


































Economics, Law and Policy 
ISSN 2576-2060 (Print) ISSN 2576-2052 (Online) 

Vol. 6, No. 3, 2023 

www.scholink.org/ojs/index.php/elp 

13 
 

Original Paper 

Does the Federal Law Forbidding People under Domestic 

Violence Restraining Orders from Possessing Firearms Save 

Lives? 

John R. Lott, Jr.
1
 & Carlisle E. Moody

2*
 

1
 Crime Prevention Research Center, Missoula, MT, USA 

2
 Department of Economics, College of William and Mary, Williamsburg, VA, USA 

*
Carlisle E. Moody, Department of Economics, College of William and Mary, Williamsburg, VA, USA 

 

Received: September 15, 2023   Accepted: September 25, 2023  Online Published: October 18, 2023 

doi:10.22158/elp.v6n3p13                    URL: http://dx.doi.org/10.22158/elp.v6n3p13 

 

Abstract 

The Supreme Court of the United States will hear arguments concerning Federal Law 18 U.S.C. 

922(g)(8) which forbids individuals who are under a Domestic Violence Protection Order (DVPO) 

from possessing firearms. This paper analyzes the potential costs of overturning that law. We estimate 

a variety of models to determine the effect of the law on domestic murders, domestic femicides, 

domestic gun murders, and domestic gun femicides. We subject the analysis to a variety of robustness 

checks. The results are remarkably robust. We find that 18 U.S.C. § 922(g)(8) does not significantly 

reduce domestic murder, domestic femicide, domestic gun murder, or domestic gun femicide. 

Keywords 

domestic violence, firearm policy analysis, two-way fixed-effects, 18 U.S.C. 922(g)(8) 

 

 

 

 

 

 

 

 

 

 



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1. Introduction 

On February 2, 2023, the Fifth Circuit Court of Appeals vacated the conviction of Zackey Rahimi. A 

Federal court convicted Mr. Rahimi possessing firearms while under a Domestic Violence Protection 

Order (DVPO), violating section 18 U.S.C. 922(g)(8) of the US Code (hereafter “922” law), a law 

passed by Congress in 1994. The court concluded that,  

Doubtless, 18 U.S.C. § 922(g)(8) embodies salutary policy goals meant to protect vulnerable 

people in our society. Weighing those policy goals’ merits through the sort of means-end 

scrutiny our prior precedent indulged, we previously concluded that the societal benefits of § 

922(g)(8) outweighed its burden on Rahimi’s Second Amendment rights. But Bruen 

forecloses any such analysis in favor of a historical analogical inquiry into the scope of the 

allowable burden on the Second Amendment right. Through that lens, we conclude that § 

922(g)(8)’s ban on possession of firearms is an “outlier that our ancestors would never have 

accepted.” Id. Therefore, the statute is unconstitutional, and Rahimi’s conviction under that 

statute must be vacated (Note 1). 

Rahimi had a lengthy criminal history. In 2019, Rahimi fired a gun at a passerby who witnessed him 

dragging his girlfriend through a parking lot. Months later, he shot at a driver he had gotten into an 

accident with. And in 2020, he assaulted another girlfriend and threatened her with a gun. Finally, in 

2021, Rahimi shot a gun in the air when his friend’s credit card was declined (Note 2). Restraining 

orders are unlikely to work against the people who are truly dangerous. Someone who is willing to 

commit murder is already facing either a life sentence or the death penalty. The additional penalties for 

illegally obtaining a gun or violating a protective order are unlikely to provide marginal deterrence. 

Despite a protective order, Mr. Rahimi still illegally obtained and used a firearm. In any application of 

a restraining order, there are false positives and false negatives. Restraining orders mistakenly applied 

to law-abiding individuals will likely be effective in disarming them, so that they will not be able to 

defend themselves and others. As just noted, restraining orders might not impose a real marginal 

penalty on those who are truly dangerous. Thus, the law is most likely to restrain only those who are 

most law-abiding and fail to restrain the most dangerous.  

What are the merits of this law that will be lost in the protection of Second Amendment rights if the 

Supreme Court finds it unconstitutional? In this paper, we attempt to assess the benefits of banning 

individuals who are subject to domestic violence protection orders from possessing firearms. That is, 

we are assessing the costs of declaring 18 U.S.C. § 922(g)(8) unconstitutional. 

