Consensus and disagreement on Multiple Victim Public Shootings (Correction to my earlier work also included)

I found this important series of quotes in an article by Jake Berry at the Nashua Telegraph.  The amazing thing to me is that you can see such a very consistent pattern and all these individuals still just think that it is due to randomness.

. . . Once again, Lott’s findings show that each mass shooting, except the Giffords incident, took place in “gun-free zones.”
“Killers go where victims can’t defend themselves,” Lott wrote last week in an email to The Telegraph, using this year’s Aurora movie theater shooting as an example. “Out of seven theaters showing the Batman movie premiere within 20 minutes of the suspect’s apartment, only one banned permitted concealed handguns. The suspect didn’t go to the closest nor the largest, but to the one that banned self-defense. Time after time, the story is the same.”
On the whole, Lott’s colleagues – both in the media and academia – don’t dispute his findings.
“I suspect that most places that mass public shootings could logically occur are ‘gun-free zones’ either determined by the government (schools) or by private businesses and institutions,” David Hemenway, director of the Injury Control Research Center at Harvard University, wrote in an email.
But they do debate Lott’s conclusions, and Hoell’s point that a location’s “gun-free” status actually attracts shooters.
“There is no evidence, to our knowledge, of any mass shooter specifically selecting his location based on its likelihood of being ‘gun-free,’” Mary Vriniotis, a research specialist for the Harvard school, wrote in an email.
“Schools might be a likely target because that is where a mass of people congregate and those people involve a lot of troubled adolescents who may harbor bad feelings toward the people there who bullied them, were unfair to them, etc,” added Daniel Webster, director of Center for Gun Policy and Research at Johns Hopkins University. “The shooters in these instances didn’t say, ‘Hey, I’ll find a gun-free zone where I can shoot a lot of people.’ No, they went to a place for reasons wholly unrelated to gun-free zones.”

Some of the documentation of these cases can be found in my original research with Bill Landes that studied the period from 1977 to 1999 and it is available here.  Here is the problem with Hemenway's and Webster's response: Whether it is something like the Colorado movie theater shooting where the killer went to the single movie theater out of seven within a twenty minute drive of the killer's apartment that banned concealed handguns or the various mall shootings where the killer went to the one place where guns were banned, there were lots of similar targets and the killers went to the place where victims couldn't defend themselves.  In right-to-carry states people can usually carry their concealed handguns almost anywhere and yet the attacks keep taking place in those small areas where guns are banned.  The paper with Landes and my subsequent research continues to show that this isn't random. As to Vriniotis' claim, I have also found other evidence that in at least a few cases, such as Columbine, the killers knew about the gun free zone and even actively opposed legislation letting potential victims carry guns.

Some of my posts with information on the Colorado movie theater shooting, the Sikh Temple shooting in Wisconsin, the business shooting in Wisconsin, Mall shootings (see here, here, and here), cafe and coffee shop shootings (here), and other places (here and here).  I have a long list of over 325 other postings here and here.

Based on Jake's research it appears that I had one case classified incorrectly (the attack at the International House of Pancakes restaurant in Carson City, Nevada in 2011) so that there was a second case in the US where an attack occurred where guns weren't banned. What appears to have happened there is that one of the five who died was the killer himself and I don't count the death of the killer in my totals of those killed.  Secondly, the fourth victim who died after the attack (one of the many original stories that I used is here (original post here)) and I hadn't gone back to double check on that case, as I admittedly should have done.

So what can the US learn from other developed countries regarding guns and crime

Charles Blow recently claimed in the New York Times: “America has the highest gun homicide rate, the highest number of guns per capita . . . .”  On Sunday, the New York Times quotes researcher David Hemenway as claiming: “Generally, if you live in a civilized society, more guns mean more death.”  CNN’s Piers Morgan believes: “America has the worst incidents of gun murders of any of what they call the civilized world.”

If you look at just the countries used in the Small Arms Survey, you get this.

Data available here.

Note that it is somewhat misleading to speak of a US homicide rate as 3 percent of the counties in the US account for over 70 percent of the murders (they have about 23 percent of the population).

