Introduction rate. Because of the loss of disposal income,

Introduction

            Unemployment
is among the world’s biggest problem along with a decrease in jobs.  The U. S. Bureau of Labor Statistics in
January 2016, estimated that approximately 7.9 million people, which is 5
percent of the working population were unemployed (Gupta, 2016).  To date, the biggest indicators of the health
of the economy is the national unemployment rate.  Because of the loss of disposal income, and
with the continued high unemployment rate, consumers become highly frustrated
because they can’t find work.  Because of
unemployment, they can’t find work, and their standard of living falls
substantially and that puts pressure of them to maintain the lifestyle that
they have become accustomed to (Young, 1993). It has always been assumed that
unemployment and crime are married to each other, with an increase in one
leading to the rise in the other.  According
to the NSW Bureau and Crime Statistics and Research (BOCSAR), economic stress
on unemployed parents leads to inadequate parenting practices, which in turn, increases
the risk of juvenile involvement in crime (2012).  

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Historically,
there have been two major thoughts regarding the unemployment-crime
relationship, the first focus on the supply of offenders and the second, supply
of victims (Melick, 2003).  Economist
have tried to explain the economic behavior of potential offenders and the way
they react to changes in the economy.  I
hypothesis that higher unemployment leads to higher crime rates.  In my paper, I will attempt to examine the
relationship between criminal activity and unemployment rate.  I believe that higher unemployment rates
definitely leads to higher crime rates. 
When people are unemployed, they tend to commit crimes that are
considered misdemeanors and carry less jail time (Chiricos, 1987).  The notion that unemployment induces criminal
behavior is instinctively interesting and grounded in the belief that
individuals are rational creatures and they respond to incentives (Raphael and
Winter-Ebmer, 2001).  To ascertain the
current information needed to prove my claim, I will be using three datasets,
the Census Bureau’s American Community Survey, the Federal Bureau of
Investigation’s Crime Report, and the United States Bureau of Labor
Unemployment Statistics. 

Unemployment means
that a person is unemployed if for whatever reason they are unsuccessful in
finding adequate employment.  But a
person who choose not to work is considered to be economically inactive, and
are not considered to be unemployed because they choose not to work (Raphael
and Winter-Ember, 2001).  There has been several
reports where people believe that unemployment is one of the major factors
leading to an increase in crime rate. 
Unemployment is no joke and neither is crime.  Being unemployed lead to huge income
disparities in society and could very well lead to an increase in crime rate
(Papps & Winkelmann, 1999). 

The thought that
high unemployment rates result in an increase in the crime rate and criminal
activity is based on the fact that economically people can be rational creatures
but they respond to incentives (Weisband and Eck, 2004).  In other words, people are rational
individuals but they respond to incentives and as a consequence, they commit
crimes that they think will give them the highest and quickest payoff (Levitt,
2001). This theory gives the thought behind why when times of economic
recession and high unemployment would generally lead to a spike in the crime
rate (Raphael and Winter-Ebmer, 2001).  

 There are many factors that can be the cause
of the crime rate when there is a huge degree of unemployment.  As we have heard and seen from economic
reports, the unemployment rate is one of the most commonly referenced economic
gauges.  In many discussions of potential
impacts of the economy on crime rates, scholars and policy makers have used the
unemployment rate as a substitution for economic strength (Finklea, 2011).  Congress as well as President Obama when he
was in office, showed interest in the relationship between the economy,
unemployment, and crime rates since the early part of 1970 (U.S.
Congress).  As I look back to the most
recent recession that was accompanied by a rise in the unemployment rate the
focus is once again on the relationship between unemployment and crime
rates.  In fact, according to the Bureau
of Labor Statistics, at the onset of the most recent recession in December
2007, the national unemployment rate was 5.0%. 