 

 

 

 

 

 



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2. Method 

2.1 Econometric Methodology 

We measure the strength of state Domestic Violence Protective Orders (DVPO) by the duration of the 

order. The 922 law enhances the state domestic violence protection order. It has no effect in the 

absence of a state protection order. The 922 law passed in 1994. We create a dummy variable to 

measure its effect, taking the value zero for years before 1994 and the unit value for the remaining 

years. We then create two test variables by multiplying the temporary order duration and the final order 

duration variables by the dummy variable for 1994 and later. We are, therefore, estimating the 

additional effect of the 922 law given an existing state protection order. This methodology allows us to 

estimate a Two-Way Fixed-Effects (TWFE) panel data model where we include the year dummies to 

control for things like recessions, the passage of other federal laws, and anything else that affects all 

states in a given year. It also makes the necessary assumption of independence across states more 

likely.  

A DVPO is intended to protect the applicant against potential violence committed by a domestic 

partner. While a person of any gender can request such an order, women are the primary users. As such, 

we investigate several different outcomes, domestic homicides, domestic homicides of women, 

domestic homicides committed by firearms, and femicides by a domestic partner using firearms. Since 

these are count variables, we use the TWFE negative binomial model (Cameron & Trivedi, 1998, pp. 

70-79). The negative binomial is a generalization of the Poisson distribution in that it does not require 

the mean and variance of the distribution of the dependent variable to be equal. The typical case is that 

the variance is greater than the mean, called overdispersion. We tested for overdispersion in each of the 

models reported below. There was significant overdispersion in every case, justifying the negative 

binomial model. The model is as follows. 

1 2 3 4
log( ) ( ) ( )

it i t it it it it it it it it
y tdvpo fdvpo D tdvpo D fdvpo X u                 (1) 

The αi are the state fixed effects, the δt are the year fixed effects, Dit is the dummy variable indicating 

that the year is 1994 or later, tdpvoit is the duration of the state’s temporary protection order in year t, 

fdvpoit is the duration of the state’s final DVPO in that year, Xit is a matrix containing observations on 

the control variables. The primary coefficients of interest are β3 and β4 indicating the enhancement 

effect of 922. However, the coefficients on the temporary and final protection order durations (β1 and 

β2) may also be of interest.  

The choice of control variables to include in the model is crucial. If we leave out a potentially relevant 

variable, it results in omitted variable bias. Including an irrelevant variable whose true coefficient is 

zero, does not bias the estimates, but the estimates are inefficient. The inefficiency could be so bad that 

a relevant variable could have a standard error so large that it inaccurately appears to be insignificant.  

 

 



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According to Rao (1971) and Wallace (1964), if the true t-ratio for a given variable is less than one in 

absolute value, dropping this potentially irrelevant variable from the original (general) model will 

reduce the mean squared error (variance plus squared bias) of all the remaining coefficients in the 

resulting (specific) model. Accordingly, we start with a general model including many potentially 

relevant variables. We then drop all control variables with absolute t-ratios less than one and test them 

as a group for significance using a standard F-test. If the F-test is not significant, we are justified in 

dropping the insignificant variables. If the F-test is significant, we go back and find the variable or 

variables making it significant and add them back into the model. We continue until the F-test is 

insignificant. If the resulting specific model has any t-statistics less than one in absolute value, we drop 

them, add them to the F-test, and test for significance. If the F-test is insignificant, we re-estimate the 

new, reduced, specific model. We repeat until the specific model has no t-ratios less than one in 

absolute value. The resulting model is the specific model and, while it may have some bias, the bias 

will be small. The specific model will be more efficient, with presumably more precise estimates, and 

have lower mean squared errors, than the general model. This general-to-specific methodology (Hendry, 

1995) has been used successfully in many applications, e.g., Owen and Weatherston (2004), Muelbauer 

and Nunziata (2004), Rao and Singh (2006), and Reade (2007). We rely on this modeling strategy to 

estimate the true data generating process as closely as possible.  

It is unlikely that the federal government passed the law banning individuals subject to a DVPO from 

possessing firearms because of a contemporaneous increase in deaths of individuals otherwise 

protected by state protection orders. We assume that the policy variables are exogenously determined. 

