Introduction Unemploymentis among the world’s biggest problem along with a decrease in jobs. The U. S. Bureau of Labor Statistics inJanuary 2016, estimated that approximately 7.9 million people, which is 5percent of the working population were unemployed (Gupta, 2016).
To date, the biggest indicators of the healthof the economy is the national unemployment rate. Because of the loss of disposal income, andwith the continued high unemployment rate, consumers become highly frustratedbecause they can’t find work. Because ofunemployment, they can’t find work, and their standard of living fallssubstantially and that puts pressure of them to maintain the lifestyle thatthey have become accustomed to (Young, 1993). It has always been assumed thatunemployment and crime are married to each other, with an increase in oneleading to the rise in the other. Accordingto the NSW Bureau and Crime Statistics and Research (BOCSAR), economic stresson unemployed parents leads to inadequate parenting practices, which in turn, increasesthe risk of juvenile involvement in crime (2012). Historically,there have been two major thoughts regarding the unemployment-crimerelationship, the first focus on the supply of offenders and the second, supplyof victims (Melick, 2003). Economisthave tried to explain the economic behavior of potential offenders and the waythey react to changes in the economy.
Ihypothesis that higher unemployment leads to higher crime rates. In my paper, I will attempt to examine therelationship between criminal activity and unemployment rate. I believe that higher unemployment ratesdefinitely leads to higher crime rates. When people are unemployed, they tend to commit crimes that areconsidered misdemeanors and carry less jail time (Chiricos, 1987). The notion that unemployment induces criminalbehavior is instinctively interesting and grounded in the belief thatindividuals are rational creatures and they respond to incentives (Raphael andWinter-Ebmer, 2001).
To ascertain thecurrent information needed to prove my claim, I will be using three datasets,the Census Bureau’s American Community Survey, the Federal Bureau ofInvestigation’s Crime Report, and the United States Bureau of LaborUnemployment Statistics. Unemployment meansthat a person is unemployed if for whatever reason they are unsuccessful infinding adequate employment. But aperson who choose not to work is considered to be economically inactive, andare not considered to be unemployed because they choose not to work (Raphaeland Winter-Ember, 2001). There has been severalreports where people believe that unemployment is one of the major factorsleading to an increase in crime rate. Unemployment is no joke and neither is crime. Being unemployed lead to huge incomedisparities in society and could very well lead to an increase in crime rate(Papps & Winkelmann, 1999). The thought thathigh unemployment rates result in an increase in the crime rate and criminalactivity is based on the fact that economically people can be rational creaturesbut they respond to incentives (Weisband and Eck, 2004). In other words, people are rationalindividuals but they respond to incentives and as a consequence, they commitcrimes that they think will give them the highest and quickest payoff (Levitt,2001).
This theory gives the thought behind why when times of economicrecession and high unemployment would generally lead to a spike in the crimerate (Raphael and Winter-Ebmer, 2001). There are many factors that can be the causeof the crime rate when there is a huge degree of unemployment. As we have heard and seen from economicreports, the unemployment rate is one of the most commonly referenced economicgauges.
In many discussions of potentialimpacts of the economy on crime rates, scholars and policy makers have used theunemployment rate as a substitution for economic strength (Finklea, 2011). Congress as well as President Obama when hewas 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 mostrecent recession that was accompanied by a rise in the unemployment rate thefocus is once again on the relationship between unemployment and crimerates. In fact, according to the Bureauof Labor Statistics, at the onset of the most recent recession in December2007, the national unemployment rate was 5.
0%. Since then, we sawthe unemployment rate continue to increase throughout the recession, reaching awhopping 9.5% in June 2009 when the recession was officially ended (BLS). This rate continued to grow and peaked at10.0% in October 2009, then started decreasing slightly in 2010 andthereafter. As of November 2017,unemployment rate is 4.1% the lowest it has been since the economic recession in2007. Just to get a picture of how theunemployment rates have changed over the last 18 years, I will be using informationfrom the Bureau of Labor Statistics that shows the unemployment rate on amonthly basis beginning January 2000 through November 2017.
The table below will give a breakdown of theunemployment percentage rates beginning 2000 to 2017, with the averagepercentage calculated for each of the total years. Even though thetable above show the unemployment monthly rates for 2000-2017, the chart itselfis based on the average percentage rates for the same years. On an average 2009 (9.3) and 2010 (9.
6) showthe highest percentage rate, followed by 2011 (8.9) and 2012 (8.1). .
According to Raphael andWinter-Ember (2001), there have been significant positive effects ofunemployment on property crime rates. However,both researchers and scholars have some theories concerning the relationshipbetween unemployment and crime (Becker, 1968), some of which will be discussedin this paper. One theory is that peopletend to make irrational choices between legitimate activities and criminalactivities as a means of economic gain (Becker, 1968). This theory predicts that as unemploymentincreases it will be correlated with increases in violent crime rates. The reason for this connection is that duringperiods when there are less opportunities for legitimate income, people wouldusually turn to illegal ways of obtaining money (Freeman, 1996). In theory anyonewould think that the crime rates would go down as unemployment decreases, butthat is not always the case. A personthat has a criminal mind, will continue to steal even though unemployment hasdecreased (Howsen and Jarrell, 1987). But a nominal increase in jobs should have a more positive affect ondecreasing the crime rate because people have a legitimate reason to make aliving (Melick, 2003).
