Violent Crime in America

Running head: VIOLENT CRIME IN AMERICA 1

Violent Crime in America: An Empirical Analysis of Predictors and Policy Areas

Michael Montanye

University of Wisconsin-Whitewater

VIOLENT CRIME IN AMERICA 2

Abstract

The reduction of violent crime rates has been, and continues to be a significant source of debate

in both the academic and political realms. The theories, related to the factors affecting violent

crime rates, can impact legislation in various policy areas such as: education, economics, and law

enforcement. To study these relationships, a cross-sectional analysis of the violent crime rates in

the United States was conducted. It included each of the 50 states as well as the District of

Columbia. The regression analysis showed links between the gun control variable and violent

crime rate when the District of Columbia was included, and education and violent crime rate

when the District of Columbia was omitted. Race and gender were both significant factors in

determining the violent crime rates, but neither was related to the policy areas that were being

studied. The results prompt potential attention to the gun control and education policy areas.

However, overall there was a lack of conclusive evidence, which promotes the need for further

research on the subject. It also shows the need for improvement of the predictive model used in

the analysis.


Violent Crime in America: An Empirical Analysis of Predictors and Policy Areas

According to the FBI (2012), there was a violent crime committed every 26 seconds in

the United States last year. It was also reported that 1,214,462 violent crimes were committed

across the United States in 2012 alone; up from the 2011 total of 1,206,005 (FBI, 2012). This

was the first increase in total violent crimes since 2006 when overall violent crime rose by

44,378 from the 2005 numbers (FBI, 2012). With statistics like that, it is not hard to understand

why the reduction of violent crime is on the public policy agenda.

In addition, specific policy areas related to reduction of violent crime have recently

reached the national spotlight. The always-contentious gun control argument has once again

been renewed by a string of high-profile shootings. Most notable among the shootings were the

Aurora theater shooting, and the Sandy Hook Elementary School shooting. Closer to home,

there was also a shooting at a Sikh temple in Oak Creek, Wisconsin (Follman, Aronsen, Pan, and

Caldwell, 2012). These events lead to a public display of action by President Barack Obama

who signed executive orders regarding the strengthening of gun control. While mass shootings

are just a small proportion of the total violent crimes committed in America, the media attention

intensifies the concern over violent crimes and highlights the importance of reducing them.

While the increase in total violent crimes in 2012 is concerning, it is also important to

note that the crime rate per 100,000 inhabitants stayed relatively the same from 2011 to 2012

(FBI, 2012). This suggests that an increase in population could explain the jump in volume of

violent crimes. Further data shows that 2012 could just be a small irregularity in an overall

positive trend for violent crime reduction. Since 1993, the overall number of violent crimes fell

from 1,926,017 to the current 1,214,462 and the rate per 100,000 inhabitants fell from 747.1 to


the current 386.9 (FBI, 2012). This trend shows improvement in violent crime reduction, but the

overall number of violent crimes still being committed is what keeps it a relevant policy issue.

In response to this, I will be looking at violent crime rates at the state level and their

relationship with a number of different variables. Each variable, or combination of variables,

will represent a potential policy area to be addressed in an effort to decrease violent crime rates.

The variables are split into four groups, three of which correspond with a policy area. The policy

areas include law enforcement, economics, and education. In addition, there are several

variables that are included as control variables and as such do not correlate with any specific

policy area.

This idea springs from two separate sources. The first source inspired my predictive

model. It was an unpublished econometrics paper, written by a peer, which looked at economic

factors in relation to the volume of murders at the state level (Mjelde, 2013). The inspiration for

adding the other policy areas came from Neumayer’s article in the Journal of Peace Research

(2003). The article found that good policies regarding governance and the economy were able to

lower homicide rates. While neither of these dealt directly with violent crime, they both dealt

with the adjacent topic of murders. I decided instead to work with violent crime as a whole.