Much is made of comparing some rather arbitrarily defined "civilized" nations, but what can Americans learn from these nations?   If the non-US developed nations show anything, even with the extremely questionable data that Charles Blow at the New York Times apparently trusts regarding gun ownership rates, it shows that higher gun ownership means lower homicide or no change in gun homicide rates.  He just hadn’t even bothered to graph out the numbers.

There is a real problem in using cross-sectional data.  Below is part of a long discussion in The Bias Against Guns, Chp. 5 (More Guns, Less Crime also has a long discussion in Chp. 2).  Take a simple example.  Suppose for the sake of argument that high-crime countries are the ones that most frequently adopt the most stringent gun control laws. What if gun control actually lowered crime, but not by enough to reduce rates to the same low levels prevailing in the majority of countries that did not adopt the laws. Looking across countries, it would then falsely appear that stricter gun control resulted in higher crime. Economists refer to this as an “endogeniety” problem. The adoption of the policy is a reaction to other events (that is, “endogenous”), in this case crime. To resolve this, one must examine how the high-crime areas that chose to adopt the controls changed over time —not only relative to their own past levels but also relative to areas that did not institute such controls. 

Unfortunately, many contemporary discussions rely on misinterpretations of cross-sectional data. The New York Times recently conducted a cross-sectional study of murder rates in states with and without the death penalty, and found that “Indeed, 10 of the 12 states without capital punishment have homicide rates below the national average, Federal Bureau of Investigation data shows, while half the states with the death penalty have homicide rates above the national average” (Raymond Bonner and Ford Fessenden, “States With No Death Penalty Share Lower Homicide Rates,” New York Times, September 22, 2000, p. A1.).  However, they erroneously concluded that the death penalty did not deter murder. The problem is that the states without the death penalty (Alaska, Hawaii, Iowa, Maine, Massachusetts, Michigan, Minnesota, North Dakota, Rhode Island, West Virginia, Wisconsin, and Vermont) have long enjoyed relatively low murder rates, something that might well have more to do with other factors than the death penalty. Instead one must compare, over time, how murder rates change in the two groups – those adopting the death penalty and those that did not.

Of course, I have other problems with the New York Times discussion.  For example, the rates of gun ownership for Switzerland and Israel are ridiculously low.  This survey excludes all the military weapons kept in Swiss homes in 2007 because they were technically owned by the government.  Israeli guns are also excluded for the same reason.  But if people have possession of guns in their homes for decades, the issue should be that public possession, not who technically owned the guns.  My point above was that even if those numbers are taken as given, you still find the opposite relationship from what the New York Times was claiming.  Of course, if you fix them, you will get a negative relationship across all developed countries even with the US observation included.  The reason that the Small Arms Survey messes up those two entries is that Israel and Switzerland are very low murder rate countries and giving them their true values would pull down the regression line a lot.  Putting the US all by itself out there at 88 firearms per 100 people drives the supposed positive relationship claimed for developed countries (see below).

The graph showing all non-US countries is shown here.

So what if we asked a different question?  Including the US in the data shows the absurdity of the Small Arms Survey measure of gun ownership in 2007.  They define it in such a way to exclude the military weapons in Swiss homes and to exclude most Israeli guns because the government technically owns them.  The Small Arms Survey claims that there are only 7 guns per 100 people in Israel, when up to 15 percent of the adult Jewish Israeli population has been able to openly carry handguns.  Virtually all guns in Israel are technically owned by the government, but Israelis may have possession of a gun for decades.  It seems that possession of the gun and not technical ownership is what is the real question here.  Switching either or both of these countries so that they had a higher gun ownership rate than the US would offset their bias for the US rate. In any case, despite my objections to both cross-sectional data and the obviously bogus Small Arms Survey measure of gun ownership, here are the results with the US included.  Doing this leaves the result for the world essentially unchanged and makes the relationship for OECD countries equal zero.

A note on David Hemenway

Since I get comments regarding Hemenway's NEJM review of the 1998 edition of my book, More Guns, Less Crime, I thought that I would note that there are many problems with it and I respond to some of the claims in the Second edition of MGLC from 2000.  (For other issues with Hemenway, please see here).  Here is one example that I discuss in MGLC.