That means that we can avoid the use of instrumental variables. However, since some of the control 

variables could be simultaneously determined with the dependent variable, we lag the potentially 

endogenous control variables. 

2.2 Data 

Observations on the dependent variables are taken from Kaplan (2021). Jacob Kaplan has produced a 

convenient database for the FBI’s Supplementary Homicide Reports (SHR). The SHR data for 

1980-1983 are missing and most of the data for the control variables end in 2018. Consequently, our 

primary results are derived from an unbalanced panel consisting of 50 states. In the robustness section 

we restrict the dataset to 1984-2018 to form a balanced panel. 

We defined a domestic homicide as one in which the first victim was related to the first offender as a 

husband, wife, boyfriend, girlfriend, common-law husband, common-law wife, ex-husband, ex-wife, or 

in a homosexual relationship. We then determined if the victim was female and if the offender used a 

firearm.  

For the policy variables, we examined the law codes of all fifty states from 1970 to 2023. There are two 

types of protective orders. Temporary or emergency orders are usually issued without the participation 

of the respondent. These are usually short, averaging 27 days (median 14 days), but they can be as long 

as two years. The final order, which replaces any temporary order, is longer, averaging four years with 



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a median of one year. However, many states issue permanent orders, which could be modified or 

rescinded. To translate these into numerical values, we had to make several assumptions. For example, 

since we don’t know the respondent’s age or the probability of the order being appealed or otherwise 

reduced or rescinded, we assume that a permanent order lasts 25 years. The online appendix lists all the 

assumptions. Since the numerical values are necessarily arbitrary, we did robustness checks with these 

long-duration order values doubled and halved to see if the results changed. The results are not 

sensitive to these assumptions. 

The control variables are listed in Table 1 along with the means of the dependent variables and the 

policy variables of interest. 

 

Table 1. Means and Other Statistics  

Variable N Mean St. Dev Min Max 

Domestic murders 1587 33.73 43.53 0 344 

Domestic femicides 1587 23.88 30.18 0 217 

Dom gun murders 1587 19.59 27.24 0 241 

Dom gun femicides 1587 14.37 19.02 0 141 

Final order, years 1587 3.98 7.40 0.05 42.48 

Temp order, days 1587 27.13 73.38 0.28 730 

Final*922 dummy 1587 3.55 7.26 0 42.48 

Temp*922 dummy 1587 21.19 68.85 0 730 

Prison pc 1587 333.90 154.32 32.84 871.62 

Police pc 1587 277.60 50.41 161.67 522.74 

Abortion 1587 1.51 1.31 0 6.02 

Unemployment rate 1587 5.65 1.88 2.30 14.80 

Employment pc 1587 57.90 5.33 37.99 73.07 

Military pc 1587 1.01 0.90 0.27 6.65 

Construction pc 1587 3.33 0.75 1.36 6.90 

Alcohol pc 1587 1.91 0.45 0.89 4.04 

Crack 1587 1.41 1.10 -0.70 7.78 

Density 1587 4.31 13.54 0.08 111.34 

Income pc 1587 16.80 3.48 9.16 30.45 

Poverty rate 1587 12.89 3.71 2.90 27.20 

Welfare pc 1587 249.58 89.65 0.33 548.53 

Gun suicide/suicide 1587 61.10 12.73 0 100 

Executions pc 1587 0.87 3.08 0 40 

Percent population 15-34 1587 28.92 2.72 23.37 39.81 

Percent black men 15-34 1587 1.66 1.44 0.07 5.90 

 

 

 



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Police, prison, and the death penalty all have deterrent roles. Prison and executions also have 

incapacitation effects. These variables are all in per capita rates. We also lag them to avoid 

simultaneity.  

The effective abortion rate for murder from Donohue and Levitt (2020) is included because there is 

some evidence that legalized abortion could affect crime rates. Unfortunately, the Donohue and Levitt 

(2020) sample ends in 2014 and computation of the effective abortion rate is complicated. However, it 

is easy to extrapolate the series since it is a smooth trend, and we only need to extrapolate four years. 

