Defensive gun use in Kansas involving concealed handgun permit holder

Phillips 66 Owner Dean Yee was not going to make it easy for the two men he says tried to rob him at gunpoint Friday. “They demanded money and to make things tougher I said I'm not gonna give you the money you can come around here and get it yourself.”

That's when a customer in the store, and valid conceal to carry permit holder in Oklahoma, took action.

“He got their attention. He looked at my friend and pointed the gun at him and then his life was in danger and he exercised his right and shot him,” said Yee.

Emergency crews took the 17 year old juvenile to a Kansas City hospital. The other suspect fled.

“I would've been shot in the chest,” said Yee.

After fourteen years of trying to pass the Conceal Carry law in Kansas, State Senator Phill Journey says Friday’s incident is a prime example of where the new law could help society. It is absolutely going to be seen as more permit holders are out there. I think its important that criminals need to be on notice that this will happen to them.”

“I think its gonna give businesses a lot of added protection police can't be everywhere at every minute,” said Journey. . . . .

Thanks to Gus Cotey for sending this to me.

A new cure to smoking addiction?

This is a little extreme, but it sure seems to work.

Many heavy smokers quit immediately and permanently when a small structure deep in the brain is damaged, a finding that provides a new lead in the search for smoking-cessation treatments, a study says.
"Nicotine addiction depends on a healthy insula" -- a region of the brain linked to emotion -- say the study's authors at the Brain and Creativity Institute at the University of Southern California and collaborators at the University of Iowa. . . . .

DC Mayor admits DC's gun control hasn't worked, though the solution is more national gun control

Here is the interesting problem. In other countries that have adopted national gun control, they blame the fact that we don't have international gun control for their high crime rates.

[Mayor Adrian M.] Fenty (D), speaking at a news conference held by the Mayors Against Illegal Guns coalition, said local laws are not sufficient to fight the use of illegal weapons.

"We have one of the highest homicide rates in the country but at the same time have the strictest [gun] law," Fenty said, joining other urban chief executives at the bipartisan summit on the issue yesterday on Capitol Hill. "Local jurisdictions just can't solve the problem. You need to have the federal government have one standard for dealing with illegal guns.". . . .

Thanks very much to Robert VerBruggen for sending this link to me.

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.

Mankiw interview

Here is another interesting post from Econtalk, where Russell Roberts conductes a very interviewing Greg Mankiw. Mankiw is a very interesting and bright guy. However, I did have one serious point to make with Russell. When Greg made the claim about the Coase theorem only applying when transaction costs are zero with the note that when transaction costs are positive it matters who we give the property rights to. That is not quite right. The Coase theorem implies that if the transaction costs are less than the gains from trade it doesn't matter who we assign property rights to. Similarly, I wish that Russell had raised the point that the market will solve externality problems, not just when transaction costs are zero, but when they are less than the gains from trade.

For the first time presidential candidates not accepting federal funds for general election

It is finally official. After a couple of close calls last time around, Hillary Clinton is the first to announce that she will not accept federal funds.

Sen. Hillary Rodham Clinton, D-N.Y., is the first top-tier candidate to tip her hand that she intends to leave the public money on the table. Senior Clinton adviser Howard Wolfson said in an e-mail Sunday that she would not take matching funds in the primary or the general election campaigns if she won the Democratic nomination. . . . .

The Media Gets it Wrong on the Rate that Women are Married

I recently posted a discussion here on the media coverage of the claim that 51 percent of "women" were not married. Well, Jeff Jacoby has a very nice discussion that makes some of the points that I raised earlier, but he also makes a new stunning one about the study counting women whose husbands are not living with them (away in Iraq for example) as being excluded from the sample of married individuals.

"Women," for example, isn't the word most of us would use to describe high school sophomores. Yet the Times includes girls as young as 15 in its analysis. Not surprisingly, girls who in many cases aren't old enough to get a drivers' license are unlikely to have husbands. According to the Census Bureau's 2005 American Community survey, 97 percent of females between 15 and 19 have never been married. Incorporating nearly 10 million teenagers in the ranks of marriage-aged American "women" may be a good way to pad the number of those without husbands, but it doesn't make that number more enlightening.

Actually, Census data show that even with the 15- to 19-year-olds, a majority of American females -- 51 percent -- are "now married." So how does the Times reach a contrary conclusion? By excluding from the category of women with husbands the "relatively small number of cases" -- in fact, it's more than 2 million -- in which "husbands are working out of town, are in the military, or are institutionalized." That startling Page 1 headline is true, in other words, only if the wives of US troops at war are deemed not to have husbands.

Marriage in America is undoubtedly less robust than it was 50 years ago. But it is not yet a candidate for the endangered-species list. The Census Bureau reports that by the time they are 30 to 34, a large majority of American men and women -- 72 percent -- have been married. Among men and women ages 65 and up, 96 percent have been married. Yes, the divorce rate is high -- 17.7 per 1,000 marriages -- and many couples cohabitate without getting married. But marriage remains a key institution in American life. . . . .

New Orleans drags its feet on returning guns to law-abiding citizens

Bellevue, Washington - Frustrated by repeated failures to meet court-appointed deadlines, and a pattern of disregard by the City of New Orleans, the Second Amendment Foundation has filed a motion to hold Mayor Ray Nagin and Police Superintendent Warren Riley in contempt of federal court.

A hearing on the motion is scheduled for Jan. 31 in federal district court in New Orleans. The Second Amendment Foundation is suing Nagin and Riley over the confiscation of firearms from law-abiding citizens in the aftermath of Hurricane Katrina in 2005. Joining SAF in the lawsuit is the National Rifle Association.

"Mayor Nagin, Chief Riley and the city's attorney have repeatedly failed to communicate with our legal counsel, even for the most trivial of matters related to this lawsuit," said SAF founder Alan Gottlieb. "We were prepared to file this motion months ago, but gave the city several opportunities to comply with the court, and to begin returning seized firearms to their rightful owners. They haven't done it. . . . .