Problems with the latest Miller, Hemenway, Azrael study on guns

The New York Times reports yesterday that a new study from Miller, Hemenway, and Azrael claims: "States with the greatest number of guns in the home also have the highest rates of homicide, a new study finds. . . . " Well, I have just spent a short time looking at the study, but there are some of things that are pretty obvious: 1) They excluded the District of Columbia without any explanation, 2) they use other crime rates to explain the homicide rate (by the way, they don’t use anything like an arrest or conviction rate, nothing to do with law enforcement), 3) they use purely cross-sectional data that never allows one to properly control for what may cause differences in crime rates, and 4) data from different years is used without any explanation (for the sake of argument I will use what they did, but it is weird to have the unemployment rate from 2000 to explain the homicide rate from 2001 to 2003, etc.). The data for a panel test on this is readily available from the sources used in their paper, though I have only collected the data to redo the estimates for 2001 that they use (why is it that these papers where one can put together the data in an afternoon get any serious attention). Why they only looked at the CDC data for 2001 when it is available for many other years is a bit of a puzzle. Since Miller and Hemenway have refused in the past to let me look at their data, I didn't bother this time and simply put the data together myself.

The bottom line is that their results comes from two factors: the exclusion of DC and the use of other crime rates to explain the murder rate. Changing these two factors causes their result to go from positive and significant to negative and significant. I also decided to run these regressions on the robbery rate and doing so produced a statistically significant negative effect whether or not DC was excluded. Using arrest rate data, not shown, also caused the results to be more significantly negative. If I had the necessary panel data handy, my strong presumption is that would also reverse with their result whether or not DC was included.

It is problematic to include the other crime rates in these regressions, particularly since they must believe that guns cause robbery as well as homicide. The results below indicate that more guns mean fewer robberies (again this is using their flawed set up, though I believe that this would continue to be observed with panel data).

The general issue when you are doing this type of empirical work is to use all the data available. When I have done my empirical work on guns I have used all the data available for all jurisdictions for all the years available. In this case, the CDC survey data is available for many years after 1995, not just 2001, and they are not using all the jurisdictions. If you selectively pick years or places one should have a good explanation for why you are doing that, and I don't see any such explanations in the paper. The regressions reported by Miller et. al. are also not the type of regression estimates that any economist would run. What I try to show below is how sensitive the results are to what I would consider to be the most obvious corrections. Including all jurisdictions and make the estimates slightly more consistent with the way an economist would look at it without even having to add new variables.

In any case, noting that this is purely cross-sectional data and not very useful, here is an attempt to redo their estimates looking at the homicide rate from 2001 to 2003 on the gun ownership rate from the CDC and the other variables that they use (I wasn't able to find their gini coefficient, but that is the only variable that they used that wasn't included). Here are some very simple linear regressions that I put together fairly quickly:

DC excluded (used all their variables in their Table 3, except for the gini coefficient)

Homcide01to03 = average homicide rate from 2001 to 2003.
I think that the other variables should be clear.

. reg Homcide01to03 gunownershiprate2001 percenturban medianfamilyincome1999 percentbelowpovertylevel percentblack percentsinglefemaleparenthouseho unemploymentrate2000census percentdivorced percentpop18342001 aggrivatedassaultrate2001 robberyrate2001 southerncensusregion alcoholconsumption2001 if notDC==0

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 13, 36) = 21.98
Model | 275.288226 13 21.1760174 Prob > F = 0.0000
Residual | 34.6827793 36 .963410535 R-squared = 0.8881
-------------+------------------------------ Adj R-squared = 0.8477
Total | 309.971006 49 6.32593889 Root MSE = .98153

------------------------------------------------------------------------------
Homcide01~03 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunowne~2001 | 6.158754 2.575103 2.39 0.022 .9362022 11.38131
percenturban | -1.20992 2.421382 -0.50 0.620 -6.12071 3.70087
medianf~1999 | .000102 .000079 1.29 0.205 -.0000581 .0002622
percentbel~l | 40.05939 19.33717 2.07 0.046 .8417922 79.27699
percentblack | .1185185 .0484017 2.45 0.019 .0203554 .2166816
percentsin~o | -3.773734 39.70597 -0.10 0.925 -84.30117 76.75371
unemployme~s | -26.08681 26.27778 -0.99 0.327 -79.38061 27.20699
percentdiv~d | 27.83938 17.55642 1.59 0.122 -7.76669 63.44544
per~18342001 | 12.88474 13.88689 0.93 0.360 -15.27917 41.04865
aggriva~2001 | .0016147 .0016653 0.97 0.339 -.0017627 .0049922
robbery~2001 | .0243026 .0056717 4.28 0.000 .0127999 .0358053
southernce~n | -1.351635 .599814 -2.25 0.030 -2.568114 -.1351559
alcohol~2001 | .0742161 .3756206 0.20 0.844 -.6875778 .83601
_cons | -14.5245 5.782964 -2.51 0.017 -26.2529 -2.796107
------------------------------------------------------------------------------

