Running head: Poverty Rate & Violent Crime
ROUGH DRAFT
Poverty Rate and the Increase in Violent Crime
Nathan Monroe
Lucie Martin
Ashley Anderson
POLS – 626
Abstract
Violent crime exists to varying degrees throughout the United States. It is important to examine
what the root correlations that mitigate the occurrence of violent crime. The primary explanatory
variable in this research is poverty rate by state. The other control variables that were utilized
were population density, average family income, percent white non-Hispanic, median age of
population, and high school graduation rate. The strongest independent variable was found to be
median age of the population with a β coefficient of -13.612; however the t-test showed the
variable to be not significant. The research did indicate that poverty rate is correlated with
violent crime rate with a R2=.312.
Poverty Rate and the Increase in Violent Crime
The link between poverty and crime has been well established in research over the past
40 years. The criminological theory is that as the stress of an impoverished lifestyle begins to
wear down the societal folkways and mores this breakdown leads to more at risk behavior up to
and including committing a violent crime. The crime is at once an outlet for the stress, as well as
a means to an end to the poverty through crimes of acquisition. The time for a model that
identifies explanatory variables that correlate with violent crime has never been more needed.
The primary independent variable in this research is poverty rate; this was chosen
because prior research has shown a strong correlation between variables of violent crime rate and
poverty (Karmen, 2010). The control variables in this model were selected because the selected
variables also correlated well with violent crime rate and a better understanding of the control
variables’ influence on the dependent variable will give a greater insight in to the strength and
significance of the poverty rate as an explanatory variable.
This type of model is necessary for sociologists and criminologists to review and make
recommendations on proactive steps that can be taken in order to reduce the violent crime rate
within a community. It is unlikely that a solution to how a community overcomes something as
complex and deeply rooted as poverty will emerge without a great deal more research as well as
several test plans being put in to place. The answer is out there for how to combat a problem
such as violent crime, and the first step is a basic understanding of the root causes that correlate
to a significant degree with violent crime. The model developed here takes a look at the state
level and breaks the data down accordingly, 50 states and the District of Columbia so 51
instances for each variable. This model does not distinguish between the three types of poverty
identified Stretesky, Schuck, & Hogan, (2004) “(1) persistent poverty, where individuals react to
conditions of absolute or relative deprivation; (2) underclass poverty, where nonconventional
attitudes and behaviors produce a subculture of violence; and, (3) ‘ghetto poverty1’, where the
spatial concentration of extreme poverty produces social isolation from mainstream society.”
The reason for disregarding the categories and relying on an average individual level of poverty
rate is to provide a model that accounts for poverty as a homogenous category equally providing
the same influence on violent crime rate.
Literature Review
Spatial Poverty Clustering
According to the research by Stretesky et al (2004) the restructuring of the job market
which led to a reduction in manufacturing jobs and a relocation of managerial jobs to the suburbs
created a form of economic segregation. This segregation in part has created a stratification of
cultures. The poorest segment of the population unable to uproot and move remains in older
parts of the larger cities, and without the economic resources that larger property and sales taxes
bring to a community the urban areas begin to decay.
The research from Stretesky et al (2004) suggests that the high concentration of
impoverished residents creates an isolation effect from mainstream society. The downfall in to a
higher crime rate begins with the fall from mainstream society. The isolated segments of poverty
stricken areas are unconsciously overlooked by the body politic. This leads to indifferent
legislation being passed in regards to regulating and diversion resources to the area. The overall
lack of resources: policing, infrastructure assistance, etc. leads to the creating of an alternative
set of folkways and mores as the research refers to as “Code of the Street.”
1
The term ‘ghetto poverty’ is quoted from another piece of research cited by Stretesky, Schuck, & Hogan, (2004);
however the research is not cited in this in the paper.
This “alternative set of norms” (Stretesky et al 2004) is an argument for the rationale that
crime is merely a reactionary measure to an environment with fewer opportunities for legitimate
gain. The data in this study did show areas of high poverty clustered in metropolitan areas. The
resolution seems to be a finding a way to reintegrate these segments of the population and see to
the equitable distribution of resources as to avert poverty clustering and correlating violent
crime.
Urban Renewal
In the research performed by Lehrer (2000), the crime statistics showed an overall
decrease from 1990 to 1999. This reduction in crime rate was due primarily to impovements in
larger cities of that decade. The study estimates that “more than 3 million additional crimes
during 1999 alone” would have been committed had it not been for the dramatic decrease in
crime. The study observed nine different neighborhoods in Los Angeles, CA, Boston, MA,
Garden Grove, CA, and Providence, RI.
