12/03/2005

Yet another person pretending to be me



A link to the posting can be found here.

I appreciate being emailed about this.

Latest Gallup Poll shows that 42 percent of Americans Households Own Guns

Judge Jack Weinstein lets New York City's suit against the gun industry go forward

Brooklyn Federal Judge Jack Weinstein lets New York City's suit against the gun industry go forward. That Weinstein found this is hardly surprising. There is an exception for these suits if the gun companies have violated state or federal law in selling the guns, but I can't seem to find exactly what law they are said to have violated. As far as I can tell, simply claiming that gun makers have "dumped" guns on the market so that criminals will eventually get them doesn't qualify.

12/02/2005

What is going on this week at the NY Post?

The NY Post is still one of my favortite newspapers, but I am not sure what is going on this week at the NY Post regarding guns (see here and here). The NY Post has always been the one New York City newspaper that was willing to provide a different perspective on the gun issue.

Incorrect claims about the data in More Guns, Less Crime

I have received an email comparing my work to that of Steve Levitt's regarding claimed coding errors in More Guns, Less Crime. Of course, this discussion is not very accurate.

1) There are no coding errors in the data used in “More Guns, Less Crime.” The book used crime data from 1977 to 1996, and as far as I know, no academics have claimed that there were any coding errors in that data. An interesting and useful Stanford Law Review article by Plassmann and Whitley added an additional four years to the data and the problem arose in this additional data. Overall, less than 200 cells out of 7.5 million cells were accidentally left blank. More important, the results that Plassmann and Whitley noted were the results which they thought were the correct ones were not affected at all by this minor change in the data.

2) I had put the data set on my website as a favor to Plassmann because it was very large and he did not have the ability to put it up for people to download. The data was corrected as soon as the missing cells were discovered and it was available for people to download. A note was added to the site to alert people to the correction. While I had helped them out on their paper, I let Plassmann and Whitley make their own statements about their results. My website reported the regressions in the same way that I had done them in all my previous estimates.

3) In statements here and here, I have discussed what was involved in Levitt's errors. The regressions where they left out the fixed effects were essentially the only ones that were at all useful in testing their hypothesis because they were the only ones that didn't require large degrees of aggregation to get to their "effective abortion rate" that were related to total murder rates in a state. The work that I did with Whitley directly related the age of the murder to the number of abortions when that murderer was born.

Thanks to Bob Thomas for his email.

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12/01/2005

9-year-old child uses toy gun to scare off criminals in South Africa

The bravery of a nine-year-old boy who rescued his grandmother from armed robbers and scared them off using his toy gun has been recognised by the police, who on Tuesday gave him a “hero” award.

Michael-Lee Ellington, a pupil at Arcadia Primary School, was honoured in front of his schoolmates when Inspector Owen Musiker of Sunnyside police station handed him an award in the school hall.

Looking on proudly – and gratefully – was his grandmother Marjorie, with whom he lives in a flat in Pretorius Street, Arcadia. . . .

Shortly after she was tied up for a second time, Michael-Lee knocked on the door. To his surprise two armed men opened the door and dragged him in, with one of the men placing his hand over the boy's mouth to prevent him from screaming.

Michael-Lee said: “I knew I had to do something, so I bit his hand hard and he let go of me. I quickly grabbed my toy gun and started shouting at them. They ran out of the flat, taking off with my granny's belongings, including her camera.”

Marjorie said she was surprised and relieved at her grandson's bravery. After the men fled, she removed the tape from her mouth. . . .


There are a few points to make. I am not sure that such a story could occur in the US because of all the regulations on how toy guns look here. In addition, I am not sure how much longer this could go on at in South Africa because legitimate gun ownership continues to be forced down dramatically by the government, it will be harder to convince criminals that the toy gun is real. Finally, I wonder whether a child like this would ever receive such a reward. In my book, The Bias Against Guns, I discuss multiple cases where young children used real guns to save lives and those cases got little publicity and no rewards were given out as far as I can tell.

Thanks to Walter E. Williams for sending this to me.

More on Levitt from Clayton Cramer

11/29/2005

Two talks tomorrow at Capital University in Columbus, Ohio

I have a talk on Wednesday at the Capital University Law School tomorrow at 4:30 on the judicial confirmation process.

UPDATE: I just want to say that both talks to the law school faculty and to the students that night were very enjoyable, and that it was particularly nice to spend time with Brad Smith and his family.

Donohue and Levitt on Abortion, How can they miss having used fixed effects?

