COVID pandemic conversation. In short, Sweden took a less restrictive (some might say much less restrictive) approach to the pandemic. This debate was probably the most fevered in mid-to-late 2020, when some were even claiming that the pandemic was over in Sweden (it wasn’t). The extent to which Sweden took a radically different approach is somewhat overstated, especially in relation to other Nordic countries. And as is clear in both charts above, the Nordic countries all did relatively very well on excess mortality.

The bottom line from my first chart is that what really matters for a country’s overall excess mortality during the pandemic is how well they vaccinated their population. There seems to be a lot of interest on social media to rehash the debates about whether lockdowns (and lighter restrictions) or masks worked in 2020. But what really mattered was 2021, and vaccines were key. A scatterplot isn’t the last word on this (we should control for lots of other things), but it does suggest that a big part of the picture is vaccines (you can see this in scatterplots of US states too). It’s frustrating that many of those wanting to rehash the 2020 debates to “prove” masks don’t work, or whatever, either ignore vaccines or have bought into varying degrees of anti-vaccination theories. It’s completely possible that lockdowns don’t pass a cost/benefit test, but that vaccines also work very well (this has always been my position).

Why did Sweden have such great relative performance on excess mortality? Vaccines are almost certainly the most important factor among many that matter to a much smaller degree.

What About the US?

Note: for those wondering about the US, we don’t have the vaccination rate for ages 60-69 that I can find. Collyer also didn’t include the US in his analysis, it was only Europe. So, for both reasons, I didn’t include them in this post. The CDC does report first-dose vaccinations for ages 65+ in the US, though they top-code states at 95%. As of the end of 2021, here are the states that were below 95%: Mississippi, Louisiana, Tennessee, West Virginia, Indiana, Ohio, Wyoming, Georgia, Arkansas, Idaho, Alabama, Montana, Alaska, Missouri, Texas, Michigan, and Kentucky. These states generally have very high age-adjusted COVID death rates. Ideally we would use age-adjusted excess mortality for US states, but in the US that is horribly confounded by the rise in overdoses, homicides, car accidents, and other causes that are independent of vaccination rates (though they may be related to 2020 COVID policies — this is still a matter of huge debate).

The ACA and Entrepreneurship: The Importance of Age

James Bailey1 Comment

Thinking about one of my older papers today, since I just heard it won the Eckstein award for best paper in the Eastern Economic Journal in 2019 & 2020.

One big selling point of the Affordable Care Act was that by offering more non-employer-based options for health insurance, it would free people who felt locked into their jobs by the need for insurance. This would free people up to leave their jobs and do other things like start their own businesses. Did the ACA actually live up to this promise?

It did, at least for some people. The challenge when it comes to measuring the effect of the ACA is that it potentially affected everyone nationwide. If entrepreneurship rises following the implementation of the ACA in 2014, is it because of the ACA? Or just the general economic recovery? Ideally we want some sort of comparison group unaffected by the ACA. If that doesn’t really exist, we can use a comparison group that is less affected by it.

That’s what I did in a 2017 paper focused on younger adults. I compared those under age 26 (who benefit from the ACA’s dependent coverage mandate) to those just over age 26 (who don’t), but found no overall difference in how their self-employment rates changed following the ACA.

In the 2019 Eastern Economic Journal paper, Dhaval Dave and I instead consider the effect of the ACA on older adults. We compare entrepreneurship rates for people in their early 60’s (who might benefit from the availability of individual insurance through the ACA) with a “control group” of people in their late 60’s (who are eligible for Medicare and presumably less affected by the ACA). We find that the ACA led to a 3-4% increase in self-employment for people in their early 60’s.

Figure 1 from our 2019 EEJ paper

Why the big difference in findings across papers? My guess is that it’s about age, and what age means for health and health insurance. People in their 60s are old enough to have substantial average health costs and health insurance premiums, so they will factor health insurance into their decisions more strongly than younger people. In addition, the community rating provisions of the ACA generally reduced individual premiums for older people while raising them for younger people.

In sum, the ACA does seem to encourage entrepreneurship at least among older adults. At the same time, our other research finds that the employer-based health insurance system still leads Americans to stay in their jobs longer than they would otherwise choose to.

