How Many Deaths Are Too Many?

Earlier this year, in a blog post on Faster Than 20 about George Floyd, I tried to point out that, as terrible and visceral as his murder was, the overall racial disparity in police killings should feel far more horrifying. But, I explained:

No one has ever looked at a number and taken to the streets. There are lots of mental hoops required to make sense of that number, to trust its implications, and then to get outraged by it.

Later, in an exchange with a colleague in the comments, I wrote:

There’s also a larger question worth asking about whether the 1,000 police killings a year is too high, regardless of what you think of the racial disparity, which gets you into questions about police militarization and policies for community safety in general.

I suppose now is as good a time as any to ask the larger question: Is 1,000 police killings a year too high?

All things being equal, my first guess as to what the “right” number of police killings should be is zero. Hard to argue with that, right?

Well, that depends. Consider a school shooting, for example. If somebody is spraying bullets at a school with the clear intent to kill as many people as possible, I definitely want the police to shoot and kill that person. It’s not hard to think of other situations where a police killing is not only justified, but where it might save many other lives.

So the “right” number of police killings is probably greater than zero. But how much greater?

I might try going down that rabbit hole another day, but I want to pivot to a different question: How many COVID-19 deaths are too high?

As of today, 240,000 people have officially died of COVID-19 in the U.S. (This doesn’t count indirect fatalities, which would put the number well over 300,000.) Over the past week, we’ve averaged 940 deaths a day from COVID-19. On the one hand, it’s less than half of our peak on April 24, when we averaged 2,240 deaths a day. On the other hand, the number is trending in the wrong direction.

Is a thousand deaths a day too much? What would an “acceptable” number of daily deaths be?

Let’s try to think of this question in a different way. How many car deaths per day are too many? How many car deaths per day are “acceptable”? Don’t do any research. Just try to come up with two numbers and some explanation as to how you came up with them. Don’t worry about being “right.” This is simply an experiment.

Got an answer? Okay, suppose that you’re surpassing your “too many” number. What would you do to get those numbers down?

Think about this for a second. Now compare your numbers from the 2016 U.S. numbers listed in this Wikipedia page.

I don’t have good answers to any of these questions. (I’d love to hear yours in the comments below.) I think that a thousand deaths a day is too many, but I really can’t justify the tradeoffs.

I do know two things. First, human intuition is pretty much useless when it comes to these questions. Joseph Stalin supposedly said, “The death of one man is a tragedy. The death of millions is a statistic.” It turns out that this is a fact of human nature. It’s known as psychic numbing.

Second, economists estimate that the value of one human life in the U.S. is roughly $10 million. So 240,000 deaths is equivalent to the loss of $2.4 trillion, over 10 percent of our GDP last year. By these admittedly crass and undoubtedly wrong estimates, it seems like a 10 percent drop in GDP is worth the tradeoff of saving 240,000 lives.

Why Are We Afraid of Data?

My friend, Gbenga Ajilore, is an economics professor. Last month, he gave a great talk at AlterConf in Chicago entitled, “How can open data help facilitate police reform?” It concisely explains how data helps us overcome anecdotal bias.

I was particularly struck by his point about how we need police buy-in for this data to be truly useful, and I was left with a bit of despair. Why is buy-in about the importance of data so hard? This should be common sense, right?

Clearly, it’s not. Earlier this year, I expressed some disbelief about how, in professional sports, where there are hundreds of millions of dollars riding on outcomes, there is still strong resistance to data and analytics.

On the one hand, it’s incredible that this is still an issue in professional sports, 14 years after Moneyball was first published and several championships were won by analytics-driven franchises (including two “cursed” franchises, the Boston Red Sox and the Chicago Cubs, both led by data nerd Theo Epstein).

On the other hand, it’s a vivid reminder of how hard habits and groupthink are to break, even in a field where the incentives to be smarter than everyone else come in the form of hundreds of millions of dollars. If it’s this hard to shift mindsets in professional sports, I don’t even want to imagine how long it might take in journalism. It’s definitely helping me recalibrate my perspective about the mindsets I’m trying to shift in my own field.

The first time I started to understand the many social forces that cause us to resist data was right after college, when I worked as an editor at a technology magazine. One of my most memorable meetings was with a vendor that made a tool that analyzed source code to surface bugs. All software developers know that debugging is incredibly hard and time-consuming. Their tool easily and automatically identified tons and tons of bugs, just by feeding it your source code.

“This is one of the best demos I’ve ever seen!” I exclaimed to the vendor reps. “Why isn’t everyone knocking on your door to buy this?”

The two glanced at each other, then shrugged their shoulders. “Actually,” one explained, “we are having a lot of trouble selling this. When people see this demo, they are horrified, because they realize how buggy their code is, and they don’t have the time or resources to fix it. They would rather that nobody know.”