"Correcting DL’s programming error is straightforward, because adding state-year controls requires only that we rewrite a line of computer code for each regression."
Quote from Foote and Goetz's paper, p. 8.


Sorry for making such a long post here, but I wanted to give non-statisticians at least a rough idea of what was required for john Donohue and Steve Levitt to miss seeing whether they had included fixed effects in their regressions on abortion. The two thoughtful authors from the Boston Fed mentioned the "programming oversight" done by Donohue and Levitt in their abortion research. I still believe that saying that is "being much too nice," but I will leave it to readers to decide what level of sloppiness is involved in this research. Here is an abortion regression with fixed effects (though I have three types of fixed effects rather than the two used by the Boston Fed people). It is hard for me to see how this could have been missed. If one looks at these regressions, it is just hard to see how someone could miss having included them. The state-year interactions discussed by Foote and Goetz would add up to ((51*number of years)-1) to the list of control variables. I only wish that Donohue and Levitt had provided me with their regressions and all their data when I first asked for this six years ago. Their refusal to provide this information is at best extremely unfortunate. The obvious thing is that they should be ashamed for not providing this information in a timely manner when it was asked for.

Here are two simple regressions. Obviously more control variables can be included, but I decided to just show the simplest case from my paper with John Whitley. Notice how in this case fixed effects change the coefficient sign on the abortionpop variable. Including an absorb statement can eliminate one set of these fixed effects from being reported and thus make this point less obvious, but the general point is still the same.

. xi:xtpois murders1 abortionpop Unktrnd- year_23, i(FIPSSTAT) irr pa robust
note: year_2 dropped due to collinearity
note: year_3 dropped due to collinearity
note: year_4 dropped due to collinearity
note: year_22 dropped due to collinearity

Iteration 1: tolerance = .09532036
Iteration 2: tolerance = .10020875
Iteration 3: tolerance = .03855165
Iteration 4: tolerance = .008876
Iteration 5: tolerance = .00160909
Iteration 6: tolerance = .00028531
Iteration 7: tolerance = .00005063
Iteration 8: tolerance = 8.966e-06
Iteration 9: tolerance = 1.588e-06
Iteration 10: tolerance = 2.813e-07

GEE population-averaged model Number of obs = 21756
Group variable: FIPSSTAT Number of groups = 51
Link: log Obs per group: min = 299
Family: Poisson avg = 426.6
Correlation: exchangeable max = 437
Wald chi2(40) = 196144.51
Scale parameter: 1 Prob > chi2 = 0.0000