Itching to Change (Property Rights)

Zachary BartschLeave a comment

I live in southwest Florida where it is quite tropical. We don’t have four seasons. We mark the passage of time with the rainy season for 8 months and the dry season for 4 months. We also mark time with ‘season’. Season is when the snow-birds – those who live in places further north – migrate to and occupy Florida for about 4-5 months. During those times the roads are more crowded and the grocery store customers are less friendly. We can also mark the passage of time with mosquitos. January has fewer mosquitos. The rest of the year we know not to go outside at dusk.

Therefore, we have the Collier Mosquito Control District. This little government entity does several things. But I want to focus on spraying. On some nights, more so during the rainy season, the CMCD flies airplanes and sprays our inland bodies of water that are susceptible to mosquito infestation. Let’s put aside for the moment any alleged negative human health effects that spraying might cause.

I want to talk about taxes.

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Online Reading Onpaper

Zachary Bartsch4 Comments

We have six weekly contributors here at EWED and I try to read every single post. I don’t always read them the same day that they are published. Being subscribed is convenient because I can let my count of unread emails accumulate as a reminder of what I’ve yet to read.

Shortly after my fourth child was born over the summer, I understandably got quite behind in my reading. I think that I had as many as twelve unread posts. I would try to catchup on the days that I stayed home with the children. After all, they don’t require constant monitoring and often go do their own thing. Then, without fail, every time that I pull out my phone to catch up on some choice econ content, the kids would get needy. They’d start whining, fighting, or otherwise suddenly start accosting me for one thing or another – even if they were fine just moments before. It’s as if my phone was the signal that I clearly had nothing to do and that I should be interacting with them. Don’t get me wrong, I like interacting with my kids. But, don’t they know that I’m a professional living in the 21st century? Don’t they know that there is a lot of good educational and intellectually stimulating content on my phone and that I am not merely zoning out and wasting my time?

No. They do not.

I began to realize that it didn’t matter what I was doing on my phone, the kids were not happy about it.

I have fond childhood memories of my dad smoking a pipe and reading the newspaper. I remember how he’d cross his legs and I remember how he’d lift me up and down with them. I less well remember my dad playing his Game Boy. That was entertaining for a while, but I remember feeling more socially disconnected from him at those times. Maybe my kids feel the same way. It doesn’t matter to them that I try to read news articles on my phone (the same content as a newspaper). They see me on a 1-player device.

So, one day I printed out about a dozen accumulated EWED blog posts as double-sided and stapled articles on real-life paper.

The kids were copacetic, going about their business. They were fed, watered, changed, and had toys and drawing accoutrement. I sat down with my stack of papers in a prominent rocking chair and started reading. You know what my kids did in response? Not a darn thing! I had found the secret. I couldn’t comment on the posts or share them digitally. But that’s a small price to pay for getting some peaceful reading time. My kids didn’t care that I wasn’t giving them attention. Reading is something they know about. They read or are read to every day. ‘Dad’s reading’ is a totally understandable and sympathetic activity. ‘Dad’s on his phone’ is not a sympathetic activity. After all, they don’t have phones.

They even had a role to play. As I’d finish reading the blog posts, I’d toss the stapled pages across the room. It was their job to throw those away in the garbage can. It became a game where there were these sheets of paper that I cared about, then examined , and then discarded… like yesterday’s news. They’d even argue some over who got to run the next consumed story across the house to the garbage can (sorry fellow bloggers).

If you’re waiting for the other shoe to drop, then I’ve got nothing for you. It turns out that this works for us. My working hypothesis is that kids often don’t want parents to give them attention in particular. Rather, they want to feel a sense of connection by being involved, or sharing experiences. Even if it’s not at the same time. Our kids want to do the things that we do. They love to mimic. My kids are almost never allowed to play games or do nearly anything on our phones. So, me being on my phone in their presence serves to create distance between us. Reading a book or some paper in their presence? That puts us on the same page.

Steal My Paper Ideas!

