Running Head: EMPLIRICAL PROJECT
1
Empirical Project II
Name
Class
EMPIRICAL PROJECT
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I. Introduction
Shall issue laws, also known as the right to carry concealed weapon and their probable
impact on the violent crime rate, has been a subject of academic debates which is expected to
impact the policy decision-makers as well. Shall laws are controversial because of the concerns
they raise. Several researchers, through their research studies, have presented the possibility that
laws to permit to carry concealed handguns increases the possibility of crime with an easy access
to handguns for criminals. The criminals can procure guns through threat or by stealing it from
their original owner which will lead to an increase in cases of civil arms (Hemenway, 1997;
Ludwig, 1998; Cook & Leitzel, 1996). Cook & Leitzel (1996), through their research study
presented the fact that a relatively small percentage of youths and felons utilize primary market
to purchase handguns and the majority procures handguns from their friends or grey market.
Some youths also steal the guns from their original owner.
Permitting the concealed guns and the inability of gun owners to maintain the guns with
them actually increases the availability of guns to the criminals. Kellermann et al. (1995) found
that in several cases, the guns of the citizens were used against them by the criminals because the
citizens couldn’t use them effectively. Hemenway (1997) demonstrated that the increased access
to the handgun provided the necessary impetus to criminals to use handguns while committing
the crime to reduce the level of resistance offered by victims. Moreover, the effectiveness of
shall issue laws in reducing or controlling the violent crime rate is debatable as varied studies
have found different results. Lott & Mustard (1997) found negative correlation between violent
crimes and shall issue laws and a positive correlation between property crimes and shall issue
laws.
EMPIRICAL PROJECT
Zimring & Hawkins (1997) however rejected the results from the Lott & Mustard study
as they demonstrated their study to have some biases attributable to omitted variables such as the
fixed-effects approach used by Lott & Mustard didn’t account for unobserved factors influencing
the crime trends such as crack and gang activity. Several researchers have supported the shall
laws using the concept of deterrence effect which states that criminals will refrain from attacking
citizens because they would be uncertain of the armed response (Polsby, 1995; Lott, 1998). The
facilitating effect as put forth by those opposing shall laws is countered by the deterrence effect
by the supporters of shall laws.
Kovandzic (2005) through research study using the panel data concluded that shall laws
has minimal or no impact on the rates of violent crime. The study of Kovandzic was supported
by Rosengart et al (2005) which found statistically insignificant relationship between concealed
weapon and the rate of violent crime. Despite using the same data, several research studies
reports contained varied result because of their different treatment of the data. Donohue (2003)
rejected the findings of Lott & Mustard study as the results could be obtained by manipulating
the data or by using a different technique. The ambiguity of the data analysis techniques
deployed by the researchers has fueled this debate. In the absence of unanimous result on the
impact of concealed weapon laws on violent crime rate, shall laws are adopted by some states
whereas some states still don’t allow the citizens to carry concealed weapons.
3
EMPIRICAL PROJECT
4
II. Empirical Analysis
1. Summary Statistics
The dataset used for the project is a balanced panel dataset for 50 states of United States
and for the District of Columbia for 1977-1999. The summary statistics for the dataset are;
Variable
Obs
Mean
Std. Dev.
Min
Max
year
vio
mur
rob
incarc_rate
1173
1173
1173
1173
1173
88
503.0747
7.665132
161.8202
226.5797
6.636079
334.2772
7.52271
170.51
178.8881
77
47
.2
6.4
19
99
2921.8
80.6
1635.1
1913
pb1064
pw1064
pm1029
pop
avginc
1173
1173
1173
1173
1173
5.336217
62.94543
16.08113
4.816341
13.7248
4.885688
9.761527
1.732143
5.252115
2.554543
.2482066
21.78043
12.21368
.402753
8.554884
26.97957
76.52575
22.35269
33.14512
23.64671
density
stateid
shall
1173
1173
1173
.3520382
28.96078
.2429668
1.355472
15.68352
.4290581
.0007071
1
0
11.10212
56
1
As the summary statistics demonstrates, the dataset has 13 variables that are used to
analyze the impact of shall laws on violent crime rate. The dataset has 1173 observations and the
violent crime rate has the mean of 503 and the standard deviation of 334. This implies that there
are 503 incidents of violent crime per 100,000 members of population. The murder rate is 7.6
and robbery rate 161 indicating a far lower rate of 7 murders and 161 robberies per 100,000
members of the population. The incarceration rate is 226 per 100,000 residents.