(standard errors adjusted for clustering on FIPSSTAT)
------------------------------------------------------------------------------
Semi-robust
murders1 IRR Std. Err. z P>z [95% Conf. Interval]
-------------+----------------------------------------------------------------
abortionpop 1.405027 .2128516 2.24 0.025 1.044079 1.890756
Unktrnd 1.024132 .0046993 5.20 0.000 1.014963 1.033384
aFIPS_2 .0989696 .0001332 -1718.30 0.000 .0987088 .099231
aFIPS_4 .5898942 .0001919 -1622.84 0.000 .5895183 .5902703
aFIPS_5 .5880759 .0002303 -1355.74 0.000 .5876248 .5885275
aFIPS_6 5.794082 .0147365 690.75 0.000 5.765271 5.823037
aFIPS_8 .3988566 .000475 -771.84 0.000 .3979267 .3997886
aFIPS_9 .2799524 .0003301 -1079.79 0.000 .2793062 .2806001
aFIPS_10 .0661884 .0000789 -2277.49 0.000 .0660339 .0663433
aFIPS_11 .1034435 .0557807 -4.21 0.000 .0359503 .2976485
aFIPS_12 2.402414 .0112149 187.76 0.000 2.380533 2.424496
aFIPS_13 1.407365 .0012569 382.62 0.000 1.404904 1.409831
aFIPS_15 .1101832 .0002945 -825.28 0.000 .1096075 .1107619
aFIPS_16 .0940332 .0000456 -4871.68 0.000 .0939438 .0941227
aFIPS_17 1.614205 .0023658 326.72 0.000 1.609575 1.618849
aFIPS_18 .5732324 .0001949 -1636.57 0.000 .5728505 .5736146
aFIPS_19 .1128952 .0002638 -933.54 0.000 .1123794 .1134134
aFIPS_20 .2369441 .0024803 -137.56 0.000 .2321324 .2418555
aFIPS_21 .6326708 .0011611 -249.45 0.000 .6303992 .6349507
aFIPS_22 1.016173 .0002205 73.94 0.000 1.015741 1.016605
aFIPS_23 .0579125 .0002006 -822.27 0.000 .0575206 .0583071
aFIPS_24 .7755058 .0015016 -131.30 0.000 .7725683 .7784546
aFIPS_25 .3166921 .0007269 -500.97 0.000 .3152706 .3181199
aFIPS_26 1.645792 .0018611 440.58 0.000 1.642149 1.649444
aFIPS_27 .241033 .0002058 -1666.02 0.000 .2406298 .2414367
aFIPS_28 .5054685 .0002175 -1585.63 0.000 .5050424 .505895
aFIPS_29 .7520471 .000341 -628.41 0.000 .751379 .7527158
aFIPS_30 .0546092 .0002382 -666.66 0.000 .0541444 .0550781
aFIPS_31 .1011488 .0000618 -3747.60 0.000 .1010277 .1012701
aFIPS_32 .2792081 .0000737 -4831.47 0.000 .2790636 .2793526
aFIPS_33 .0504129 .0001025 -1469.58 0.000 .0502124 .0506142
aFIPS_34 .7724087 .0009178 -217.32 0.000 .7706118 .7742097
aFIPS_35 .2183319 .0004742 -700.57 0.000 .2174044 .2192634
aFIPS_36 2.17522 .0369114 45.80 0.000 2.104065 2.248782
aFIPS_37 1.589501 .0011554 637.51 0.000 1.587238 1.591767
aFIPS_38 .0182674 .0000229 -3195.96 0.000 .0182226 .0183123
aFIPS_39 1.225201 .0010425 238.71 0.000 1.223159 1.227246
aFIPS_40 .6485567 .0002798 -1003.57 0.000 .6480084 .6491054
aFIPS_41 .3239948 .0007478 -488.27 0.000 .3225324 .3254639
aFIPS_42 1.387713 .0019498 233.20 0.000 1.383897 1.39154
aFIPS_44 .0755543 .0000414 -4713.95 0.000 .0754732 .0756354
aFIPS_45 1.081743 .0003085 275.49 0.000 1.081138 1.082348
aFIPS_46 .02442 .0000246 -3684.40 0.000 .0243718 .0244683
aFIPS_47 .991302 .0002734 -31.67 0.000 .9907662 .991838
aFIPS_48 4.273158 .0012148 5108.87 0.000 4.270777 4.275539
aFIPS_49 .1169221 .0000596 -4211.61 0.000 .1168053 .1170389
aFIPS_50 .0294125 .0000315 -3295.45 0.000 .0293509 .0294743
aFIPS_51 1.147393 .0006312 249.93 0.000 1.146157 1.148631
aFIPS_53 .4719951 .0010989 -322.49 0.000 .4698463 .4741538
aFIPS_54 .3279638 .0001477 -2475.52 0.000 .3276744 .3282534
aFIPS_55 .3507432 .0007353 -499.74 0.000 .349305 .3521874
aFIPS_56 .0536214 .000039 -4025.53 0.000 .053545 .0536978
age1_11 1.966192 .3025431 4.39 0.000 1.454285 2.658291
age1_12 4.734692 .5743331 12.82 0.000 3.732831 6.005445
age1_13 13.53745 1.632393 21.61 0.000 10.68799 17.14659
age1_14 38.02512 5.282721 26.19 0.000 28.96113 49.92586
age1_15 87.06121 12.9882 29.94 0.000 64.98878 116.6302
age1_16 152.4797 23.9504 32.00 0.000 112.0757 207.4496
age1_17 220.7705 32.41546 36.76 0.000 165.5618 294.3892
age1_18 264.9041 39.6383 37.29 0.000 197.5701 355.1863
age1_19 274.0828 40.61976 37.88 0.000 204.9894 366.4645
age1_20 284.7132 47.03216 34.21 0.000 205.9664 393.5671
age1_21 234.3185 32.71881 39.08 0.000 178.2174 308.0798
age1_22 226.4202 32.14569 38.19 0.000 171.4222 299.0634
age1_23 214.5435 30.03564 38.35 0.000 163.0606 282.2811
age1_24 199.3217 28.51133 37.02 0.000 150.5903 263.8225
age1_25 212.4668 33.23983 34.25 0.000 156.3591 288.7082
age1_26 173.2493 24.38667 36.62 0.000 131.4788 228.2901
age1_27 166.417 23.04193 36.94 0.000 126.8648 218.3003
age1_28 159.6084 22.47962 36.02 0.000 121.1074 210.3493
age1_29 147.8315 19.87613 37.16 0.000 113.5853 192.4031
age1_30 161.0707 24.55669 33.33 0.000 119.4657 217.165
age1_31 1759.683 250.7702 52.44 0.000 1330.855 2326.688
age1_99 4.23e-18 3.85e-17 -4.39 0.000 7.45e-26 2.40e-10
year_5 1.532679 .118351 5.53 0.000 1.317416 1.783116
year_6 1.334756 .0968619 3.98 0.000 1.157794 1.538767
year_7 1.290539 .0928728 3.54 0.000 1.120766 1.486029
year_8 1.226638 .1079416 2.32 0.020 1.032316 1.45754
year_9 1.126 .0994771 1.34 0.179 .9469754 1.33887
year_10 1.120204 .0910245 1.40 0.162 .9552807 1.313601
year_11 1.223229 .0872979 2.82 0.005 1.063556 1.406874
year_12 1.135106 .0966264 1.49 0.137 .9606778 1.341206
year_13 1.234025 .0961375 2.70 0.007 1.05928 1.437598
year_14 1.271295 .0877407 3.48 0.000 1.11045 1.455437
year_15 1.378969 .1031067 4.30 0.000 1.190994 1.596613
year_16 1.483771 .1128602 5.19 0.000 1.278268 1.722313
year_17 1.410172 .0995151 4.87 0.000 1.228013 1.61935
year_18 1.433584 .0916863 5.63 0.000 1.264689 1.625035
year_19 1.367123 .0674563 6.34 0.000 1.241103 1.505939
year_20 1.20845 .0494248 4.63 0.000 1.11536 1.309309
year_21 1.048189 .0542946 0.91 0.364 .9469969 1.160194
year_23 .9175812 .0253189 -3.12 0.002 .869275 .9685718
------------------------------------------------------------------------------