We include the proportion of the population between 15 and 34 and the proportion of black males 

15-34 because relatively young men largely commit murder. We have three employment variables, 

total employment per capita, indicating the amount of legitimate employment available; military 

employment, since the military concentrates many young men in certain areas but also sends many 

young men out of the country; and construction employment per capita because the construction 

industry also concentrates large numbers of young men. The denser the population, the more 

interactions among the inhabitants, some of which could cause domestic murder. For this reason, we 

include population density as a control variable. We include per capita consumption of alcohol because 

alcohol can reduce inhibitions. We include the unemployment rate, real personal income per capita, the 

poverty rate, and real welfare payments per capita, any of which could affect domestic murder by 

altering the stresses associated with domestic life.  

A possibly important factor in any crime equation using historical data is the emergence of crack 

cocaine in the 1980’s. The resulting large increase in the supply of cocaine caused turf battles among 

drug gangs, increasing murder rates. We control for the crack cocaine epidemic by including the Fryer 

et al. (2013) crack index, a combination of indicators of cocaine use compiled by Fryer and his 

colleagues for the period of the crack epidemic. There are continuous values for 1981 to 2000 for each 

state. We set pre-1981 values at the 1981 levels and post-2000 values at the 2000 levels. Finally, we 

include population as an exposure variable.  

 

3. Results 

The next two tables present the primary results. Table 2 shows the effects of the federal 922 law on 

domestic murder and domestic femicide. The temporary and final domestic violence protection orders 

insignificantly change domestic murders or domestic femicides. 

Table 3 shows the law’s effects on domestic gun murder and domestic gun femicide. Here we again find 

that neither temporary nor final domestic violence protection orders significantly reduce domestic gun 

murders or domestic gun femicides. The effect of the 922 law working through temporary orders is a 

small, but significant, increase in domestic gun murders. The estimates for domestic gun femicides show 

a similarly small effect but are not consistently significant. The effect of the 922 law working through 

final orders shows a very small and insignificant decline in both domestic gun murders and domestic gun 

femicides. Overall, 18 U.S.C. § 922(g)(8) has not significantly reduced domestic murders, domestic 



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femicides, domestic gun murders, or domestic gun femicides. 

 

Table 2. Results for Domestic Murder and Domestic Femicide 

 

Domestic Murder Domestic Femicide 

Variables General Specific General Specific 

Temporary order, days 1.000 1.001 1.001 1.001 

 

(0.602) (0.862) (1.031) (1.364) 

Final order, years 0.993 0.991 0.993 0.991 

 

(-1.183) (-1.250) (-1.101) (-1.165) 

922 dummy * temp days 1.000 1.000 1.000 1.000 

 

(0.556) (1.350) (-0.432) (0.121) 

922 dummy * final years 0.997 0.994 0.998 0.994 

 

(-0.651) (-1.103) (-0.423) (-1.115) 

Prisoners per capita lagged 1.000 1.000 1.000 

 

 

(-1.356) (-1.338) (-0.653) 

 Police per capita lagged 1.000 

 

1.000 

 

 

(-0.302) 

 

(0.0198) 

 Abortion 0.984 

 

0.958 

 

 

(-0.282) 

 

(-0.788) 

 Unemployment 1.010 

 

1.014 

 

 

(0.772) 

 

(1.125) 

 Employment 0.993 

 

0.995 

 

 

(-0.460) 

 

(-0.300) 

 Military 1.007 

 

1.039 

 

 

(0.0536) 

 

(0.305) 

 Construction 1.103 1.078 1.087 1.041 

 

(1.798) (1.760) (1.506) (1.044) 

Alcohol 1.503* 1.446** 1.335 1.386* 

 

(2.172) (2.810) (1.541) (2.551) 

Crack 1.042 1.076 1.042 1.079 

 

(1.411) (1.673) (1.376) (1.666) 

Density 0.995 

 

0.985 0.982* 

 

(-0.485) 

 

(-1.540) (-2.135) 

Income 1.004 

 

1.007 

 

 

(0.192) 

 

(0.345) 

 Poverty 1.011 1.014 1.007 

 



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(1.221) (1.665) (0.779) 

 Welfare 1.000 

 

1.000 

 

 

(-0.666) 

 

(-0.0126) 

 Gun suicide/suicide lagged 0.997 0.997 0.997 0.998 

 

(-1.671) (-1.450) (-1.406) (-1.072) 

Executions pc lagged 1.000 

 

1.001 

 

 

(-0.0873) 

 

(0.414) 

 Pct pop 15-34 0.982 

 

0.976 

 

 

(-0.707) 

 

(-0.941) 

 Pct black men 15-34 1.751*** 1.613*** 1.558*** 1.471** 

 

(3.813) (3.378) (3.357) (3.014) 

Note. ***p<.001, p<**.01, p<*.05; negative binomial model; coefficients are incidence rate ratios; 

t-ratios in parentheses; standard errors clustered on states; state and year effects estimates are suppressed; 

complete results, programs and data are available at <cemood.people.wm.edu/ELP_dvpo.zip>. 