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5
2. Time Trends
6
5
4
lnvio
7
8
Violent Crime Rate
75
80
85
90
95
100
year
stateid = 1/stateid = 19/stateid
stateid
= 34/stateid
= 2/stateid
= 50
= 20/stateid = 35/stateid = 51
stateid = 4/stateid = 21/stateid
stateid
= 36/stateid
= 5/stateid
= 53
= 22/stateid = 37/stateid = 54
stateid = 6/stateid = 23/stateid
stateid
= 38/stateid
= 8/stateid
= 55
= 24/stateid = 39/stateid = 56
stateid = 9/stateid = 25/stateid
stateid
= 40= 10/stateid = 26/stateid = 41
stateid = 11/stateid = 27/stateid
stateid
= 42
= 12/stateid = 28/stateid = 44
stateid = 13/stateid = 29/stateid
stateid
= 45
= 15/stateid = 30/stateid = 46
stateid = 16/stateid = 31/stateid
stateid
= 47
= 17/stateid = 32/stateid = 48
stateid = 18/stateid = 33/stateid = 49
Shall
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6
2
4
5
6
8
9
10
11
12
13
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
44
45
46
47
48
49
50
51
53
54
55
56
.5
0
1
.5
0
1
.5
0
80
90
100
70
.5
1
70
0
shall
1
0
.5
1
0
.5
1
0
.5
1
1
70
80
90
100
70
80
90
100
70
80
90
100
year
Graphs by stateid
80
90
100
70
80
90
100
70
80
90
100
70
80
90
100
EMPIRICAL PROJECT
7
0
500
100015002000
Incarceration Rate
75
80
85
90
95
100
year
stateid = 1/stateid = 19/stateid = 34/stateid
stateid
= 50= 2/stateid = 20/stateid = 35/stateid = 51
stateid = 4/stateid = 21/stateid = 36/stateid
stateid
= 53= 5/stateid = 22/stateid = 37/stateid = 54
stateid = 6/stateid = 23/stateid = 38/stateid
stateid
= 55= 8/stateid = 24/stateid = 39/stateid = 56
stateid = 9/stateid = 25/stateid = 40
stateid = 10/stateid = 26/stateid = 41
stateid = 11/stateid = 27/stateid = 42 stateid = 12/stateid = 28/stateid = 44
stateid = 13/stateid = 29/stateid = 45 stateid = 15/stateid = 30/stateid = 46
stateid = 16/stateid = 31/stateid = 47 stateid = 17/stateid = 32/stateid = 48
stateid = 18/stateid = 33/stateid = 49
EMPIRICAL PROJECT
8
0
10
pop
20 30
40
State Population
75
80
85
90
95
100
year
stateid = 1/stateid = 19/stateid = 34/stateid
stateid
= 50= 2/stateid = 20/stateid = 35/stateid = 51
stateid = 4/stateid = 21/stateid = 36/stateid
stateid
= 53= 5/stateid = 22/stateid = 37/stateid = 54
stateid = 6/stateid = 23/stateid = 38/stateid
stateid
= 55= 8/stateid = 24/stateid = 39/stateid = 56
stateid = 9/stateid = 25/stateid = 40
stateid = 10/stateid = 26/stateid = 41
stateid = 11/stateid = 27/stateid = 42 stateid = 12/stateid = 28/stateid = 44
stateid = 13/stateid = 29/stateid = 45 stateid = 15/stateid = 30/stateid = 46
stateid = 16/stateid = 31/stateid = 47 stateid = 17/stateid = 32/stateid = 48
stateid = 18/stateid = 33/stateid = 49
3. Regression of ln(vio)
Estimating regression of ln(vio) against shall using formula
generate lnvio = log(vio)
regress lnvio shall
EMPIRICAL PROJECT
Source
SS
9
df
MS
Model
Residual
42.3348289
446.29673
1
1171
42.3348289
.381124449
Total
488.631558
1172
.416921125
lnvio
Coef.
shall
_cons
-.4429646
6.134919
Std. Err.