DC excluded (did not include their variables for other crimes)

. reg Homcide01to03 gunownershiprate2001 percenturban medianfamilyincome1999 percentbelowpovertylevel percentblack percentsinglefemaleparenthouseho unemploymentrate2000census percentdivorced percentpop18342001 southerncensusregion alcoholconsumption2001 if notDC==0

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 11, 38) = 14.32
Model | 249.711 11 22.701 Prob > F = 0.0000
Residual | 60.2600055 38 1.58578962 R-squared = 0.8056
-------------+------------------------------ Adj R-squared = 0.7493
Total | 309.971006 49 6.32593889 Root MSE = 1.2593

------------------------------------------------------------------------------
Homcide01~03 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunowne~2001 | 2.69241 3.090395 0.87 0.389 -3.563767 8.948587
percenturban | 5.193162 2.623195 1.98 0.055 -.1172174 10.50354
medianf~1999 | .0000198 .0000975 0.20 0.840 -.0001776 .0002172
percentbel~l | 25.22912 24.1867 1.04 0.303 -23.7343 74.19253
percentblack | .2104145 .0536538 3.92 0.000 .1017981 .3190309
percentsin~o | 10.48135 48.55617 0.22 0.830 -87.81547 108.7782
unemployme~s | 1.005869 32.85402 0.03 0.976 -65.50361 67.51534
percentdiv~d | 50.45611 21.41619 2.36 0.024 7.101307 93.81091
per~18342001 | 6.999652 17.28577 0.40 0.688 -27.99356 41.99286
southernce~n | -1.131898 .7236749 -1.56 0.126 -2.596902 .333105
alcohol~2001 | .0678944 .4816396 0.14 0.889 -.9071341 1.042923
_cons | -13.31319 7.321042 -1.82 0.077 -28.13387 1.507483
------------------------------------------------------------------------------


Same as above, but DC is included
. reg Homcide01to03 gunownershiprate2001 percenturban medianfamilyincome1999 percentbelowpovertylevel percentblack percentsinglefemaleparenthouseho unemploymentrate2000census percentdivorced percentpop18342001 southerncensusregion alcoholconsumption2001

Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 11, 39) = 31.88
Model | 1620.08306 11 147.280278 Prob > F = 0.0000
Residual | 180.146769 39 4.61914793 R-squared = 0.8999
-------------+------------------------------ Adj R-squared = 0.8717
Total | 1800.22983 50 36.0045966 Root MSE = 2.1492

------------------------------------------------------------------------------
Homcide01~03 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunowne~2001 | -9.199294 4.729762 -1.94 0.059 -18.76614 .3675525
percenturban | -3.598846 4.131027 -0.87 0.389 -11.95464 4.756945
medianf~1999 | .0000194 .0001664 0.12 0.908 -.0003172 .000356
percentbel~l | 39.06187 41.19014 0.95 0.349 -44.25305 122.3768
percentblack | .4766173 .0751993 6.34 0.000 .3245123 .6287222
percentsin~o | -201.1131 71.71166 -2.80 0.008 -346.1636 -56.06257
unemployme~s | 98.52408 52.70362 1.87 0.069 -8.079052 205.1272
percentdiv~d | 94.91258 35.49413 2.67 0.011 23.11892 166.7062
per~18342001 | 95.1942 23.88845 3.98 0.000 46.87524 143.5132
southernce~n | -3.159236 1.169235 -2.70 0.010 -5.524236 -.7942356
alcohol~2001 | 1.496186 .7727291 1.94 0.060 -.0668065 3.059178
_cons | -25.89853 12.24821 -2.11 0.041 -50.67287 -1.124194
------------------------------------------------------------------------------