 

Table 3. Domestic Gun Murder, Domestic Gun Femicide 

 Domestic gun murder Domestic gun femicide 

Variables General Specific General Specific 

Temporary order, days 1.000 1.001 1.000 1.000 

 

(0.274) (0.703) (0.323) (0.486) 

Final order, years 0.994 0.992 0.991 0.991 

 

(-0.965) (-1.104) (-1.402) (-1.351) 

922 dummy * temp days 1.001* 1.001** 1.001* 1.001 

 

(2.083) (2.588) (2.258) (1.839) 

922 dummy * final order years 0.993 0.991 0.996 0.996 

 

(-1.329) (-1.654) (-0.786) (-0.826) 

Prisoners per capita lagged 1.000 

 

1.000 

 

 

(-0.316) 

 

(0.400) 

 Police per capita lagged 0.999 

 

0.999 

 

 

(-0.737) 

 

(-0.434) 

 Abortion 0.962 

 

0.956 

 

 

(-0.614) 

 

(-0.826) 

 Unemployment 1.013 

 

1.006 

 

 

(0.808) 

 

(0.411) 

 Employment 0.990 

 

0.997 

 

 

(-0.461) 

 

(-0.157) 

 



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Military 0.974 

 

0.978 

 

 

(-0.179) 

 

(-0.171) 

 Construction 1.106 

 

1.088 

 

 

(1.562) 

 

(1.360) 

 Alcohol 1.648* 1.664*** 1.470 1.589*** 

 

(2.317) (3.373) (1.850) (3.524) 

Crack 1.072* 1.109* 1.084* 1.091** 

 

(2.041) (2.242) (2.314) (2.768) 

Density 0.997 

 

0.991 0.984 

 

(-0.270) 

 

(-1.075) (-1.537) 

Income 0.994 

 

0.987 

 

 

(-0.294) 

 

(-0.698) 

 Poverty 1.015 1.016* 1.010 

 

 

(1.602) (1.995) (1.143) 

 Welfare 1.000 

 

1.000 

 

 

(-0.243) 

 

(0.376) 

 Gun suicide/suicide lagged 0.997 

 

0.998 

 

 

(-1.112) 

 

(-0.995) 

 Executions pc lagged 0.996 0.995 0.998 

 

 

(-1.356) (-1.889) (-0.727) 

 Pct pop 15-34 0.979 

 

0.968 0.972 

 

(-0.660) 

 

(-1.045) (-1.032) 

Pct black men 15-34 1.697** 1.525* 1.542** 1.571** 

 

(3.192) (2.458) (2.843) (2.997) 

Note. ***p<.001, p<**.01, p<*.05; negative binomial model; coefficients are incidence rate ratios; 

t-ratios in parentheses; standard errors clustered on states; state and year effects estimates are suppressed; 

complete results, programs and data are available at <cemood.people.wm.edu/ELP_dvpo.zip>. 

 

4. Robustness Checks 

An alternative method for estimating the impact of a crime policy is to investigate the policy’s effect on 

the per capita domestic murder rate. For this analysis, we apply the two-way fixed-effects model to the 

rate of domestic murders per one million population. Table 4 presents the results. The estimates are 

very similar to those shown in Tables 2 and 3. The federal 922 law does not significantly affect 

domestic murder or domestic femicide. The small positive effect for domestic gun murders which was 

significant in Table 3 is not significant in the per capita fixed-effects model. As reported in Table 3, 

there is a significant small positive effect on domestic gun femicide for temporary protective orders, 

but only in the more efficient specific model. 