I hypothesize that there are certain variables, corresponding with policy areas that will

have a more significant impact on violent crime rates than other variables. By creating a model,

where the violent crime rate is the dependent variable, I will attempt to figure out which

variables affect the violent crime rates the most. Based on the results of the model, I will explain

which variables and their corresponding policy areas should take precedence in the effort to

decrease violent crimes. Previous policy initiatives from those areas will be discussed as

possible solutions.


Literature Review

Violent crime, the factors that affect it, and solutions to reduce it have been widely

studied. There is a general agreement in the literature that violent crime is a problem, and that

certain factors affect violent crime rates. Where the disagreement originates from are the

theories on how the factors affect violent crime rates, and which factors are the most significant.

The theories point to everything from economic factors, to education levels, to demographics of

the population.

One particular article in the Journal of Peace Research by Eric Neumayer (2003) played

a big role in the development of my model. The study looked at 117 countries over the period of

1980-97 in regards to homicide rates (Neumayer, 2003). An important finding from the study

was that, “good policies in terms of political governance and the economy can lower homicide

rates” (Neumayer, 2003, pg. 620). In a separate article by Haynie and Armstrong (2006), they

describe how things like poverty and unemployment, which are factors of socioeconomic

disadvantage, have been studied in relationship to violent crime. They state, “The results of this

research suggest that measures of concentrated disadvantage are relevant predictors of both

Black and White homicide rates” (pg. 6). Kubrin, Messner, Deane, McGeever, and Stucky

(2010) also discussed the relationship of socioeconomic status with crime rates (pg. 58). Both

articles support what Neumayer found in his study, and made it clear that economic policies

should be one of my policy areas of study.

Haynie and Armstrong (2006), also discussed the role that race and gender play in

homicides. They noted that, “compared to men, women commit a very small number of

homicides” (pg. 3) and, “mounting evidence indicates wide racial disparities in homicide rates,

with homicides committed disproportionately by African Americans” (pg. 4). Kubrin et al


(2010) did not mention gender, but included “the proportion of the population that is non-

Hispanic Black” (pg. 69) as a variable in their model. Lance Lochner (2007) also used race in

his model where one of the variables was, “the percent of males that are black” (pg. 8). The use

of race as a variable in these sources convinced me to use it as a control variable in my model.

While he mentions race, Lochner (2007) focuses more on education in his article, which

elaborates on his theory that by increasing education you can decrease violent crime with and

economic bonus. Lochner, “estimate[s] that a one percent increase in high school graduation

rates would save the U.S. economy nearly $2 billion from reduced costs associated with criminal

activity” (2007, pg. 12). This combined economic benefit and violent crime reduction that he

proposes is caused by education convinced me to add an education variable to my model.

Of the variables discussed in the literature regarding the reduction of violent crime, the

most contentious is gun control laws. Lott and Mustard (1997) found in their study on concealed

handguns that giving people the right to carry concealed handguns, assuming they do not have

criminal records or mental illness, actually helps to deter violent crime (pg. 64). Black and

Nagin (1998) directly respond to Lott and Mustard (1997) and conclude that there is no evidence

to show a significant impact of right to carry laws deterring violent crime (pg. 218). They go on

to state that Lott and Mustard’s study is, “highly sensitive to small changes in the sample and the

model” (pg. 218-19). This debate shows that there is more research to be done in that area, and

provides a good reason to include some sort of gun control variable in my model.

Methods

I did a cross-sectional analysis of the violent crime rates in the United States. In order to

do this, I accumulated data on all 50 states, plus the District of Columbia. I used states as my

unit of analysis due to the fact that many of the policy areas, including education, law


enforcement, and economic factors, are implemented at the state level. The data for states are

also readily available, and include the particular statistics that I am looking for. The data for

other variables, which I will discuss later in the paper, would have been more difficult to track

down and were therefore omitted from the model due to time constraints.