The neighborhoods selected for this study consisted “of people with low to moderate
incomes: Poverty rates range between 20 and 55 percent, and a majority of students at local
schools qualify for free or reduced-price lunches” (Lehrer, 2000). The neighborhoods were in
easily accessible locations to larger commercial areas. The resources were diverted to develop
infrastructure and city parks. The policing was increased, and as the crime rate started to fall the
influx of young professionals and families began which stimulated the consumer economy in the
area.
Urban Economic Change
The development of a model that not only measures poverty, but takes employment in to
the model as well was developed by Hwan Oh, 2005. The model shows employment rate as well
as whether that employment is based in the service sector or the higher paying manufacturing
sector2. The units of measure were 153 central cities within metropolitan statistical areas in the
United States. The city that had the highest population over 100,000 was selected as the central
city in this model and utilized for the study. The data was collected from the United States
Bureau of the Census.
The shift within the central-city from an employment ratio dominated by the service
sector to one with more manufacturing jobs has the significant impact of violent crime rates
being reduced. The converse was shown to be significant as well that when central-city
employment restructures to a more service based sector. The violent crime rate increases.
Labor Market Conditions
Lee & Slack (2008) developed a research model to measure the association between
secondary sector work3, low hour work, low pay work, and violent crime. This study moves
“beyond the question of the availability or absence of employment (in terms of employment
versus unemployment) by shifting the focus to the quality of employment available in local labor
markets” (Lee & Slack, 2008). The quality of the employment that an individual has access to
can be just as much of a disadvantage as whether or not the individual is employed.
2
Hwan Oh (2005) makes the assertion throughout his research that manufacturing sector jobs are higher paying;
however it should be clarified that this is true only as an average when compared to the service sector, and may
differ on a case by case basis.
3
Secondary Sector Work: is identified in this case as a distinct labor segment emphasizing low-skilled work with
low worker retention and high turn-over.
The study did show a correlation between the “working poor” and higher levels of crime;
however the issue of collinearity produced a stumbling block for the research. The quality of
employment an individual has are correlated with other types of resource advantage or
disadvantage, therefore controlling for quality of employment becomes increasingly difficult.
The research is relevant to the violent crime discussion; however the quality of work the
individual has attained could be placed in a broader category of simple poverty.
Age and Violent Crime
The elderly are considered targets of opportunity because of limited mobility and
instances of disability. The research by Clark, Kawachi, Ryan, Ertel, Fay, & Berkman, 2009 was
an eight year longitudinal cohort study over the percieved neighborhood safety of elders age 6574 in New Haven, Connecticut. The research was conducted over an 8 year period and the
participants were assessed on their mobility as well as lifestyle characteristics including
smoking, alcohol use, and physical activity.
The data showed no significant impact on the elderly population was made by the
percieved threat of living in a neighborhood with a high crime rate. The income of the elderly
person had no determining effect on the significance.
Suburbanization of Crime
The research from Jargowsky & Park, (2009) attempts to determine if criminal behavior
is mobile to the suburbs from the central city within a metropolitan area. The argument for
criminal relocation can only be made by reviewing the population and crime rate within the
central city and the the wider metropolitan area over a period of time. The study examines four
possibilities: (1) no suburbanization where all residents remain within the central-city, (2)
neutral suburbanization where an even number of law-abiding citizens and criminals relocate to
the suburbs and remain in the central-city, (3) Selective Suburbanization where only law-abiding
citizens relocate and all criminals and some law-abiding citizens remain in the central-city, and
(4) suburbanization with problematic central-city where only law-abiding citizens relocate and
the number of criminals increases within the central-city.
The data showed a significant impact of higher population density in the central-city was
correlated with a lower crime rate, indicating that lower suburbanization would lead to a lower
central-city crime rate as illustrated in possibility number 1, however the remaining possibilities
could not be significantly explained due to a lack of reliable explanatory data.
Poverty the Root Cause
A current article by Dr. Andrew Karmen, (2010) identifies poverty as the root cause of
crime in the United States. The evidence from the economic downturn of 2008 shows that crime
actually went down by a small percentage according the the Uniform Crime Report from the
Federal Bureau of Investigation. The argument made by the author; however is that the
“overwhelming majority of slayings could be characterized as poor young men snuffing out the
lives of other poor young men” (Karmen, 2010). The subjective commentary that is offered is
that “to prevent criminal activity, it is in our enlightened self-interest to improve failing schools,
provide job training for ex-convicts, and stimulate job growth in both the public and private
sectors.”4
4
The educated opinion of a PhD in the field of sociology, not an empirically proven solution to the overarching
problem.
