But it's always great to see a professional like Dr. John Lott step in.

I've talked a bit about the study in JAMA that claims permissive gun laws lead to more children being killed. I've poked my holes in it. Lott, however, is someone who conducts studies, which means he's infinitely more qualified than I to look at a study and tear it to pieces, and that's pretty much what he does.

We have previously written extensively on the false claim made in the first sentence of this study: “Firearm deaths are now the leading cause of death among US children and adolescents” (see here and here).

Of course, I have too, but as I didn't have the full study, I didn't get into it. Lott does and did, and he's absolutely right to point it out. If that's your starting premise, you're already screwing up.

Unlike typical research, which compares crime or suicide rates before and after states change their laws and contrasts those changes with states that didn’t alter their laws, this study ignores how laws change over time. It takes what could be panel data which allows one to account for average differences across states and years (so-called fixed effects). The paper limits there discussion to a purely cross-sectional comparison. The purely cross sectional comparison cannot be used for any discussion of causation. They don’t even try to account for basic factors like law enforcement practices—such as arrest and conviction rates, imprisonment rates, or the death penalty—that influence crime. Nor do they account for any factors that might explain changes in suicides or accidental gun deaths in the 2011 to 2023 period.

Bingo.

They didn't really look at anything beyond what the anti-gun groups said about various states' gun laws, look at some numbers, and reach a conclusion that, I believe, they already had in mind. Especially since their jumping off point was the McDonald decision, which really didn't do much to state or even local gun laws beyond Chicago's de facto handgun ban.

It was a lazily constructed study that was only published in such a prestigious journal because that journal prefers work that advances the anti-gun narrative.

The study categorizes the level of gun control laws in each state into one of three broad categories and assumes that there laws remain constant over time, and lumps many different laws together in an arbitrary manner. Are these additive? Do we simply add a concealed-carry law to a safe-storage law to universal background checks? For these gun control advocates, is a red flag law twice as important as having lots of gun-free zones? There is so much arbitrariness in how a measure that combines these different laws and even what laws to include. How did they decide to have eight strict gun-control law states, eleven permissive states, and 30 most permissive states? Why not 1/3rd in each of these different categories?

The methodology falters in several ways. It relies on the epidemiological concept of “excess deaths,” commonly used to gauge the impact of diseases like Covid-19. The authors applied Poisson regression, using only a time trend as a control variable, to estimate expected deaths for three state groups. They labeled the gap between predicted and actual deaths as “excess deaths,” attributing these to permissive state laws. This gap, however, could simply reflect error or residuals, encompassing random error, omitted variables, heteroskedasticity, serial correlation, measurement errors, and other statistical challenges inherent in such analyses.

Among these issues, omitted control variables stand out as the most glaring flaw. The authors failed to account for Covid-19, which spiked murders while reducing other street crimes. A basic dummy variable could have mitigated this, but a more refined approach would have incorporated variables for each state’s lockdown policies. Other neglected factors include standard crime equation variables: police presence, arrests, executions, population density, unemployment, income, alcohol consumption, poverty, and welfare. These omissions render the estimates biased and inconsistent, leading to unreliable forecasts.

Look, I can't go through the whole thing because, well, it's long and I have other things to write today.

What I will say, though, is that Lott gets deep into the weeds on many, many problems with this study, and that's because there are many, many problems. It's downright pathetic how badly it was constructed.

Which wouldn't be a huge issue were it not for the fact that this is now being touted in the media as evidence that less restrictive gun control laws result in child deaths.

If this were just something that was laying down some suggested directions for future research, I wouldn't gripe as much, but it's not. The media and the activist class (but I repeat myself) don't really understand research that well. I'm not an expert on it by any stretch, but I'm far better informed on the subject than most.

In 2015, science journalist John Bohannon scammed the media severely. He convinced countless media outlets that chocolate could help you lose weight. He conducted a study--it was a real study, just one that was intentionally constructed poorly--and then ran with the outcome, published it in a pay-to-play journal, then sent out a press release to everyone and their brother.

Journalists all over the world ran with it. Almost none actually looked at the research or even asked questions of the researchers. They just bought it hook, line, and sinker.

And Bohannon targeted science journalists, people who specifically deal with scientific research. Most were clueless, of course, but they're more likely to pick up some stuff than most.

Imagine the odds of someone who covers guns or general news actually understanding what this study really means.

And you're going to be hard-pressed to convince me that so-called gun researchers are unaware of this. They know it, they construct studies with that understanding, and then push out bad data to affect a political outcome that they prefer.

But the truth is that the studies in question are just plain garbage, as we've seen all too often.