The Impact of Arming Teachers

Since the tragedy in Newtown, there’s been talking of arming teachers.

Reading things like this got me wondering if we could estimate the impact of this because surely it can’t be a free lunch.  Then I saw this great graphic about gun deaths vs gun ownership.  And he made the data available, so I built a simple linear model using R:

deaths = read.table("deaths.csv", sep="\t", header=T)
oecd = read.table("oecd.csv", sep="\t", header=T)
data = merge(guns, deaths, by="Country")
data$OECD = data$Country %in% oecd$Country
data.oecd = subset(data, data$OECD==T)

p <- ggplot(data = data.oecd, aes(x = Guns, y = Deaths))
 + geom_smooth(method="lm", se=FALSE, color="blue", formula= y ~ x) + geom_point()

mylm = lm(Guns~Deaths, data=data.oecd)


Deaths (per 100k people) = 0.599 + 0.089 * Guns (per 100 people).

Here again is a graph of the data with a plot of the linear model added:


This model has an R2 = 0.384 and p = 0.00015.  The residuals of this model are:


Not terrible – something’s going on with Mexico (row #42).  And it’s overestimating slightly for larger values of x (Guns), but probably due to Mexico.

It was then a simple matter to look up the number of teachers according to the U.S. Census:  7.2 million.

So, if we arm each teacher in American that results in 7.2 mil / 250 mil * 100 = 2.88 additional guns per 100 people.  Plug that into the above linear model and we get:

2.88 * 0.089 = 0.2564 additional deaths per 100k people.  So in the U.S. that translates to :

250 million/100k  * 0.2564 = 641 additional deaths

Look, I would consider this to be a toy model.  The underlying data is from different points in time, there’s the Mexico thing, and also 641 is only an average – could be more; could be less.

My point is that we should use data and assess the net impact of any actions we take to prevent school shootings.


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