DC excluded, not using their selective set of control variables
. reg Homcide01to03 gunownershiprate2001 if notDC==0

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 1, 48) = 0.00
Model | .00402852 1 .00402852 Prob > F = 0.9802
Residual | 309.966977 48 6.45764536 R-squared = 0.0000
-------------+------------------------------ Adj R-squared = -0.0208
Total | 309.971006 49 6.32593889 Root MSE = 2.5412

------------------------------------------------------------------------------
Homcide01to03 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunownershiprate2001 | -.0743955 2.978593 -0.02 0.980 -6.063259 5.914468
_cons . . . . . . . . . | 4.707644 1.0878 4.33 0.000 2.520475 6.894813
-

Same with DC included
. reg Homcide01to03 gunownershiprate2001

Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 1, 49) = 5.18
Model | 172.063659 1 172.063659 Prob > F = 0.0273
Residual | 1628.16617 49 33.227881 R-squared = 0.0956
-------------+------------------------------ Adj R-squared = 0.0771
Total | 1800.22983 50 36.0045966 Root MSE = 5.7644

------------------------------------------------------------------------------
Homcide01to03 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunownershiprate2001 | -14.46889 6.358312 -2.28 0.027 -27.24639 -1.69138
_cons . . . . . . . . . | 10.34603 2.299427 4.50 0.000 5.725162 14.9669
------------------------------------------------------------------------------




What it means. Again, this uses purely cross-sectional data, but accepting that: their result depends on excluding DC and including other crime rates to explain the murder rate. This would mean that more guns, less homicide. Even when DC is excluded, the simple correlation using cross-sectional data is negative, though not at all statistically significant.

Just for the sake of argument, I did the same regressions for robbery (though I only took the time to put together the robbery rates for 2001).


DC Excluded
. reg robberyrate2001 gunownershiprate2001 percenturban percentdivorced medianfamilyincome199
> 9 percentbelowpovertylevel percentsinglefemaleparenthouseho percentblack southerncensusregion
> percentpop18342001 unemploymentrate2000census alcoholconsumption2001 if notDC==0

Source | SS df MS Number of obs = 50
-------------+------------------------------ F( 11, 38) = 14.80
Model | 151143.145 11 13740.2859 Prob > F = 0.0000
Residual | 35287.596 38 928.620948 R-squared = 0.8107
-------------+------------------------------ Adj R-squared = 0.7559
Total | 186430.741 49 3804.709 Root MSE = 30.473

------------------------------------------------------------------------------
robbery~2001 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunowne~2001 | -148.547 74.7843 -1.99 0.054 -299.9399 2.845877
percenturban | 220.1914 63.47854 3.47 0.001 91.68583 348.697
percentdiv~d | 940.7374 518.2491 1.82 0.077 -108.4031 1989.878
medianf~1999 | -.0024856 .0023595 -1.05 0.299 -.0072621 .0022909
percentbel~l | -425.7565 585.2927 -0.73 0.471 -1610.62 759.1066
percentsin~o | 99.18109 1175.008 0.08 0.933 -2279.498 2477.861
percentblack | 3.950401 1.298365 3.04 0.004 1.321999 6.578804
southernce~n | .8315924 17.51217 0.05 0.962 -34.61994 36.28313
per~18342001 | -100.722 418.2974 -0.24 0.811 -947.5208 746.0768
unemployme~s | 892.2601 795.0325 1.12 0.269 -717.1991 2501.719
alcohol~2001 | -.6820588 11.65517 -0.06 0.954 -24.27672 22.9126
_cons | 11.46862 177.1615 0.06 0.949 -347.1761 370.1133
------------------------------------------------------------------------------


DC included
. reg robberyrate2001 gunownershiprate2001 percenturban percentdivorced medianfamilyincome199
> 9 percentbelowpovertylevel percentsinglefemaleparenthouseho percentblack southerncensusregion
> percentpop18342001 unemploymentrate2000census alcoholconsumption2001

Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 11, 39) = 34.80
Model | 468437.017 11 42585.1833 Prob > F = 0.0000
Residual | 47727.1118 39 1223.7721 R-squared = 0.9075
-------------+------------------------------ Adj R-squared = 0.8815
Total | 516164.128 50 10323.2826 Root MSE = 34.982

------------------------------------------------------------------------------
robbery~2001 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gunowne~2001 | -269.6794 76.98545 -3.50 0.001 -425.3971 -113.9616
percenturban | 130.6335 67.23995 1.94 0.059 -5.372167 266.6391
percentdiv~d | 1393.584 577.7313 2.41 0.021 225.0122 2562.156
medianf~1999 | -.0024894 .0027086 -0.92 0.364 -.007968 .0029893
percentbel~l | -284.852 670.4441 -0.42 0.673 -1640.953 1071.249
percentsin~o | -2056.182 1167.237 -1.76 0.086 -4417.142 304.7783
percentblack | 6.662021 1.224005 5.44 0.000 4.186237 9.137804
southernce~n | -19.81946 19.03141 -1.04 0.304 -58.31413 18.6752
per~18342001 | 797.6534 388.8279 2.05 0.047 11.17482 1584.132
unemployme~s | 1885.609 857.8469 2.20 0.034 150.45 3620.768
alcohol~2001 | 13.86693 12.57757 1.10 0.277 -11.5736 39.30746
_cons | -116.7293 199.3618 -0.59 0.562 -519.9766 286.5179
------------------------------------------------------------------------------

For Robbery whether you included DC or not there is a statistically significant negative relationship between the CDC's measure of gun ownership in 2001 and robbery rates in that year.

Sorry about the typos. I was working on this pretty late.

Lindgren's post on David Gross and the Survey data

Jim Lindgren has a new post that indicates that a concern Lindgren raised earlier that David Gross might have taken Hemenway's 1996 survey and not my 1997 survey. In bold type near the end of his post he notes:

"Since neither the demographics nor the descriptive accounts matched Gross to any Minnesota respondent, it seems clear that Gross was not surveyed by Hemenway in 1996."

Lindgren notes that it is possible that Gross may have taken another suurvey in 1997, but no such remotely similar survey has ever surfaced. Opponents, even those immersed in these issues, have not pointed to any remotely similar survey during anywhere near that time period. If Lindgren does not believe that Gross is making up this memory out of whole cloth and Lindgren claims that he believes that Gross did take a survey when Gross says that he did, either point to some other possible survey that was given around that time or concede that Gross took my 1997 survey. Of course, I raised this several years ago and at that time Lindgren pointed to the 1996 Hemenway survey. Now that Hemenway has finally released the data from that survey, it is clear to everyone that Gross could not have taken that survey. Rather than just conceding the point, Lindgren points to claimed inconsistencies in Gross's statements, but Gross strongly disagrees with those claims (see point 4a). David Mustard also states that Lindgren inaccurately reported what Mustard told Lindgren.

It is also very disappointing that Lindgren has put no pressure what so ever on getting Hemenway to release his 1999 data. However, Lindgren in a email to me this week indicates that: "By the way, when I talked with either Hemenway or the survey organization they used for the 1996 study, they suggested that for most of their studies after 1996, they had lists at least of the telephone numbers used (they didn't for the 1996 study except on paper in boxes someplace)." However, Hemenway told Jeff Parker earlier this year something quite different:

From: David Hemenway [mailto:hemenway@hsph.harvard.edu]
Sent: Friday, February 10, 2006 10:56 AM
To: Jeffrey Parker
Subject: Re: John Lott survey

Dear Jeffrey:
Unfortunately, it does not appear to be possible. I emailed the
survey firm which conducted the surveys, and they no longer have the data
on the phone numbers. Here is the email response I received.

Best Regards,
David

Just as he did for years with the 1996 survey, Hemenway has consistently refused to release the 1999 data.

Hemenway and Co-authors Refuse to Provided Data Set From 1999

Previously, I complained that David Hemenway (Harvard) and co-authors would not give out the data to a recent study that they did on road rage despite the fact that they had already published a paper in a journal and gone public talking to the media. Recently, however, I have asked for data from two other surveys in 1996 and 1999 that Hemenway also conducted. The 1996 data is available at the ICPSR, but the data from the 1999 survey is not released and Hemenway is not responding to requests on information on even when the data will be released (I last asked on March 9th). It seems as though seven years, long after their study results have been published, is excessively long. The strategy that Hemenway seems to be following is to delay providing the data for so long that no one is able to critically comment on his research simply because the data is so old. An possible concerns that anyone might have would be easy to resolve if data were provided in a timely manner.