.0420294
.020717
t
-10.54
296.13
Number of obs
F( 1, 1171)
Prob > F
R-squared
Adj R-squared
Root MSE
=
=
=
=
=
=
1173
111.08
0.0000
0.0866
0.0859
.61735
P>|t|
[95% Conf. Interval]
0.000
0.000
-.525426
6.094272
-.3605032
6.175566
Estimating regression of ln(vio) against shall, incarc rate, density, avginc, pop, pb1064, pw1064,
and pm1029
Formula
regress lnvio shall incarc_rate density avginc pop pb1064 pw1064 pm1029
EMPIRICAL PROJECT
Source
10
SS
df
MS
Model
Residual
275.712977
212.918581
8
1164
34.4641221
.182919743
Total
488.631558
1172
.416921125
lnvio
Coef.
shall
incarc_rate
density
avginc
pop
pb1064
pw1064
pm1029
_cons
-.3683869
.0016126
.0266885
.0012051
.0427098
.0808526
.0312005
.0088709
2.981738
Std. Err.
.0325674
.0001072
.013168
.0077802
.0025588
.0166514
.0083776
.0107737
.5433938
t
-11.31
15.05
2.03
0.15
16.69
4.86
3.72
0.82
5.49
Number of obs
F( 8, 1164)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.000
0.043
0.877
0.000
0.000
0.000
0.410
0.000
=
=
=
=
=
=
1173
188.41
0.0000
0.5643
0.5613
.42769
[95% Conf. Interval]
-.4322844
.0014024
.0008527
-.0140597
.0376894
.0481825
.0147636
-.0122671
1.915598
-.3044895
.0018229
.0525242
.01647
.0477303
.1135227
.0476374
.0300089
4.047879
1. Enactment of shall laws leads to a decrease in violent crime as represented by the coefficient
of -0.3683. Shall laws have caused a decrease in the violent crime by 36% in the states that have
enacted this law. This estimate is large in the real-world sense because a reduction of 35% in
violent crime is considered a significant reduction.
2. As measured by statistical significance, the p value for shall is 0 for both regression 1 and 2.
Therefore, adding the control variables has not changed the estimated effect of a shall-carry law
in regression. Using the real-world significance of the estimated coefficient, regression 1 has
shall coefficient of 44% which has reduced to 36% in regression 2 after addition of control
variables. Thus, adding the control variables reduced the estimated impact of shall-carry laws in
reducing violent crime by 8%.
EMPIRICAL PROJECT
3. A variable that is omitted from the study however could impact the results is the characteristic
specific to state which influence violent crime rate and shall law adoption. One such variable is
the governing party of the state. For example, if conservative party governs the state, then the
state would implement stringent measures to ensure citizen safety. This will enhance the
probability of shall law adoption and strict action to prevent violent crime. This variable omitted
from the study would lead to result that violent crime was reduced due to shall law adoption
whereas another variable strict action to prevent violent crime contribution is not considered.
4. Addition of Fixed State Effect
The regression is performed with n-1 dummy variable and n is the number of state. This
is done in order to remove the absolute state characteristics.
Formula:
xtset stateid
xtreg lnvio shall incarc_rate density avginc pop pb1064 pw1064 pm1029, fe
11
EMPIRICAL PROJECT
12
Fixed-effects (within) regression
Group variable: stateid
Number of obs
Number of groups
=
=
1173
51
R-sq:
Obs per group: min =
avg =
max =
23
23.0
23
within = 0.2178
between = 0.0033
overall = 0.0001
corr(u_i, Xb)
F(8,50)
Prob > F
= -0.3687
=
=
34.10
0.0000
(Std. Err. adjusted for 51 clusters in stateid)
Robust
Std. Err.
lnvio
Coef.
t
shall
incarc_rate
density
avginc
pop
pb1064
pw1064
pm1029
_cons
-.0461415
-.000071
-.1722901
-.0092037
.0115247
.1042804
.0408611
-.0502725
3.866017
.0417616
.0002504
.1376129
.0129649
.014224
.0326849
.0134585
.0206949
.7701057
sigma_u
sigma_e
rho
.68024951
.16072287
.94712779
(fraction of variance due to u_i)
-1.10
-0.28
-1.25
-0.71
0.81
3.19
3.04
-2.43
5.02
P>|t|
0.275
0.778
0.216
0.481
0.422
0.002
0.004
0.019
0.000
[95% Conf. Interval]
-.1300223
-.0005739
-.4486936
-.0352445
-.0170452
.0386308
.0138289
-.0918394
2.319214
.0377392
.0004318
.1041135
.016837
.0400945
.1699301
.0678932
-.0087057
5.412819
With the addition of the state effect, the coefficient of shall reduce drastically to 4%. The
coefficient of the shall can now be interpreted as that states that have enacted shall laws have
lowered the violent crime rate by 4% in comparison to the states that have not enacted this law.