I collected data from several sources including the Federal Bureau of Investigation (FBI)

Uniform Crime Reports, the Rhode Island Department of Labor and Training, and the United

States Census Bureau. I chose these sources because they are reliable and have consistent data

reporting. This is important because I, or another person, could easily repeat my model with data

from other years. The data that I collected from those sources includes: violent crime rates,

background check numbers, population and income statistics, unemployment numbers, education

levels, and demographic information. In addition, I added a gun control variable based on

individual state laws. These variables were aggregated into a dataset that I used for the analysis

of my model. I plan to estimate the following equation:

CrimeRate = β + β1BackChecks + β2Strictness + β3Income + β4Pop + β5LawEnforce +

β6HisPop + β7BlackPop + β8UnempRate + β9Education + β10Gender + £

Where CrimeRate is the rate per 100,000 population in a state in a given year, BackChecks is

how many National Instant Criminal Background Check System (NICS) firearm background

checks are conducted in a state that year, Strictness is the relative strictness of the gun laws in a

state compared to the rest of the states, Income is median household income for each state that

year, Pop is the total estimated population for each state that year, LawEnforce is the number of

full-time law enforcement officers that are employed in a state that year, HisPop is the

percentage of Hispanic people living in a state that year, BlackPop is the percentage of African

Americans living in a state that year, UnempRate is the unemployment rate in a state that year,


Education is the percentage of people (who are older than 25) in a state that have a high school

diploma or higher education, and Gender is the percentage of males in a state that year. β₀ is the

constant; β1, β2, β3, β4, β5, β6, β7, β8, β9, and β10 are the estimable coefficients; and £ is the

stochastic error term.

Using Ordinary Least Squares Regression, I tested this model in order to discover what

effects the variables have on the violent crime rates. I actually ran the model six separate times

using different combinations of the data. The first time I ran the model I used both the data from

2010 and 2011. However, I felt compelled to run the model on each year separately as well. I

was concerned that using two data points from each state in the first model would cause there to

be a lack of independence of the observations. The data points were independent to a point,

since they came from different years, but to be safe I ran the models separately. I also ran each

model again omitting the District of Columbia because I felt it may have been skewing the data.

The crime rate numbers were collected from Table 5 of the 2010 Uniform Crime Report

(FBI, 2010) and 2011 Uniform Crime Report (FBI, 2011). The NICS background check

numbers were collected from the NICS Firearm Background Checks by State report (FBI, 2013).

The gun control strictness variable was taken from an unpublished paper. The author

looked at state laws regarding, “conceal carry, open carry, permit for right-to-carry, permit to

purchase, assault weapons bans, requirements to register guns, license to own a gun, and a ban

on NFA [National Firearms Act] weapons” (Montanye, 2013, pg. 5). He described his

methodology of creating the strictness variable in depth. The aforementioned variables were

coded and added together for each state to create a new scale variable. The variable ranges from

0 being the least strict, to 1 being the strictest. The new strictness variable, which I used in my

model, measures the overall strictness of gun laws of that state (Montanye, 2013, pg. 6).


The median household income, gender, population, Black population, Hispanic

population, number of law enforcement officers, and education data was collected from the 2010

American Community Survey (United States Census Bureau, 2010) and the 2011 American

Community Survey (United States Census Bureau, 2011). The unemployment data came from a

report comparing the average annual unemployment rates of states since 2006 (Rhode Island

Department of Labor and Training, 2013).

I grouped variables together based on which policy area each variable correlated with. I

arranged the remaining variables into a group of control variables. The group of control

variables included population, Hispanic population, Black population, and gender. These

variables were meant to help prevent the other variables from seeming more significant than they

really were. For example, let us say that the total population strongly correlates with education

levels. If the population variable is left out, the education level variable could be shown to

correlate stronger with violent crime, when in reality the population variable could be the real

reason for the fluctuations in crime rates.

