The Determinants of Rates of Crime in England and Wales - Essay Example

?Executive Summary The project discusses about the economics of crime taking the England and Wales under consideration. The crime rates will be takenunder consideration and market model of crime with supply and demand schedules will help to explain the relevant theories. The positive and the negative incentives that drive the offenders into crime will also be taken into account. The final portion illustrates about spatial incidence of crime. Introduction The project discusses about the effectiveness of economics on social issues. Crime has been taken as a social issue and discussion on economics of crime follows. The crime incidence of England and Wales has been taken as the real world scenario. The market model of crime determines the expectations of the participants on probability of getting victimized. It has been found out that the elasticity of the supply and demand curve have a major role to play. In order to deal with the effect of crimes, the public enforcement laws can be made more stringent as that will reduce the opportunity cost of committing crimes and pay-off will get reduced. Aims and objectives of the study The overall aim of the project is to show the relevance of the economic analysis to real world scenario. The micro or the macro theory is used by the policy makers. There is no simple solution to the major social issues and the aim of the project is not to provide readymade remedies. The aim is not to take into account the fine details of the different policies of the government that relates to social issues but the concrete aim of the study is to witness the way economists analyze problems relating to social issues. The aim is to show the use of economics in the vast fields and economics cannot be encircled only in business. Economics of crime The aim of the project is to determine the determinants of rates of crime in England and Wales. Some researchers put out their views by providing a framework in which they provide an economic analysis of crime. They assumed that the offenders are economically rational as well as utility maximizing. They predicted crime can be kept in check through stringent law enforcement polices as high prices for crime commitment would act as the disincentive for the offenders to participate in illegal activities (Winter, 2008). On the other hand if the labour market does not provide job opportunities the potential offenders would get involved in criminal activities as it reduces the opportunity cost of doing such activities. Following the inferences many scholars were interested in empirically testing the predicted relationship between crime and variables which can influence the offender’s decision by affecting the costs and benefits of crime commitment (Eide, Rubin, Mehlop, 2006). The analysis has distinct implications for the purpose of policy analysis. The response towards crime is one of the priorities for governments. It is necessary to understand the effective control of crime and the interaction between crime and the various social factors. Crime can be reduced by increasing the income level of the offenders and if the empirical tests on the crime rates in England and Wales can confirm the validity of the predictions, it can be stated that economic growth can act as the effective way to reduce the frequency of crime (Peltzman, and Fiorentini, 1997). Effective control of crime through economic growth will be less costly for the government as economic prosperity is indeed the aim of the government (Freeman, 1999). The reduction of the rates of crime is a positive side effect of good conditions within the economy (Benson, 2010). The decision of committing crime is analyzed on the basis of cost. According to BBC Magazine the average cost of keeping an offender in prison for a year sums up to 50,000 pounds. The champions of community punishment point their effectiveness stating the reoffending crime rates to be at 36% compared to 60% among those released after short term punishments. Decision to commit crime: An individual can earn 100 pounds in legal income and the income can be supplemented with property crime. The economists will consider the point of how to use time is one of utility maximization. The following figure shows a concave utility curve. The individual are under incomplete information and can determine whether the benefits from life of crime are more than costs of a life of crime. Therefore a successful criminal can gain the legal income plus loot amount (U1) while unsuccessful one loses some portion of legal income (U2). The expected utility is as follows: EU{U1,U2, p1,p2} = (p1* U1) + (p2* U2) It has been assumed that expected utility from crime is equivalent to expected utility from legal earnings. Data trends A crime is defined as the act that is against the act of laws or legal code of conduct. There are different types of crimes namely crimes against some person, crimes against property, crimes against morality, organized crimes and white collar crimes. In order to analyze the data the rates of crime are measured by the figure of sins documented by police per 1000 population. The data ranges over the period of 1971 to 2000 and are obtained from Home Office publication Criminal Statistics. The chart below shows the time trends of four crimes over the period under consideration. (Han, 2009, p. 68). From the above diagram it can be judged that burglary and theft depicted quite similar shapes. The two types of crimes kept on increasing and reached the peak in 1992-1993. But till 2000 the