James Bailey2 Comments

Since early in graduate school I’ve kept a running list of ideas for economics papers I’d like to write and publish some day. I’ve written many of the papers I planned to, and been scooped on others, but the list just keeps growing. As I begin to change my priorities post-tenure, I decided it was time to publicly share many of my ideas to see if anyone else wants to run with them. So I added an ideas page to my website:

Steal My Paper Ideas! I have more ideas than time. The real problem is that publishing papers makes the list bigger, not smaller; each paper I do gives me the idea for more than one new paper. I also don’t have my own PhD students to give them to, and don’t especially need credit for more publications. So feel free to take these and run with them, just put me in the acknowledgements, and let me know when you publish so I can take the idea off this page.

Here’s one set of example ideas:

State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.

You can find more ideas on the full page; I plan to update to add more ideas as I have them and to remove ideas once someone writes the paper.

Thanks to a conversation with Jojo Lee for the idea of publicly posting my paper ideas. I especially encourage people to share this list with early-stage PhD students. It would also be great to see other tenured professors post the ideas they have no immediate plans to work on; I’m sure plenty of people are sitting on better ideas than mine with no plans to actually act on them.

On Counting and Overcounting Deaths

Jeremy Horpedahl2 Comments

How many people died in the US from heart diseases in 2019? The answer is harder than it might seem to pin down. Using a broad definition, such as “major cardiovascular diseases,” and including any deaths where this was listed on the death certificate, the number for 2019 is an astonishing 1.56 million deaths, according to the CDC. That number is astonishing because there were 2.85 million deaths in total in the US, so over half of deaths involved the heart or circulatory system, at least in some way that was important enough for a doctor to list it on the death certificate.

However, if you Google “heart disease deaths US 2019,” you get only 659,041 deaths. The source? Once again, the CDC! So, what’s going on here? To get to the smaller number, the CDC narrows the definition in two ways. First, instead of all “major cardiovascular diseases,” they limit it to diseases that are specifically about the heart. For example, cerebrovascular deaths (deaths involving blood flow in the brain) are not including in the lower CDC total. This first limitation gets us down to 1.28 million.

But the bigger reduction is when they limit the count to the underlying cause of death, “the disease or injury that initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury,” as opposed to other contributing causes. That’s how we cut the total in half from 1.28 million to 659,041 deaths.

We could further limit this to “Atherosclerotic heart disease,” a subset of heart disease deaths, but the largest single cause of deaths in the coding system that the CDC uses. There were 163,502 deaths of this kind in 2019, if you use the underlying cause of death only. But if we expand it to any listing of this disease on the death certificate, it doubles to 321,812 deaths. And now three categories of death are slightly larger in this “multiple cause of death” query, including a catch-all “Cardiac arrest, unspecified” category with 352,010 deaths in 2019.

So, what’s the right number? What’s the point of all this discussion? Here’s my question to you: did you ever hear of a debate about whether we were “overcounting” heart disease deaths in 2019? I don’t think I’ve ever heard of it. Probably there were occasional debates among the experts in this area, but never among the general public.

COVID-19 is different. The allegation of “overcounting” COVID deaths began almost right away in 2020, with prominent people claiming that the numbers being reported are basically useless because, for example, a fatal motorcycle death was briefly included in COVID death totals in Florida (people are still using this example!).

A more serious critique of COVID death counting was in a recent op-ed in the Washington Post. The argument here is serious and sober, and not trying to push a particular viewpoint as far as I can tell (contrast this with people pushing the motorcycle death story). Yet still the op-ed is almost totally lacking in data, especially on COVID deaths (there is some data on COVID hospitalizations).

But most of the data she is asking for in the op-ed is readily available. While we don’t have death totals for all individuals that tested positive for COVID-19 at some point, we do have the following data available on a weekly basis. First, we have the “surveillance data” on deaths that was released by states and aggregated by the CDC. These were “the numbers” that you probably saw constantly discussed, sometimes daily, in the media during the height of the pandemic waves. The second and third sources of COVID death data are similar to the heart disease data I discussed above, from the CDC WONDER database, separated by whether COVID was the underlying cause or whether it was one among several contributing causes (whether it was underlying or not).

Those three measures of COVID deaths are displayed in this chart:

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