Methods
Data Collection and Estimation
The data was collected primarily from the United States Bureau of the Census (USBC).
The USBC has several data sets broken down by state; each data set we collected was vetted
under the terms of whether or not it was collected or estimated within the year 2007 and the units
of measure. The USBC is a valid and empirical source for datasets and therefore it was
determined to a valid place to acquire the base data for this model.
Data and Theory
The data for poverty rate was acquired using a prepared dataset from the United States
Bureau of the Census. The data was computed based on the estimated percentage of United
States citizens that currently live in a household earning less than the poverty rate for the number
of people that reside in the household. The data is broken down by state and entered in to the
model. The selection of poverty rate as the primary explanatory variable was based on the
general nature of poverty. The research has shown that elements of poverty have increasing or
decreasing effects on crime; however the consistency with each data set is that all forms of
poverty have an effect.
Percent white was acquired from a similar dataset as poverty rate. The selection of
percent white was made because of the stratification of earnings between the non-Hispanic white
population and all other minority ethnicities. This stratification between earnings links, ethnicity
to income.
The median age of the population was selected as a control variable and the data was
acquired through the United States Bureau of the Census. The median age is important because
the research indicates that age is a mitigating factor for crime and therefore relevant to the model
as a control variable.
The population density came up in the research as a relevant control. The study done by
Jargowsky & Park (2009) indicated that population density has a mitigating effect on crime rate.
The units of measure were number of people within a sqaure mile. The dataset was acquired
from the United States Bureau of the Census and placed in the model as a control variable.
`The education of a population has been shown in research to have a mitigating effect on
crime rate. The units of measure were estimated percentage of the population over the age of 25
who graduated from high school. The dataset was acquired from the United States Bureau of the
Census and placed in the model as a control variable.
The average family income by state was considered to be relevant because it indicated
not only the level of poverty, but also the level of wealth in a state. The dataset was acquired
from the United States Bureau of the Census and placed in the model as a control variable.
The regression model used was a basic non-logrythmic model:
Model 1
ycrime rate = 2098.717 + 8.458xpovertyrate – 4.006xpercentwhite – 13.612xmedage + .088xpopden –
13.533xhighschgrad + .003xavgincome + 158.918
The final model used after the F-test revealed the non-significance of average income was:
Model 2
ycrime rate = 2435.567 + 1.998xpovertyrate – 4.443xpercentwhite – 14.339xmedage + .089xpopden –
13.798xhighschgrad + 157.985
Results
The according to the model the constant rate of crime with all other variables equal to
zero would be 2435.567 instances of crime per 100,000 people. The model does require some
data be entered in order to have an inferential value. The control variables that were chosen in
the final model as illustrated in Table 1 have negative β coefficients. This negative coefficient
indicates that three of our control variables (percent white, median age, and high school
graduation rate) are mitigating factors when correlated with crime.
The first model was discredited because the average income variable was believed to be
too collinear with the variable of poverty rate. The F-test showed the variable of average income
to be not significant as illustrated in Table 1.
Discussion
This study seeks to find supporting data for the hypothesis that there is a strong link
between poverty rate and crime rate. The areas of a community that superficially tend to have
the highest instances of crime are the most impoverished areas. The prior research in the field of
criminology indicates a strong correlation between poverty and criminal activity. The
justification for the research is the argument that if an individual is impoverished the
opportunities available to that person are limited. The strains of other viable options to leave a
lifestyle of poverty are theoretically what drive the criminal to act.
The testing of our hypothesis of a direct link between poverty rate and crime rate required
we include a number of control variables. The rationale for the inclusion of the percentage of
white people per variable instance (state) was that, even in a twenty-first century post civil rights
society, there is still a stratification between non-Hispanic white individuals and minority
ethnicities. This stratification was thought to correlate well with income and geographic
location. The data shows that in the final model the percent white was a significant variable
(p < .05). The β coefficient was negatively correlated with crime rate indicating that ethnicity is
a factor in crime rate; however the prior mentioned stratification of ethnicity could be
misleading.
Age correlates with many factors that make crime more or less likely. The hypothesis the
control variable was designed to indicate is that the higher the median age of the variable
instance (state) the lower the crime rate. This control variable was not significant (p >.10). The
strength of the β coefficient is high and negatively correlated with crime rate; however the
significance is in question.