Here is what it would look like without fixed effects:

. poisson murders1 abortionpop Unktrnd, irr robust

Iteration 0: log likelihood = -1248609.3
Iteration 1: log likelihood = -658235.69
Iteration 2: log likelihood = -544299.3
Iteration 3: log likelihood = -531248.22
Iteration 4: log likelihood = -531238.71
Iteration 5: log likelihood = -531238.71

Poisson regression Number of obs = 21756
Wald chi2(2) = 1419.07
Prob > chi2 = 0.0000
Log likelihood = -531238.71 Pseudo R2 = 0.2365

------------------------------------------------------------------------------
Robust
murders1 IRR Std. Err. z P>z [95% Conf. Interval]
-------------+----------------------------------------------------------------
abortionpop .3488533 .0530301 -6.93 0.000 .2589701 .4699332
Unktrnd 1.001123 .0000327 34.38 0.000 1.001059 1.001187
------------------------------------------------------------------------------

[Update: An email from Carl Moody indicates that Donohue is giving out a regression file that accounts for fixed effects in at least one of the sets of estimates. The way that this is done indicates that they do not surpress the print out of the estimates and thus one would see whether fixed effects had been estimated. In any case, while I am very glad to see this now being made available, it would have been useful 4, 5, or 6 years ago when I was asking for the information.]
Update 2: It should also be made clear that despite whatever sloppiness occurred Donohue and Levitt have acknowledged the coding error and agree that correcting the error and using arrest rates suggests no effect of legalizing abortion on crime.

New Op-ed on Alito's Confirmation Process

11/28/2005

Levitt's Abortion "Disappeared" When "Programming" Error Fixed

Levitt's Abortion results disappeared when programming Error Fixed

"The Boston Fed's Mr. Foote says he spotted a missing formula in the programming oversight of Mr. Levitt's original research. He argues that the programming oversight made it difficult to pick up other factors that might have influenced crime rates during the 1980s and 1990s . . . . He also argues that Levitt should have counted arrests on a per-capita basis. Instead, he coundted overall arrests. After he adjusted for both factors, Mr. Foote says, the abortion effect disappeared.


Christopher L. Foote and Christopher F. Goetz's paper can be found here. Personally, I think calling this a "programming oversight" is being much too nice (See my post here to see an example of what you would have to miss to not notice whether you have used fixed effects). More importantly everyone who works with panel data knows that you use fixed effects.

My own work concentrated on murder rates, but I also included fixed effects. Donohue and Levitt never provided us with all their data or their regressions and would never answer any questions that we had so I just assumed that they had included fixed effects from the beginning. It would have been nice if they had provided us with this same information years ago.