To begin, I arranged NICS background checks, and the gun strictness variable together

with the number of law enforcement officers to form the law enforcement policy group. The

median household income, and unemployment rate data were arranged together to form the

economic policy group. The study by Mjelde (2013) had two separate variables for education;

high school graduates and bachelor’s degree holders. For my model I combined the two into one

variable that I used for education policy. I created these groups so that I could add up the effects

of each individual variable to create a total effect for the policy area as a whole. This would give

me an idea of which policy areas should be given priority when trying to reduce the violent

crime rates in states across America.


Discussion

By analyzing these variables with respect to their effects on the violent crime rates across

the United States, I hope to discover which areas have the most significant impact on violent

crime rates. By doing so, I hope to be able to add to the public policy discussion and promote

the further study of particular policy areas based on my results. These aspirations are fairly lofty,

but the goal is worthy of the pursuit. Decreasing the violent crime in America, and creating a

safer environment for the next generation is important. After all, politicians love to say that the

children are our future. By conducting this study I am trying to put those words into action.

The first model that I ran was the one using just the 2010 data. The result was Table 1,

which can be seen in Appendix A. The adjusted R-square for this model was .431. That means

the variables that I chose accounted for 43.1 percent of the variation in violent crime rates across

states. When I ran the analysis, several variables stood out. Of these variables, Black population

was variable the one that I could use to predict violent crime rates with the most confidence. As

the percentage of Black population increased within a state, crime rates also went up. Table 1

shows that for every one percent increase of Blacks in the total population, ceteris paribus, we

can expect there to be an increase in the crime rate of that state by 13.863 crimes per 100,000

inhabitants. I can say this with a 99 percent confidence, which means there is less than a 0.01

probability of a type I error. The type I error in this case would be if the increase in Black

population would not actually have any effect on the crime rate in the state where the increase

occurred.

Along with the Black population, the Hispanic population was another significant

predictor of the violent crime rate in a state. As the percentage of Hispanic population increased

within a state, crime rates also went up. Table 1 shows that for every one percent increase of


Hispanics in the total population, ceteris paribus, we can expect there to be an increase in the

crime rate of that state by 5.645 crimes per 100,000 inhabitants. I can say this with 90 percent

confidence, which means there is less than a 0.10 probability of a type I error.

Gender also seemed to be a significant predictor of the violent crime rate in a state. As

the percentage of males in the population increased within a state, crime rates also went up.

Table 1 shows that for every one percent increase of males in the total population, ceteris

paribus, we can expect there to be an increase in the crime rate of that state by 72.022 crimes per

100,000 inhabitants. I can say this with 90 percent confidence.

The last variable that was significant, from the first round of analysis, was the gun control

strictness variable. Contrary to what may have been expected, the data showed that as the gun

control laws became stricter, there was a higher rate of violent crime. Table 1 shows that for

every 1 increase in the gun control strictness, remember the strictness variable is on a scale from

0 to 1, we can expect there to be an increase in the crime rate of that state by 244.440 crimes per

100,000 inhabitants, ceteris paribus. I can say this with 90 percent confidence.

The second model that I ran was the one using just the 2011 data. The result was Table

2, which can be seen in Appendix A. The adjusted R-square for this model dropped to .415. In

addition to explaining less of the variation in violent crime rates across states, this analysis

yielded very few significant results. In fact, only one of the variables met the threshold of

significance. It is, however, important to note that both the strictness and gender variables were

close to meeting the threshold. The variable that did meet the threshold was the Black

population, which once again showed that when the Black population increased so did crime

rates in that state. Table 2 shows that for every one percent increase of Blacks in the total


population, ceteris paribus, we can expect there to be an increase in the crime rate of that state by

11.758 crimes per 100,000 inhabitants. I can say this with a 99 percent confidence.

The third model that I ran was the one using both the 2010 and 2011 data. The result was

Table 3, which can be seen in Appendix A. The adjusted R-square for this model jumped up

to .482, and the results became more significant. This time both gender and Black population

had a 99 percent confidence, while Hispanic population and the gun control strictness variable

each had a 95 percent confidence.

The directionality remained the same on each of the variables, but the numbers changed.