Since then, we saw
the unemployment rate continue to increase throughout the recession, reaching a
whopping 9.5% in June 2009 when the recession was officially ended (BLS).  This rate continued to grow and peaked at
10.0% in October 2009, then started decreasing slightly in 2010 and
thereafter.  As of November 2017,
unemployment rate is 4.1% the lowest it has been since the economic recession in
2007.  Just to get a picture of how the
unemployment rates have changed over the last 18 years, I will be using information
from the Bureau of Labor Statistics that shows the unemployment rate on a
monthly basis beginning January 2000 through November 2017.  The table below will give a breakdown of the
unemployment percentage rates beginning 2000 to 2017, with the average
percentage calculated for each of the total years.

Even though the
table above show the unemployment monthly rates for 2000-2017, the chart itself
is based on the average percentage rates for the same years.  On an average 2009 (9.3) and 2010 (9.6) show
the highest percentage rate, followed by 2011 (8.9) and 2012 (8.1). 


         According to Raphael and
Winter-Ember (2001), there have been significant positive effects of
unemployment on property crime rates.  However,
both researchers and scholars have some theories concerning the relationship
between unemployment and crime (Becker, 1968), some of which will be discussed
in this paper.  One theory is that people
tend to make irrational choices between legitimate activities and criminal
activities as a means of economic gain (Becker, 1968).  This theory predicts that as unemployment
increases it will be correlated with increases in violent crime rates.  The reason for this connection is that during
periods when there are less opportunities for legitimate income, people would
usually turn to illegal ways of obtaining money (Freeman, 1996). 

In theory anyone
would think that the crime rates would go down as unemployment decreases, but
that is not always the case.  A person
that has a criminal mind, will continue to steal even though unemployment has
decreased (Howsen and Jarrell, 1987). 
But a nominal increase in jobs should have a more positive affect on
decreasing the crime rate because people have a legitimate reason to make a
living (Melick, 2003).  But based on
Department of Justice and Uniform Crime Report data, during periods of
unemployment people have more time to spend at home and communities where
people look out for each other, or violent crimes are more often involve
acquaintances or strangers rather than individuals with close relations (Chiricos,
1987).  Based on the two assumptions
above, one can assume that if unemployment had an equal effect on increasing
criminal incentive and decreasing criminal prospect, then there would categorically
be no correlation between the two.  But
if the criminal intent was stronger than the effects of decreased opportunity,
then there would be a positive correlation between unemployment and crime rates
(DOJ-UCR).

            The
data that will be used for my equation comes from the Bureau of Labor
Statistics, the World Bank, and the U.S. Crime Disaster Center.  I used a sample size of 18 years that was
sufficient to give an accurate picture of variation in unemployment and crime
rates.  All the data sources are
credible, accurate and reliable.  The
model variables will consist of the following:

            E =
Employee

N = Work or Crime

            W = Wages

            Wb = Return
from crime

            UR = Unemployment rate

            A = Unemployment
benefits

            P = Probability of
being caught

            CSTn =
Committing a crime

            J = Punishment

 

Statistically
trying to determine if there is a relationship between crime and unemployment,
individuals have the choice to choose going to work of committing crimes.  The hypothesis is that if a person is
unemployed, he will more than likely go out and commit a crime.  To try and prove my hypothesis, I will be
using a model that is based on a model by Ehrlich (1973) and Freeman (1999).  The model variables are listed above.  Now if a person chooses crime when he
believes that the expected return from crime, minus the cost of committing the
crime is higher than the return that is expected from working, then this
equation is fulfilled, E(Wb) – CSTn > E(W)
(Edmark, 2005).  If this equation is
true, then the person committing the crime will believe that crime actually
pays because the money they got for committing the crime was more than actually
doing some honest work.  The expected return
on crime E(Wb), will be a probability-weighted average on the
return, if the person is caught after committing the crime, p,
and not caught (1 – p) (Edmark, 2005). 

Alternatively, if
the person is caught, then the return Wb, would be dramatically
reduced by the punishment he will receive, J. Therefore, the equation for being
caught committing the crime would be E(Wb) = (1 – p)Wb +
p(Wb – J).  This shows
that the expected return from work is highly affected by the unemployment rate
and the unemployment benefit that they may receive.  Otherwise, the Ho that a
person will commit a crime when unemployment rate is high, can be rejected if
the person decides to get a job as opposed to committing a crime (Edmark,
2005).  Also if the individual is
employed during the period, and they obtain wages before becoming unemployed,
they will receive unemployment benefits. 
Thus the hypothesis equation is E(W) = (1 – UR) W + URA.