My paper with John Whitley is available here.
A letter that I had in the WSJ is available here.
James Q. Wilson's review of this debate is available here.

UPDATE:

See also Steve Sailer's past work on this topic.
See also Steve Sailer's compliation of what others are saying here.

UPDATE II (I have been asked about inaccurate claims regarding my own research in More Guns, Less Crime):

See this for a discussion about claimed errors in More Guns, Less Crime

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Students can apply for scholarships for graduate and undergraduate studies

The returns to exercise seem pretty small

From the Washington Post

People who engaged in moderate activity -- the equivalent of walking for 30 minutes a day for five days a week -- lived about 1.3 to 1.5 years longer than those who were less active. Those who took on more intense exercise -- the equivalent of running half an hour a day five days every week -- extended their lives by about 3.5 to 3.7 years, the researchers found.


Suppose one exercises at the moderate level:
per year that is 130 hours = 52 (weeks)*5 (days)*.5 hours
over 50 years that is: 6,500 hours

There are 5,840 waking hours in a year. 8,175 over 1.4 years. At any reasonable interest rate, in terms of purely longevity, there is a strong negative return to exercise. For this to pay off, one must really enjoy the process of exercise or that it greatly improves the quality of life.

Beyond all that is the issue of the type of people who exercise. My guess is that the people who exercise generally tend to be more educated and have other characteristics (e.g., being married because they take care of themselves) that are associated with longer life expectancies. There is no mention that the study controls for such factors as income, education, and marriage.

11/27/2005

National Crime Victimization Survey Data on Self Defense

More Crime Problems in UK: Surge in unsolved crimes

If people don't think that reporting the crime to the police is less and less likely to accomplish anything, this can result in reported crime falling farther below actual crime rates. This increase in reported crime could thus underestimate the true increase in crime. It is also a reason that crime rates will increase if criminals have less to worry about. Part of the increase in unsolved crimes is consistent with the increase in gang related crime in the UK. Overall, it raises additional questions about people in the UK being able to rely on law enforcement for protection.

MORE than 100,000 police crime reports were ditched before reaching court by Scotland's prosecutors last year, up a third on the previous 12 months and the strongest indication yet of the crisis gripping the nation's justice system.

Despite many thousands of hours of detective work, fiscals marked a total of 106,481 crime reports with "no proceedings" compared with 81,028 in 2003/2004. The abandoned cases ranged from 'petty' crimes such as vandalism to serious assault.

Prosecutors insist most cases were shelved on the grounds of insufficient evidence but critics say the massive increase proves the courts and prosecutors are under intolerable pressure. . . . .

Over the same period, the amount of crime reported to the Crown rose only marginally, from 321,000 to 323,000. This means the proportion of abandoned crime reports has increased in 12 months from a quarter to more than a third.


Thanks very much to John WIlliamson for sending me this link.

"Pew Research Center Poll Shows Liberals, Media Out of Touch"

A new poll shows a large difference between how the general population and the media view the war in Iraq.

Of interest is a recent Pew Research Center for the People & the Press poll, which ascertained crucial and marked differences between the leftist media and those in academe and the American public. This conducted poll illustrated the difference in attitudes toward the US war against terrorism in Iraq and compared the Left’s answers to the general US public.

The results belied the [Mainstream Media] MSM’s “reporting” and were startling. Startling, that is, to all except those of us who already knew that the MSM’s reportage was and is bogus. It also confirms that the MSM does not report any news correctly if it strays from their agenda. Instead, old leftist media manipulate the news to support their own desires and schemes. Although the MSM tells us constantly that the majority of Americans are against the Iraq war, the new Pew poll data contradicts that assertion. The poll revealed the following information on the asked question

“Do you think democracy will succeed in Iraq?”

• 56% of the American public polled said “Yes”
• 64% of the US military in Iraq said “Yes’
• 33% of journalists said it only ‘had a chance’
• 27% of the academic world said it might succeed

When asked if they thought the decision for military action in Iraq was the correct one, those polled responded with almost 50% of the American public saying “Yes”. However, on the liberal/leftist side of the fence only 21% of academics and 28% of the press believes it was justified. Hmmm. It strongly appears that any “factual results”, regarding the Iraq scenario, old media report are taken from their own small and ever-decreasing groups! And from what I’ve observed, most (if not all) of these polls are heavily-weighted toward the liberal side of the house. That is, more liberals’ results are included in polling data than those identifying themselves as conservatives. Again—no surprise here!