But based onDepartment of Justice and Uniform Crime Report data, during periods ofunemployment people have more time to spend at home and communities wherepeople look out for each other, or violent crimes are more often involveacquaintances or strangers rather than individuals with close relations (Chiricos,1987). Based on the two assumptionsabove, one can assume that if unemployment had an equal effect on increasingcriminal incentive and decreasing criminal prospect, then there would categoricallybe no correlation between the two. Butif 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). Thedata that will be used for my equation comes from the Bureau of LaborStatistics, the World Bank, and the U.S. Crime Disaster Center. I used a sample size of 18 years that wassufficient to give an accurate picture of variation in unemployment and crimerates. All the data sources arecredible, accurate and reliable.
Themodel variables will consist of the following: E =EmployeeN = Work or Crime W = Wages Wb = Returnfrom crime UR = Unemployment rate A = Unemploymentbenefits P = Probability ofbeing caught CSTn =Committing a crime J = Punishment Statisticallytrying 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 isunemployed, he will more than likely go out and commit a crime. To try and prove my hypothesis, I will beusing 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 hebelieves that the expected return from crime, minus the cost of committing thecrime is higher than the return that is expected from working, then thisequation is fulfilled, E(Wb) – CSTn > E(W)(Edmark, 2005). If this equation istrue, then the person committing the crime will believe that crime actuallypays because the money they got for committing the crime was more than actuallydoing some honest work. The expected returnon crime E(Wb), will be a probability-weighted average on thereturn, if the person is caught after committing the crime, p,and not caught (1 – p) (Edmark, 2005). Alternatively, ifthe person is caught, then the return Wb, would be dramaticallyreduced by the punishment he will receive, J.
Therefore, the equation for beingcaught committing the crime would be E(Wb) = (1 – p)Wb +p(Wb – J). This showsthat the expected return from work is highly affected by the unemployment rateand the unemployment benefit that they may receive. Otherwise, the Ho that aperson will commit a crime when unemployment rate is high, can be rejected ifthe person decides to get a job as opposed to committing a crime (Edmark,2005). Also if the individual isemployed 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 betweenmotor vehicle theft and unemployment rate, I will be using data that wasselected to create sample based on population density that have a high crimerate (USDOJ, BJS, 2016).
The sample datais based on the years 2000-2016. Forthis analysis, I will use as the dependent variable the motor vehicle theftrate (MVTR) as reported by the FBI (2000-2016). Over the past 16 years, auto theft has fluctuated greatly as shown bythe following chart. In my analysis Iwill also use other independent variables to show change in the unemploymentrate (DUNEMP) over the years because it is very significant because it showsthat it has a significant relationship with the motor vehicle theft rate (MVTR)(Melick, 2003). My hypothesis here isthat if there is a deviation of one percentage point in the rate change ofunemployment, there would be an additional twenty-four more vehicle thefts per100,000 individuals. This answers myoriginal hypothesis, that people are indeed motivated by changes in theunemployment rate from one year to the next. Thechart show that the years between 2000 and 2006 had the highest auto theftsbased on their population size. Years2007 through 2016, auto theft rate had started a downward turn, with 2013,2014, and 2015 have the lowest auto thefts per population.
Robberyis also thought to be escalated by unemployment. In order to calculate the probabilities thata person, a worker or criminal, will encounter another, worker or criminal, isto assume that they are randomly matched, according to Roland and Verdier(2003), Burdett, Lagos and Wright (2004), Huang, Laing, and Wang (2004), andPearson and Siven (2007). If I let sand trepresent the number of criminals and the number of workers in the economy, Iwill 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 aprobability, though, that any one, the criminal or the worker, will meet acriminal, or that they get robbed is s/(s + t) = s, assuming the size ofthe population. Another probability isthat a criminal will rob is (t + s) / (s + t) = 1, the show thata criminal will rob anyone, either another criminal or someone else. The chart below shows the rate of robberiescompared to the number of robberies committed during the time period. It appears that the robbery rate doesincrease as the unemployment increase. I also included a stacked chart thatgives a little better understanding of the measurement of the robberiescompared to the population.
Next to auto theftand unemployment, violent crimes is also another aspect of how unemploymentaffects the behavior of individuals who are jobless. When people stay unemployed for any length oftime, they become discontented and set out to do bodily hard to others just totake what they have worked hard to achieve (Ehrlich, 1973). I see this too many times on the news, wherepeople have been violently assaulted and robbed and when the catch the “perp”he is often unemployed and has been for quite some time. The followingchart shows the number of violent crimes that was committeed againstunsuspected individuals from 2000-2016. Data showed that 2013 and 2014 had the lowest violent crime rate basedon population. In 2013 the populationwas 316,497,531, there were 1,168,298 violent crimes, a rate of 369 per 100,000people.
It was a little better in 2014,but not by much. In 2014 the populationwas 318,907,401 with 1,153,022 violent crimes, a rate of 361 per 100,000people. Propertycrime rate is another avenue that has been associated with unemployment. In a report done by Fallahi, Pourtaghi andRodriguez (2012), showed that if one percent increase in the unemployment ratewill increase the property crime rate by 71.13419 per 100,000 inhabitants. Ifthe same one percent was to increase, violent crime rate will also increase by31.
87251 per the same 100,000 inhabitants (2012). There are a lot ofcontroversy about unemployment being the main cause of crime and how much bothare related. Analyzing the relationshipbetween unemployment and crime rate, I hypothesized that there would be apositive correlation and I believe that my report supports that fact. Individuals that are unemployed are willingto participate in illegal activities because they believe that the return wouldbe higher than actually getting a job.
Ibelieve that my paper also supports my hypothesis that unemployment leads tohigher crime rates, both property, theft, and violent crime.