Dec 192012
 

As a follow-up to my recent post on the relationship between gun ownership and gun homicide in OECD countries, I have rolled my dataset (compiled from information published by gunpolicy.org) and my analysis script into a neat Stata package. If you want to recreate the tables and graphs, or otherwise want to play with the data just enter

net get http://www.kai-arzheimer.com/stata/guns

do guns-analysis

in your net-aware copy of Stata.

If you don’t like Stata, you can get the raw data (ASCII) from http://www.kai-arzheimer.com/stata/oecd-gun-deaths.txt . Enjoy!

Dec 162012
 

Following Friday’s events, the attached image went viral. The figures (if correct) are certainly suggestive, but obviously, the population at risk varies widely between countries. What we need is the gun-related homicide rate for a sample of comparable countries. I headed over to the Brady Campaign, which had created the image, but could not easily find comparative data. Next, I tried the very useful Gun Policy Project. Their website features very detailed country profiles, but unfortunately no ready made tables, and so I spent a lazy hour keying in gun-related and total homicide  numbers as well as possession rates (guns per 100 people) for 34 OECD countries.

Looking at the data, I decided to remove Mexico from the sample: The civil war like situation in the North means that relatively few guns are enough to kill about 11,000 people each year in a population of just over 112 million people. Put differently, about one in 10,000 Mexicans is shot dead each year. Thankfully, no other OECD member is in a comparable predicament.

Next, I created boxplots for the distribution of possession rates, gun-related and total homicide rates.

Possession, total homicide and gun-related homicide rates for OECD members

Possession, total homicide and gun-related homicide rates for OECD members

The US of A is clearly an outlier in every respect. I was somewhat surprised by Estonia’s high homicide rate. While the country’s population is small at 1.3 million people so that random fluctuation could have an impact,  a rate that is roughly six times the median seems excessively high.

Next, I specified a Negative-Binomial model of gun homicide counts as a function of the gun possession rate, controlling for the population at risk of being shot dead. Yes, I know this is dodgy with a small, non-random sample:

Negative binomial regression                      Number of obs   =         33
                                                  LR chi2(1)      =       8.17
Dispersion     = mean                             Prob > chi2     =     0.0043
Log likelihood = -162.78208                       Pseudo R2       =     0.0245

------------------------------------------------------------------------------
gunhomicides |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    possrate |   .0209951   .0083522     2.51   0.012      .004625    .0373652
       _cons |  -12.85624   .2292694   -56.07   0.000    -13.30559   -12.40688
ln(popula~n) |          1  (exposure)
-------------+----------------------------------------------------------------
    /lnalpha |  -.1823302    .228441                     -.6300664     .265406
-------------+----------------------------------------------------------------
       alpha |   .8333261   .1903659                      .5325565     1.30396
------------------------------------------------------------------------------
Likelihood-ratio test of alpha=0:  chibar2(01) = 2361.65 Prob>=chibar2 = 0.000

As expected, gun possession raises the risk of being shot dead significantly. According to the model, each additional gun per 100 citizens increases the relative risk by exp(0.021) = two per cent (careful: If the initial risk is very low, that means that you are still quite safe).

These findings are, however, largely driven by the US with their very high possession and homicide rates. If they are excluded from the sample, the effect of gun possession is much less pronounced:

Negative binomial regression                      Number of obs   =         32
                                                  LR chi2(1)      =       0.09
Dispersion     = mean                             Prob > chi2     =     0.7660
Log likelihood = -151.9413                        Pseudo R2       =     0.0003

------------------------------------------------------------------------------
gunhomicides |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    possrate |   .0043764   .0148325     0.30   0.768    -.0246948    .0334475
       _cons |  -12.61879    .298236   -42.31   0.000    -13.20332   -12.03426
ln(popula~n) |          1  (exposure)
-------------+----------------------------------------------------------------
    /lnalpha |  -.1926419   .2319586                     -.6472725    .2619886
-------------+----------------------------------------------------------------
       alpha |   .8247772   .1913142                      .5234716    1.299512
------------------------------------------------------------------------------
Likelihood-ratio test of alpha=0:  chibar2(01) = 2318.53 Prob>=chibar2 = 0.000

And yet, given the countries the number of gun homicides in the US is massively underestimated 
by the model:
Negative binomial models for gun possession/gun homicides w and w/o US

Negative binomial models for gun possession/gun homicides w and w/o US

Gun possession is easily comparable across countries but a less than perfect measure of the underlying regime. As a ratio, it does not capture the actual distribution/accessibility of guns, nor does it pick up differences in licensing laws or the availability of automatic weapons. As can be seen from the dashed line, outside the US more guns still mean more killings, but there is a lot of noise in that relationship. There are, however, three countries with very low possession rates of less than 1.5. Unsurprisingly, they also have extremely low gun homicide rates. A final, nonparametric plot picks up this relationship:

Nonparametric model for gun possession / gun homicide

Nonparametric model for gun possession / gun homicide

So what’s the implication for the US? If the model was true and the US would bring down its possession ratio to the OECD median of 13.5 per 100 citizens, the model predicts 1071 gun homicides, as opposed to 9,146 actual cases (2009). That would be 8,075 lives saved.

But the model does not fit very well, and we might be better off with a very naive, non-parametric estimate. If the US  became less like the US and more like the rest of the OECD, its gun homicide rate might come down to the OECD median. That would amount to 846 people murdered using a gun, less than 10 per cent of the current figure. Of course, some of those people who would be spared the bullet might be killed by other means, but that is arguably more difficult. And this is just homicides. If you add manslaughter, suicides and accidents, it seems safe to assume that the NRA/Second Amendment culture costs at least 8,000 lives a year.

 

Nov 032008
 

The US might face unprecedented levels of turnout in tomorrow’s election, but historically, the non-voters are the biggest camp in American politics. One intriguing explanation for this well-known fact is that low turnout could be a consequence of the very high (by any standard) levels of income inequality: because voters lack experience with universalistic institutions, they are less likely to adopt norms and values that foster participation in elections. This is the gist of an article that appeared recently (by social science standards) in the British Journal of Politics and International Relations. While the thesis is interesting enough, I did not find the evidence (design, operationalisation, statistical model) particularly convincing and consequentially embarked on a major replication exercise. As it turned out, there are indeed major problems with the original analysis, including a rather problematic application of the ever popular time-series cross-sectional approach (aka Beck&Katz). Last week, my own article on the (non-)relationship between inequality and turnout has finally appeared in the BJPIR. If you don’t have access to the journal, you can still download the preprint version (“Something Old, Something New, Something Borrowed, Something True?”) from my homepage. And if you in turn find this rather unconvincing, you can download the replication data for the various inequality/turnout models and do your own analysis. Enjoy.
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