Using this line of regression, you might even say that, in a highly developed country, the optimal number of firearms per 100 people is the y-inercept of this line, at 5.61 guns or fewer per 100 people. US has the 5th highest HDI, making it one of the world's most developed countries, so it falls squarely in this category.
Above that optimal number of guns, the line of regression shows that there is about 1 death for every 11,300 guns in a highly developed country. In other words, if we had a gun buyback program, for every 11,300 guns that were turned in, 1 human life will be saved.
The correlation between guns and deaths becomes weaker as you start to include countries with progressively lower HDI. But the correlation was still strong at R2=.75 for the top 33 countries Wikipedia had the relevant data on. This line of regression, which has a less strong correlation, suggests that the optimal number of guns is zero (since you can't have a negative number of guns), and above that, 1 human life is lost for every 12,900 guns owned. Still in the same ballpark as before.
Note that the US falls just above the line of regression in each case, suggesting that this relationship holds true for us as well.
Here's the data used to construct the above graphs, all taken from Wikipedia on December 5, 2015:
Country | HDI (Human Development Index) |
HDI Rank
|
Guns/100 (2014)
|
Gun deaths/100,000/year
|
Year of Calculation
|
Norway | 0.944 | 1 | 31.3 | 1.78 | (mixed) |
Australia | 0.933 | 2 | 21.7 | 0.86 | -2011 |
Switzerland | 0.917 | 3 | 45.7 | 2.91 | (mixed) |
Netherlands | 0.915 | 4 | 3.9 | 0.46 | -2010 |
United States | 0.914 | 5 | 112.6 | 10.5 | -2013 |
Germany | 0.911 | 6 | 30.3 | 1.24 | -2010 |
New Zealand | 0.91 | 7 | 22.6 | 1.45 | (mixed) |
Canada | 0.902 | 8 | 30.8 | 2.22 | (2007-2011) |
Singapore | 0.901 | 9 | 0.5 | 0.16 | (mixed) |
Denmark | 0.9 | 10 | 12 | 1.28 | -2011 |
Sweden | 0.898 | 12 | 31.6 | 1.47 | -2010 |
Iceland | 0.895 | 13 | 30.3 | 1.57 | (mixed, incomplete) |
UK | 0.892 | 14 | 6.6 | 0.26 | -2010 |
South Korea | 0.891 | 15 | 1.1 | 0.06 | (mixed) |
Japan | 0.89 | 17 | 0.6 | 0.06 |
(mixed)
|
Israel | 0.888 | 19 | 7.3 | 1.87 |
-2009
|
Taiwan
|
0.882 | 20 | 4.6 | 0.87 |
(mixed)
|
France
|
0.884 | 20 | 31.2 | 3.01 |
-2009
|
Luxembourg
|
0.881 | 21 | 15.3 | 2.02 |
(mixed)
|
Belgium
|
0.881 | 21 | 17.2 | 2.42 |
-2006
|
Austria
|
0.881 | 21 | 30.4 | 2.95 |
(mixed)
|
Finland
|
0.879 | 24 | 29.1 | 3.64 |
-2010
|
Slovenia
|
0.874 | 25 | 13.5 | 2.49 |
(mixed)
|
Italy
|
0.872 | 26 | 11.9 | 1.28 |
-2009
|
Spain
|
0.869 | 27 | 10.4 | 0.62 |
(mixed)
|
Czech Republic
|
0.861 | 28 | 16.3 | 1.76 |
-2010
|
Greece
|
0.853 | 29 | 22.5 | 1.64 |
(mixed)
|
Qatar
|
0.851 | 31 | 19.2 | 0.15 |
(incomplete)
|
Cyprus
|
0.845 | 32 | 36.1 | 0.96 |
(mixed)
|
Estonia
|
0.84 | 33 | 9.2 | 2.54 |
-2010
|
Poland
|
0.834 | 35 | 1.3 | 0.25 |
-2010
|
Slovakia
|
0.83 | 37 | 8.3 | 1.75 |
-2010
|
Portugal
|
0.822 | 41 | 8.5 | 1.77 |
-2010
|
Sources:
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