This time for every percent increase of Blacks in the total population there is expected to be an

increase of violent crime rates of 12.729, for every percent increase of Hispanics in the total

population there is expected to be an increase of violent crime rates of 4.410, for every percent

increase of males in the total population there is expected to be an increase of violent crime rates

of 67.681, and for every 1 increase in strictness there is expected to be an increase of violent

crime rates of 207.459 crimes per 100,000 inhabitants.

Based on the three models that I ran, a trend seemed to be emerging. There were four

variables that seemed to be the most significant in predicting violent crime. These variables

were the Black population, Hispanic population, gender, and gun control strictness variables.

Unfortunately, three out of the four variables were in my control variable group. Therefore, their

significance did not help point to any policy area that I had predicted and I could not make policy

suggestions based on those results. I also noted something questionable with the fourth variable.

It seemed that, because the District of Columbia had both really strict gun laws and really high

violent crime rates, D.C. had become an extreme outlier. This can be seen in Figure 1 in

Appendix A. I had a suspicion that the District of Columbia may be skewing the data.


Therefore, I decided to omit the District of Columbia from my data, and ran all three

models again. Unfortunately the results, which can be seen in Tables 4 through 6 in Appendix

A, proved my suspicion correct. The Black population and gender variables stayed significant in

all three of the new models, but the gun control strictness variable did not stay significant in any

of them. The Hispanic variable also failed to stay significant in any of the three models. This

left me with no significant policy variable, or policy area to suggest addressing, from the first

three models.

However, in the combined 2010 and 2011 model without the District of Columbia, a new

variable became significant. This new variable was education. Table 6 shows that for every one

percent increase of people with a high school diploma or higher in the total population, ceteris

paribus, we can expect there to be a decrease in the crime rate of that state by 12.748 crimes per

100,000 inhabitants. I can say this with a 90 percent confidence.

Conclusion

While most of the information that I gathered from the models that I ran was unhelpful

towards pointing to a specific policy area to address for reducing violent crime, I was able to get

some interesting results. To begin with, demographic variables seemed to play a large role in

predicting violent crime. The Black population, Hispanic population, and gender variables

specifically were the most significant. Research expanding upon the Haynie & Armstrong

(2006) article could potentially lead to explanations and solutions to this.

The second thing that I found was that, when the District of Columbia was included in

the models, stricter gun laws actually had the opposite effect of what they are intended for.

However, seeing as the significance disappeared with the omission of just one location, this is far


from conclusive evidence. It would be helpful to revisit the strictness variable and make it more

comprehensive before reintroducing it to my model for predicting violent crime.

The third thing I found was that a higher educated population could lead to lower crime

rates. Based on this information, I would suggest trying to address education reform as part of

an initiative to lower violent crime rates in states across America. Research similar to Mehta’s

(2013) article into policies like No Child Left Behind and common core standards should be

done to assess those policies’ strengths and weaknesses. This research should then be used to

create effective education reform, which could reduce violent crime rates.

The last thing that I found was actually what I did not find. I did not find a lot of

significance from my policy variables. There was also a surprising lack of explanation of the

variation of crime rates between states as shown by the adjusted R-square values. This leads me

to believe that my model might need to be changed. Upon review, there are several variables

that I would like to change or add in a future research project. There is the possibility of a

lagged effect from the NICS background checks, and therefore I would try to take that into

account if I did further research. I also mentioned that I would return to the strictness variable

and make it more comprehensive before using it in a future model.

In addition, I would also want to add a cost of living variable to complement the current

income variable that I have. The reason for that is because just having a higher median

household income does not mean anything if the cost of living where you reside is higher as

well. This could explain why the income variable did not show up as significant. I would also

like to add a Gini coefficient variable to account for income inequality. I think that is an

important economic issue that my model failed to address.


Another thing that I would do with a future model would be to substitute a population

density variable for the total population variable. That might be a better predictor of violent

crime rates. Also, instead of having Black population and Hispanic population variables, I think

one diversity variable could strengthen the statistical relationship that was already shown

between those populations and the violent crime rate.