The research discussed earlier indicated that population density is a mitigating factor for
crime rate (Jargowsky & Park, 2009). This was the hypothetical concept that influenced the
inclusion of population density as a control. The variable was very significant (p < .01);
however the strength of the β coefficient is below expectation and in the opposing direction of
the research the data indicates for each additional individual within a square mile the crime rate
should increase .088.
Dr. Andrew Karmen (2010) advocates that better funded schools and a refocus on
education would be a mitigating factor on the crime rate. The inclusion of high school
graduation rates for individuals 25 and over as a control variable was based on this assertion.
This variable was found to not have significance (p > .10). The β coefficient is negative and has
high strength; however it is not a viable indicator.
The average family income was not utilized as a control variable in the final model. It was
not significant (p >.10), and the β coefficient was very weak. The F-test showed it was not a
significant variable in the model so it was discarded for the final model without its inclusion.
Poverty rate was the primary explanatory variable. The hypothesis and model were set up
to show a correlation relationship with crime rate. The model indicated that the variable of
poverty rate when tested is not significant (p > .10). The β coefficient was weak for the variable
as well.
Conclusion
The final model created indicates a significant correlation between population density and
violent crime rate and an individual being a non-Hispanic white person and violent crime rate.
Future Research
In future research it would be important to log variable for median age as well as
population density per square mile. The selection of control variables would have to wider
including more indicators for criminal behavior in addition to mitigating factors of violent crime
rate. The research could also be restructured to include more data, but more even regional
spacing. The utilization of state level data was convenient; however state level data in this
instance has too much variance in population, population densities, and socio-geographic
structure. This study may have been negatively affected by that variance. An effective model
needs to be developed to identify empirical indicators of violent crime so that systematically
cities and states can devote resources to the resolution of those indicators.
Works Cited
Clark, C. R., Kawachi, I., Ryan, L., Ertel, K., Fay, M. E., & Berkman, L. F. (2009). Perceived
neighborhood safety and incident mobility disability among elders: the hazards of
poverty. BMC Public Health , 1-15.
Hwan Oh, J. (2005). Social disorganizations and crime rates in U.S. central cities: Toward an
explanation of urban economic change. The Social Science Journal , 42 (4), 569-582.
Jargowsky, P. A., & Park, Y. (2009). Cause or Consequence?: Suburbanization and Crime in
U.S. Metropolitan Areas. Crime & Delinquency , 55 (1), 28-50.
Karmen, A. (2010, January 7). Poverty Remains Root Cause. USA Today , p. 1.
Lee, M. R., & Slack, T. (2008). Labor market conditions and violent crime across the metrononmetro divide. Social Science Research , 37 (3), 753-768.
Lehrer, E. (2000). Crime-fighting and urban renewal. The Public Interest , 91-103.
StateMaster.com. (2010, March). Median Age by state. Retrieved March 2010, from
http://www.statemaster.com
StateMaster.com. (2010, March). Percent of People Who are White Alone. Retrieved March
2010, from http://www.statemaster.com
Stretesky, P. B., Schuck, A. M., & Hogan, M. J. (2004). Space Matters: An analysis of poverty,
poverty clustering, and violent crime. Justice Quarterly , 21 (4), 817-841.
United States Census. (2007). Educational Attainment for the Population Aged 25 and Over by
Region, State, and Nativity Status: 2007. United States Census.
United States Census. (2007). Individuals and Families Below Poverty Level. United States
Census.
United States Census. (2007). State Median Family Income by Family Size. United States Census
Bureau.
United States Census. (2008). State Population - Rank, Percent Change, and Population per
Square Mile of Land Area. United States Census Bureau.
United States Census. (2007). State Rankings -- Statistical Abstract of the United States. United
States Census Bureau.
Table 1
Comparison of Models with Descriptive Statistics
Model 1
Model 2
Mean
Std. Dev.
8.458
1.998
Poverty Rate by
0.588
0.871
12.67
3.12
Individual
15.513
12.275
-4.006
-4.443
Percent White Non0.073
0.038
78.79
14.24
Hispanic
2.180
2.074
-13.612
-14.339
Median age of State
0.213
0.185
36.71
2.19
Pop.
10.778
10.663
0.088
0.089
Pop. Density by
0.000
0.000
376.58
1347.07
Square Mile
0.020
0.020
-13.533
-13.798
High School Grad.
0.237
0.225
86.00
3.63
Rate
11.296
11.223
0.003
Average Family
0.495
$63,211.02 $9,162.71
Income
0.004
0.609
0.605
R2
11.410
0.473
F Change
Note: The numbers within parentheses represent standard error for the coefficient
N
51
51
51
51
51
51