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Table 4. Estimates Using Per Capita Data 

 Domestic murder Domestic femicide 

 General Specific General Specific 

Temporary order, days 0.034 0.038 0.011 0.0071 

 

(0.87) (1.03) (0.40) (0.25) 

Final order, years -0.36 -0.37 -0.18 -0.19 

 

(-1.08) (-1.19) (-0.85) (-0.82) 

922 dummy * temp days -0.015 -0.016 -0.0061 0.0046 

 

(-1.13) (-1.33) (-0.59) (0.50) 

922 dummy * final order years 0.033 0.028 0.049 -0.075 

 

(0.14) (0.12) (0.31) (-0.44) 

 Domestic gun murder Domestic gun femicide 

 General Specific General Specific 

Temporary order, days 0.026 0.032 0.0044 -0.0025 

 

(1.05) (1.31) (0.24) (-0.14) 

Final order, years -0.21 -0.27 -0.24 -0.22 

 

(-0.79) (-1.01) (-1.41) (-1.16) 

922 dummy * temp days 0.00031 -0.0012 0.011 0.027** 

 

(0.032) (-0.13) (1.39) (3.46) 

922 dummy * final order years -0.16 -0.12 0.0034 -0.070 

 

(-0.78) (-0.62) (0.027) (-0.49) 

Note. ***p<.001, p<**.01, p<*.05; two-way fixed-effects model; control variables and state and year 

effects estimates are suppressed; standard errors clustered on states; full results, programs, and data 

available at <cemood.people.wm.edu/ELP_dvpo.zip>. 

 

As noted above, there is some uncertainty as to how to translate the duration of indefinite or permanent 

final orders into numbers that can be entered into a dataset. We investigate the sensitivity of the results 

to these arbitrary values by doubling and halving the values of any orders over 10 years and re-running 

the negative binomial regressions. The results are reported in Table 5. 

 

 

 

 

 

 

 



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Table 5. Doubling and Halving Long Sentences 

 

2X Half X 

 Domestic murder Domestic murder 

 General Specific General Specific 

Temporary order, days 1.00 1.00 1.00 1.00 

 

(0.57) (0.84) (0.65) (0.89) 

Final order, years 1.00 1.00 0.98* 0.99 

 

(-0.88) (-1.07) (-2.05) (-1.70) 

922 dummy * temp days 1.00 1.00 1.00 1.00 

 

(0.61) (1.42) (0.44) (1.24) 

922 dummy * final order years 1.00 0.99 1.00 0.99 

 

(-0.87) (-1.27) (-0.40) (-0.89) 

 Domestic femicide Domestic femicide 

 General Specific General Specific 

Temporary order, days 1.00 1.00 1.00 1.00 

 

(1.00) (1.34) (1.10) (1.42) 

Final order, years 1.00 1.00 0.98 0.99 

 

(-0.82) (-1.02) (-1.86) (-1.55) 

922 dummy * temp days 1.00 1.00 1.00 1.00 

 

(-0.39) (0.19) (-0.55) (0.0044) 

922 dummy * final order years 1.00 0.99 1.00 0.99 

 

(-0.65) (-1.27) (-0.22) (-0.92) 

 Domestic gun murder Domestic gun murder 

 General Specific General Specific 

Temporary order, days 1.00 1.00 1.00 1.00 

 

(0.23) (0.67) (0.35) (0.76) 

Final order, years 1.00 1.00 0.98 0.98 

 

(-0.55) (-0.87) (-1.82) (-1.77) 

922 dummy * temp days 1.00* 1.00** 1.00 1.00* 

 

(2.14) (2.66) (1.95) (2.46) 

922 dummy*final order years 0.99 0.99 1.00 0.99 

 

(-1.52) (-1.86) (-0.72) (-1.14) 

 Domestic gun femicide Domestic gun femicide 

 General Specific General Specific 

Temporary order, days 1.00 1.00 1.00 1.00 

 

(0.28) (0.44) (0.38) (0.55) 



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Final order, years 1.00 1.00 0.98* 0.98* 

 

(-1.04) (-1.01) (-2.23) (-2.24) 

922 dummy * temp days 1.00* 1.00 1.00* 1.00 

 

(2.34) (1.91) (2.08) (1.71) 

922 dummy*final order years 0.99 0.99 1.00 1.00 

 

(-1.02) (-1.04) (-0.46) (-0.52) 

Note. ***p<.001, p<**.01, p<*.05; indefinite long sentences for final orders are doubled in the first two 

columns and halved in columns three and four; negative binomial model; coefficients are incidence rate 

ratios; t-ratios in parentheses; standard errors clustered on states; control variables, state and year effects 

estimates are suppressed; complete results, programs and data are available at 

<cemood.people.wm.edu/ELP_dvpo.zip>. 