             In order to show the relationship between
motor vehicle theft and unemployment rate, I will be using data that was
selected to create sample based on population density that have a high crime
rate (USDOJ, BJS, 2016).  The sample data
is based on the years 2000-2016.  For
this analysis, I will use as the dependent variable the motor vehicle theft
rate (MVTR) as reported by the FBI (2000-2016). 
Over the past 16 years, auto theft has fluctuated greatly as shown by
the following chart.  In my analysis I
will also use other independent variables to show change in the unemployment
rate (DUNEMP) over the years because it is very significant because it shows
that it has a significant relationship with the motor vehicle theft rate (MVTR)
(Melick, 2003).  My hypothesis here is
that if there is a deviation of one percentage point in the rate change of
unemployment, there would be an additional twenty-four more vehicle thefts per
100,000 individuals.  This answers my
original hypothesis, that people are indeed motivated by changes in the
unemployment rate from one year to the next.

            The
chart show that the years between 2000 and 2006 had the highest auto thefts
based on their population size.  Years
2007 through 2016, auto theft rate had started a downward turn, with 2013,
2014, and 2015 have the lowest auto thefts per population.

            Robbery
is also thought to be escalated by unemployment.  In order to calculate the probabilities that
a person, a worker or criminal, will encounter another, worker or criminal, is
to assume that they are randomly matched, according to Roland and Verdier
(2003), Burdett, Lagos and Wright (2004), Huang, Laing, and Wang (2004), and
Pearson and Siven (2007).  If I let s
and t
represent the number of criminals and the number of workers in the economy, I
will be able to assume that one criminal can be robbed by another criminal,
because criminals have no knowledge of who their intended targets will be
(Roland and Verdier, 2003).  There is a
probability, though, that any one, the criminal or the worker, will meet a
criminal, or that they get robbed is s/(s + t) = s, assuming the size of
the population.  Another probability is
that a criminal will rob is (t + s) / (s + t) = 1, the show that
a criminal will rob anyone, either another criminal or someone else.  The chart below shows the rate of robberies
compared to the number of robberies committed during the time period.  It appears that the robbery rate does
increase as the unemployment increase.

I also included a stacked chart that
gives a little better understanding of the measurement of the robberies
compared to the population.

Next to auto theft
and unemployment, violent crimes is also another aspect of how unemployment
affects the behavior of individuals who are jobless.  When people stay unemployed for any length of
time, they become discontented and set out to do bodily hard to others just to
take what they have worked hard to achieve (Ehrlich, 1973).  I see this too many times on the news, where
people have been violently assaulted and robbed and when the catch the “perp”
he is often unemployed and has been for quite some time. 

The following
chart shows the number of violent crimes that was committeed against
unsuspected individuals from 2000-2016. 
Data showed that 2013 and 2014 had the lowest violent crime rate based
on population.  In 2013 the population
was 316,497,531, there were 1,168,298 violent crimes, a rate of 369 per 100,000
people.  It was a little better in 2014,
but not by much.  In 2014 the population
was 318,907,401 with 1,153,022 violent crimes, a rate of 361 per 100,000
people. 

 

            Property
crime rate is another avenue that has been associated with unemployment.  In a report done by Fallahi, Pourtaghi and
Rodriguez (2012), showed that if one percent increase in the unemployment rate
will increase the property crime rate by 71.13419 per 100,000 inhabitants. If
the same one percent was to increase, violent crime rate will also increase by
31.87251 per the same 100,000 inhabitants (2012).    

There are a lot of
controversy about unemployment being the main cause of crime and how much both
are related.  Analyzing the relationship
between unemployment and crime rate, I hypothesized that there would be a
positive correlation and I believe that my report supports that fact.  Individuals that are unemployed are willing
to participate in illegal activities because they believe that the return would
be higher than actually getting a job.  I
believe that my paper also supports my hypothesis that unemployment leads to
higher crime rates, both property, theft, and violent crime.