Economist Writing Every Day

Two recent essays push back against the concept of “disinformation” in thoughtful but, I believe, ultimately incorrect ways.

Martin Gurri is primarily concerned with government trying to stamp out what it views as disinformation. I am concerned about that too, but there are ways for private actors to correct bad information too.

Dan Klein (my friend and professor in grad school) argues that most labeling of “disinformation” or “misinformation” is not really about information, but instead about knowledge. I agree that sometimes this is true. But sometimes it is not true. Sometimes we really are talking about information. And sometimes the information is about extremely important topics.

As I search through my own Twitter history for these terms, I see that there is overwhelmingly one period of time and one piece of information that I used them for: the total number of deaths in the United States in 2020. If you can think way back to the fall and winter of 2020/early 2021, you might recall that we were just finishing up the first year of the pandemic, and we were also going through one of the worst periods in the pandemic. Vaccines were now starting to become widely available as we got into 2021, and people were starting to make person decisions about whether to “get the jab.”

The number of total deaths in 2020 was an important number. There was still a lot of uncertainty about exactly how bad the pandemic was, or (to a small but vocal minority) whether the pandemic was even “real.” The data was crucial to this debate. Of course, once we have the data, we must interpret it. This is one of Klein’s main points, and a good one. But if we aren’t starting from a common baseline of true information, there is really no point in discussions based on interpretations of those different apparent realities. We will, by definition, be “talking past each other.”

So what were people saying about total deaths in 2020 during this moment of importance in late 2020/early 2021?

This Tweet was typical (it’s still up, and no correction was ever issued, so far as I can tell):

The author is clear in their point: 2020 is going to look like a normal year in terms of deaths. No pandemic! Tweets like this were widely shared. But the real data was easily available to see. For example, here is the data on total deaths you could find at the excellent Our World in Data site available on the same day as the above Tweet. Clearly, something different was happening in 2020.

And with the benefit of hindsight, we can clearly see that total deaths in 2020 were well above the prior levels, by over a half million deaths, an increase of over 18 percent. 2021 and 2022 too, tragically (data is from the extremely useful CDC WONDER database):

Of course, having that baseline of true information now does require interpretation and judgement. How many of these excess deaths were truly caused by the COVID virus? How many were caused by government policies that restricted human activity? How many could have been prevented by the vaccine which was just becoming available? All of these are extremely important questions. They should be debated and discussed vigorously. But they require us to have some agreement on some basic factual information about the world.

Here’s another Tweet (now from a suspended account, for repeatedly threatening others, not necessarily for this COVID disinformation) along the same lines, but much more concise:

According to the Tweet, 2020 was a normal year for deaths. Reopen America! And perhaps we should have reopened America (though to a large extent, we already had by December 2020), but we need to have an accurate debate based on true information. The information presented in that Tweet was just wrong, as you could clearly find by going to the CDC website on that very day (as I did) and see that over 3.1 million deaths had already been reported in 2020 (eventually growing to almost 3.4 million).

But was it just a few Tweets? Here’s a detailed article from the American Institute for Economic Research published in late November 2020, based on a presentation by a professor at Johns Hopkins. Here is how the AIER essay summarized the article (bold in the original): “These data analyses suggest that in contrast to most people’s assumptions, the number of deaths by COVID-19 is not alarming. In fact, it has relatively no effect on deaths in the United States.”

No effect on deaths! AIER later added a disclaimer, after receiving significant pushback on social media, to effect which said that this is just preliminary and needs more investigation. But it didn’t, and the appropriate context could have been provided: by late November 2020, it was clear that deaths in 2020 were well above normal. The concluding section from the AIER article is “What Do We Do With This Information?” That’s the interpretation part. But the interpretation was based on false data! And we knew this at the time, as total deaths were already about 300,000 above normal levels.

Perhaps I am belaboring an old point. At this juncture, I don’t think anyone denies that 2020 and 2021 were record years for mortality in recent US history. But at the time, people were denying this fact. It was misinformation, that is, wrong information (easily verifiable). Moreover, it was also disinformation, in that it was intended to mislead you to a particular conclusion, even though the authors knew (or really should have known, with some basic fact checking) that the information was wrong. At least that AIER essay was a bit cautious, as the author clearly knew this was a controversial claim with enormous implications. Not so for very prominent right-wing sites like Gateway Pundit used no such caution when reporting on the same “information.”

Now that we have true information about total deaths, we can hopefully start to carefully analyze those important questions about causes that I mentioned earlier. But going forward, I do think it is useful to label certain claims as misinformation or disinformation, though we should not throw the terms around cavalierly, boy-cries-wolf style.