 

The results are virtually the same as those reported in Tables 2 and 3 and they are almost identical for 

doubling and halving the order length. The results appear to be robust to assumptions concerning final 

order duration. 

The SHR data has missing values for all states from 1980-1983. The resulting panel is unbalanced. To 

get a balanced panel, we estimated the negative binomial model using data from 1984-2018. The 

results are reported in Table 6. 

 

Table 6. Using Balanced Panel, 1984-2018 

 Domestic murder Domestic femicide 

 General Specific General Specific 

Temporary order, days 1.000 1.000 1.001 1.001 

 

(0.457) (0.445) (0.875) (0.944) 

Final order, years 0.992 0.992 0.992 0.992 

 

(-1.255) (-1.192) (-1.224) (-1.094) 

922 dummy * temp days 1.000 1.000 1.000 1.000 

 

(0.530) (1.306) (-0.454) (-0.0547) 

922 dummy * final order years 0.997 0.996 0.999 0.997 

 

(-0.660) (-0.819) (-0.345) (-0.749) 

      Domestic gun murder Domestic gun femicide 

 General Specific General Specific 

Temporary order, days 1.000 1.000 1.000 1.000 

 

(0.140) (0.224) (0.222) (0.400) 

Final order, years 0.994 0.993 0.990 0.991 

 

(-1.040) (-1.016) (-1.520) (-1.447) 



www.scholink.org/ojs/index.php/elp                   Economics, Law and Policy                        Vol. 6, No. 3, 2023 

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Published by SCHOLINK INC. 

922 dummy * temp days 1.001 1.001* 1.001* 1.001 

 

(1.948) (2.453) (2.145) (1.702) 

922 dummy * final order years 0.993 0.993 0.997 0.996 

 

(-1.357) (-1.451) (-0.707) (-0.838) 

Note. ***p<.001, p<**.01, p<*.05; sample limited to 1984-2018; negative binomial model; coefficients 

are incidence rate ratios; t-ratios in parentheses; standard errors clustered on states; state and year effects 

estimates are suppressed; complete results, programs and data are available at 

<cemood.people.wm.edu/ELP_dvpo.zip>. 

 

The results are almost identical to those reported in Tables 2 and 3. 

 

5. Discussion 

We have estimated the enhancement effect of federal law 18 U.S.C. § 922(g)(8) using a panel of fifty 

states over 38 years. We have subjected the analysis to a variety of robustness checks. The results are 

remarkably robust. We find that 18 U.S.C. § 922(g)(8) has no significant effect on domestic murder or 

domestic femicide. We find some evidence that the law positively affects domestic gun murder and 

domestic gun femicide. This could be caused by a Peltzman (1975) effect, where individuals who feel 

safe engage in risky behavior. Overall, we conclude that 18 U.S.C. § 922(g)(8) does not significantly 

reduce domestic murder, domestic femicide, domestic gun murder, or domestic gun femicide.  

Complete results, programs, and data are available in the online appendix 

<cemood.people.wm.edu/ELP_dvpo.zip>. 

 

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Notes 

Note 1. UNITED STATES OF AMERICA, PETITIONER v. ZACKEY RAHIMI 

(https,//www.supremecourt.gov/DocketPDF/22/22-915/259334/20230317174308399_Rahimi%20Pet%

20-%20final.pdf, p. 66a.). 

Note 2. John Fitze, “How a Second Amendment case at the Supreme Court is putting gun rights groups in 

a jam”, USA Today, July 12, 2023 

(https,//www.usatoday.com/story/news/politics/2023/07/12/guns-supreme-court-second-amendment-ra

himi/70383454007/). 

 

 

https://doi.org/10.1177/1527002503259062
https://doi.org/10.1086/260352
https://doi.org/10.1080/13504850500425402
https://doi.org/10.1080/00031305.1971.10477302
https://doi.org/10.1111/j.1752-5209.2007.00015.x
https://doi.org/10.1080/01621459.1964.10480758