Another significant change in the statistic is that shall coefficient is no longer statistically
significant at 1% level. This set of regression is most credible because it partially accounts for
the bias of omitted variable.
EMPIRICAL PROJECT
5. Addition of Fixed Time Effects
Fixed time effect of year is added as another omitted variable because time period also
play a significant role. In a particular year, crime rate could have reduced because of any specific
reason which coincides with the enactment of shall law in the state.
Formula:
xtset stateid year
xtregar lnvio shall incarc_rate density avginc pop pb1064 pw1064 pm1029, fe
13
EMPIRICAL PROJECT
14
FE (within) regression with AR(1) disturbances
Group variable: stateid
Number of obs
Number of groups
=
=
1122
51
R-sq:
Obs per group: min =
avg =
max =
22
22.0
22
within = 0.0709
between = 0.4506
overall = 0.4070
corr(u_i, Xb)
F(8,1063)
Prob > F
= -0.8849
lnvio
Coef.
shall
incarc_rate
density
avginc
pop
pb1064
pw1064
pm1029
_cons
-.0122179
-.0004579
-.1946727
-.0140397
-.0612249
.0692321
.0438136
-.0407262
4.283124
.0177087
.0001146
.1332465
.0073061
.0317717
.031784
.0057985
.0149677
.0746431
rho_ar
sigma_u
sigma_e
rho_fov
.85565084
1.0404321
.08392514
.99353542
(fraction of variance because of u_i)
F test that all u_i=0:
Std. Err.
F(50,1063) =
t
-0.69
-4.00
-1.46
-1.92
-1.93
2.18
7.56
-2.72
57.38
16.09
P>|t|
=
=
0.490
0.000
0.144
0.055
0.054
0.030
0.000
0.007
0.000
10.14
0.0000
[95% Conf. Interval]
-.0469658
-.0006828
-.4561288
-.0283758
-.1235673
.0068656
.0324358
-.0700958
4.136659
.02253
-.000233
.0667834
.0002963
.0011175
.1315986
.0551914
-.0113567
4.429588
Prob > F = 0.0000
The coefficient of shall is not statistically significant at 5% with the p value of more than 0.05.
Moreover, the estimated coefficient is negligibly small which makes illustrates that there is no
EMPIRICAL PROJECT
association between shall law enactment and the violent crime rate. The crime rate was higher
because of omitted variables of time and state and once they are considered in the regression, the
impact of shall law on violent crime rate is minimized.
6. Using ln(rob) and ln(mur)
For ln(rob) using formula
generate lnrob = log(rob)
xtreg lnrob shall incarc_rate density avginc pop pb1064 pw1064 pm1029, fe
15
EMPIRICAL PROJECT
16
FE (within) regression with AR(1) disturbances
Group variable: stateid
Number of obs
Number of groups
=
=
1122
51
R-sq:
Obs per group: min =
avg =
max =
22
22.0
22
within = 0.0705
between = 0.4296
overall = 0.3877
corr(u_i, Xb)
F(8,1063)
Prob > F
= -0.8514
lnrob
Coef.
shall
incarc_rate
density
avginc
pop
pb1064
pw1064
pm1029
_cons
.0382567
-.0005283
-.3430603
-.052511
-.0531516
.0668636
.0281037
-.014714
4.049985
.0256349
.0001636
.1873946
.010498
.0396391
.0438854
.0083221
.0199084
.1247769
rho_ar
sigma_u
sigma_e
rho_fov
.82863582
1.4305909
.12172683
.99281199
(fraction of variance because of u_i)
F test that all u_i=0:
Std. Err.
F(50,1063) =
t
1.49
-3.23
-1.83
-5.00
-1.34
1.52
3.38
-0.74
32.46
20.68
P>|t|
=
=
0.136
0.001
0.067
0.000
0.180
0.128
0.001
0.460
0.000
10.07
0.0000
[95% Conf. Interval]
-.0120441
-.0008494
-.7107656
-.0731102
-.1309313
-.0192483
.0117742
-.0537783
3.805148
.0885575
-.0002073
.024645
-.0319119
.0246281
.1529754
.0444333
.0243503
4.294822
Prob > F = 0.0000
For ln(mur) using formula
generate lnmur = log(mur)
xtreg lnmur shall incarc_rate density avginc pop pb1064 pw1064 pm1029, fe
EMPIRICAL PROJECT
17
FE (within) regression with AR(1) disturbances
Group variable: stateid
Number of obs
Number of groups
=
=
1122
51
R-sq:
Obs per group: min =
avg =
max =
22
22.0
22
within = 0.0777
between = 0.3202
overall = 0.2686
corr(u_i, Xb)
F(8,1063)
Prob > F
= -0.9145
lnmur
Coef.