Research on Guns and Road Rage

Updated

There is a new paper that is getting some attention that has just come out in the public health journal "Accident Analysis & Prevention." The paper by David Hemenway, Mary Vriniotix, and Matt Miller is entitled "Is an armed society a polite society? Guns and road rage." The paper is based on a survey of 2,400 drivers that the authors did. The survey asked respondents if they had made an obscene gesture to an opposing driver or whether they had aggressively followed another car. After that a series of descriptive questions were asked: gender, age, income, political views, urban/rural, and whether they have had a gun in their car at least one time over the last year. The authors make a simple comparison between those who have had a gun at least once in their car and those who didn't and say that the respective numbers are 23% and 16%. The authors imply that having a gun makes it more likely that one will engage in road rage.

There are multiple concerns with this analyis. Their questions make no attempt to ask whether a gun was in the car at the time the road rage incident occurred. Nor did they attempt to differentiate law-abiding permit holders from those who illegally possessed guns (e.g., asking respondents if they have a permit to carry a gun). This last point seems particularly important given that they want to make policy conclusions on concealed carry laws.

The paper also has some funny results. For example, Liberals are apparently much more likely to engage in road rage than conservatives and the difference is larger than the difference between those who did and did not have a gun at least one time in their car over the last year. This variable is apparently never investigated, but presumably they are also concerned about liberals being allowed to drive cars.

Finally, surveys can be a useful first approximation, but there is in fact much more direct evidence available on the behavior of concealed handgun permit holders. Despite almost four million Americans currently having permits to carry concealed handguns and some states having these laws for as long as eighty years, there is only one case in Alabama where a permitted concealed handgun was used to commit road rage. There are also other much more direct mesaures that indicate that people who have concealed handgun permits and who thus carry guns in their cars legally. For example, the fact that permit holders tend to be extremely law-abiding and lose their permit for violating gun regulations occurs for only hundredths or thousandths of one percent of permit holders. If they used their guns in the way that the authors of this study fear, their permits would have been revoked.

I have asked the authors for their data, but we will see when and how quickly I get it.

UPDATE: Hemenway is unwilling to provide the data for me to look at. My response is that if he or his co-authors are making comments to the press as they have, he is under an obligation to give out the data used in this paper. Despite putting together the largest data sets that have been put together on crime, I give out those data sets when the papers get media coverage even when they haven't been published yet. Hemenway's paper has been published. (The accuracy of this update was confirmed with my intern who talked to Hemenway and emailed him about obtaining this data. I had previously emailed one of the authors about obtaining the data, but I didn't receive a response.)

UPDATE 2: After a second telephone call, Hemenway said that while he will not give out the data used in the paper, he may reconsider providing a portion of the data, but that he can't make a decision before talking to his co-authors. He is also very busy and would not say when he would check into even this. (The accuracy of this update was confirmed with my intern who talked to Hemenway about obtaining this data.)

UPDATE 3: Well, it is official. Hemenway is not going to make his data available. This is true even though I have only asked for the data used in the published paper, and I am also happy to promise to use the data to only evaluate the research that the authors have already published. Hemenway complains about the comments that I have made regarding his study and concludes that: "no one on our research team believes that it will advance the science to provide even portions of the dataset semi-exclusively to Dr. Lott at this time." Of course, Hemenway inaccurately implies that I ever wanted the data set "semi-exclusively." I think that they should provide the data to everyone. I am probably just the only person to ask for it. (This update is based an email that Hemenway sent to Chris DeMuth, the president of AEI.)

Frank Main, the crime reporter for the Chicago Sun-Times, is the only reporter who has written on this study who mentions criticisms of the Hemenway, Vriniotix, and Miller research.

Clayton Cramer, Say Uncle, Geek with a .45, and The Donovan also have some notes on this research.

Correction: The original note mentioned that only one regression had been run by these authors. In fact, it turns out that four regressions had been run. The points listed above are now correct.