I acknowledge that, due to time and resource constraints, I was not able to aggregate all

the data that is available on the subject. I looked at a small period of time, only two years, when

there is actually data that goes back several decades. While my sample size will be smaller

because of this, I used all 50 states plus the District of Columbia to try to make up for that. It is

also worth noting that the variables, which I have chosen, are not all inclusive. There are

enumerable theories as to what effects violent crime, and due to the same time and resource

constraints mentioned earlier, I could not include everything in my model for this research

project. There are many other variables, which I mentioned earlier, that I would like to add or

substitute into the model if I had more time or if I decided to revise the model for a future

project.


References

Black, D. A., & Nagin, D. S. (1998). Do right-to-carry laws deter violent crime. Journal Of

Legal Studies, 27(1), 209.

Follman, M., Aronsen, G., Pan, D., & Caldwell, M (2012, December 28). US mass shootings,

1982-2012: data from Mother Jones’ investigation. Mother Jones. Retrieved from

http://www.motherjones.com/politics/2012/12/mass-shootings-mother-jones-full-data

Haynie, D. L., & Armstrong, D. P. (2006). Race- and gender-disaggregated homicide offending

rates: differences and similarities by victim-offender relations across cities. Homicide

Studies, 10(1), 3-32.

Kubrin, C. E., Messner, S. F., Deane, G., McGeever, K., & Stucky, T. D. (2010). Proactive

policing and robbery rates across U.S. cities. Criminology, 48(1), 57-98.

Lochner, L. (2007). Education and crime. University of Western Ontario, 5(8), 1-14.

Lott, J. R., Mustard, D. B. (1997). Crime, deterrence, and right-to-carry concealed handguns.

Journal of Legal Studies, 40(1), 1-68.

Mehta, J. (2013). Why American education fails. Foreign Affairs, 92(3), 105-116.

Mjelde, K. (2013). The effect of different economic factors on the number of murders in a state

for a given year. Unpublished manuscript, University of Wisconsin-Whitewater,

Whitewater, United States.

Montanye, M. (2013). Gun control in America: a study of the effects on violent crime.

Unpublished manuscript, University of Wisconsin-Whitewater, Whitewater, United

States.

Neumayer, E. (2003). Good policy can lower violent crime: evidence from a cross-national panel

of homicide rates, 1980-97. Journal Of Peace Research, 40(6), 619-640.


U.S. Department of Commerce, United States Census Bureau. (2010). 2010 American

community survey 1-year estimates. Retrieved from http://factfinder2.census.gov/faces

/help/jsf/pages/metadata.xhtml?lang=en&type=dataset&id=dataset.en.ACS_10_1YR

U.S. Department of Commerce, United States Census Bureau. (2011). 2011 American

community survey 1-year estimates. Retrieved from http://factfinder2.census.gov/faces/

help/jsf/pages/metadata.xhtml?lang=en&type=dataset&id=dataset.en.ACS_11_1YR

U.S. Department of Justice, Federal Bureau of Investigation. (2010). Crime in the United States.

Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-

u.s.-2010/tables/10tbl05.xls


Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-

u.s.-2011/tables/table-5


Retrieved from http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012

U.S. Department of Justice, Federal Bureau of Investigation. (2013). NICS firearm background

checks. Retrieved from http://www.fbi.gov/about-us/cjis/nics/reports/nics-firearm-

background-checks-1998_2013_state_monthly_totals-100313.pdf

Rhode Island Department of Labor and Training. (2013). Unemployment rates for states annual

average rankings. Retrieved from http://www.dlt.ri.gov/lmi/laus/us/annavg.htm


Appendix A

Table 1: 2010 with DC


Table 2: 2011 with DC


Table 3: Combined with DC


Table 4: 2010 without DC


Table 5: 2011 without DC


Table 6: Combined without DC


Figure 1: DC outlier

Violent Crime in America

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