shall
incarc_rate
density
avginc
pop
pb1064
pw1064
pm1029
_cons
-.055214
-.0003973
-.5912934
.0170542
-.0344083
.0019111
.0137417
.0280983
.697153
.0339762
.0001847
.1849516
.0118027
.0197586
.0370901
.0095658
.013725
.4532594
rho_ar
sigma_u
sigma_e
rho_fov
.38553684
1.4078927
.20682138
.97887584
(fraction of variance because of u_i)
F test that all u_i=0:
Std. Err.
F(50,1063) =
t
-1.63
-2.15
-3.20
1.44
-1.74
0.05
1.44
2.05
1.54
30.44
P>|t|
=
=
0.104
0.032
0.001
0.149
0.082
0.959
0.151
0.041
0.124
11.20
0.0000
[95% Conf. Interval]
-.121882
-.0007597
-.9542052
-.0061051
-.0731786
-.070867
-.0050284
.0011671
-.1922316
.011454
-.0000349
-.2283816
.0402134
.0043619
.0746891
.0325117
.0550294
1.586538
Prob > F = 0.0000
The result for robbery and murder are comparable to violent crime rates. Both the murder rates
and robbery rates are statistically insignificant therefore, there is no association between robbery
and murder rates and enactment of shall law.
7. Impact of Concealed-Weapon Laws on Crime Rates
Based on the result of the data analysis presented above, the concealed-weapon laws have
no impact on the rates of violent crime, murder and robbery. The impact which was initially
apparent was primarily because of the omitted time and state variables. Once the variables are
considered in the analysis, there was no statistically significant relationship between concealedweapon laws and crime rates.
EMPIRICAL PROJECT
18
III. Conclusion
This project analyzes the effects of concealed weapon laws on violent crimes using the
balanced panel data from 50 U.S. states plus the District of Columbia for year 1977-1999.
STATA has been used to perform statistical analysis of the data which contains variables; violent
crime rate, murder rates, robbery rates, incarceration rates for the previous years, population,
shall laws and other relevant variables. The data analysis reveals that considering the effect of
time and state omitted variables, shall laws has statistically insignificant relationship with crime
rates. Therefore, the enactment of shall laws doesn’t impact the crime rate in the state.
EMPIRICAL PROJECT
References
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attack on gun control. Law and Contemporary Problems, 59(1), 1-28.
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Berkeley: Center for the Study of Law and Society Jurisprudence and Social Policy .
Hemenway, D. (1997). Survey research and self-defense gun use: An explanation of extreme
overestimates. Journal of Criminal Law and Criminology, 87, 1430-1445.
Kellermann, A. L., Westohal, L., Fischer, L., & Harvard, B. (1995). Weapon involvement in
home invasion crime. The Journal of the American Medical Association, 273(22), 17591762.
Kovandzic, T., Marvell, T., & Vieraitis, L. (2005). The Impact of "Shall-Issue" Concealed
Handgun Laws on Violent Crime Rates: Evidence From Panel Data for Large Urban
Cities. Homicide Studies, 9(4), 292-323.
Lott, J. (1998). More guns, less crime: Understanding crime and gun-control laws. Chicago:
University of Chicago Press.
Lott, J. R., & Mustard, D. B. (1997). Crime, deterrence and right-to-carry concealed handguns.
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Ludwig, J. (1998). Concealed-gun-carrying laws and violent crime: Evidence from state panel
data. International Review of Law and Economics, 18(3), 239-254.
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EMPIRICAL PROJECT
Polsby, D. (1995). Firearm costs, firearm benefits and the limits of knowledge. Journal of
Criminal Law and Criminology, 86(1), 207-220.
Rosengart, M., Cummings, P., Nathens, A., Heagerty, P., Maier, R., and Rivara, F. (2005). An
evaluation of state firearm regulations and homicide and suicide death rates. Injury
Prevention, 11, 77-83.
Zimring, F. E. & Hawkins, G. . (1997). Concealed handgun permits: The case of the counterfeit
deterrent. Responsive Community, 46-60.
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