two types of crime experienced mild reduction. The crime rate for fraud and forgery is less than 10 offenses per 1000 population over the period under consideration. The rate jumped sharply in the year 1998. The overall crime rate is denoted by the line at the top and picked up the trends of burglary and theft before 1998. Theft and handling crimes account for 50% of the total committed crimes while burglary accounts for 30% of total crimes. Therefore the shape of the overall crime rate curve is being dominated by data on theft and burglary. But in the three year period of 1998 to 2000 the overall crime rate curve experienced a jump in spite of the rates of burglary and theft decreased. This may be because of the introduction of new rules of crime counting. To deal with the breaks in structure, the three periods have been deleted from the sample while analyzing the overall rates of crime and that for fraud and forgery. The table below denotes the summary statistics for each crime that is being tested. The sample size is 30 if no values are missing in the analysis (Han, 2009). The rate of crime specific detection is used as the proxy for apprehension probability and anticipated to be correlated negatively with rates of crime. The rate of detection measures the percentage of crimes under record and solved by police through means of charges like fines or caution. The number of offenders put into prison per 1000 population has been taken as the proxy of severity of punishment. Hs variable is expected to be negatively correlated as well with crime rates. The table below specifies the basic summaries of explanatory variables. The quarterly update presents the crime statistics for England and Wales from the British Crime Survey and from the recorded crimes in police stations. The table below shows the percentage change in BCS crime on the basis of interviews from the month of September, 2011 compared with the previous year. (Home Office Statistical Bulletin, 2011, p. 7). From the above graph it can be seen that crimes on vandalism and bicycle theft are decreasing. The figure below shows the percentage in numbers of the crimes that were recorded in the same period as above and compared with the previous year. (Home Office Statistical Bulletin, 2011, p. 7). From the above graph it can be seen that criminal damage decreased most among all other types of criminal activities while robbery and other theft offenses increased over the period under consideration. The table below shows the crime numbers and risk of being a victim based on BCS interviews in the same period as above compared with the previous year. (Home Office Statistical Bulletin, 2011, p. 7). The above table states that all household crimes decreased but there was decrease in BCS crime. The table below shows the recorded crimes in the same period compared with the previous year. (Home Office Statistical Bulletin, 2011, p. 8). Total property crime decreased in the period under consideration along with all recorded crime. Market model of crime A market model of crime can be determined using the assumptions below. The assumptions are as follows: The offenders, potential victims, illegal goods and services purchasers and the authorities concerned with law enforcement behave in accordance with (often unobserved) optimising rules. The participants have expectations about the legitimate as well as illegitimate opportunities of employment. They have expectations on the severity of punishments and the subjective and the objective anticipations are linked. A stable distribution of preferences for crime and for safety from crime exists. The aim of the enforcement law is to maximise social welfare. The conditions of aggregation relating to the behaviour of the participants assure well defined equilibria. There are three actors in the market model. The criminals are taken to be the suppliers of crime. The general public have been looked upon as consumers. They are the potential victims of crime and spend resources with the aim to prevent crime while some demand for crime. The recreational drugs can be set as an example. The third actor is the public policy or intervention of the authorities. The policies are aimed to tax the incidence of crime through the interaction of supply and demand conditions. Crime is treated as economic offense and net return have been denoted by ? per offense (i). The payoff or the wage per offense committed is exogenous while direct costs involved in the actions are endogenous. The opportunity cost of labor is ?L and is exogenous. Penalties are endogenous while the taste of crime of the person can be exogenous or endogenous. Expected individual net return is equal to the difference between criminal wage and direct cost and opportunity cost. Therefore, Risk neutrality is assumed and taste for crime is constant. If the net payoff is more than the threshold then the person will commit crime. The supply of crime has been determined by distribution of taste. The following figure will provide a clearer picture. S is an increasing function of net returns and changes in the net return will push individuals along the threshold irrespective individual commit crime or not. The arrowed portion denotes supply of crimes of passion. Supply of crime is reduced with rise in ?. If individuals can incorporate variation in legitimate wages then S would be a function of average net return. The threshold level in that case would be jointly determined by taste of crime and legal income (Becsi, 1999). Depending upon price direct demand for offenses equals turnover of illegal goods and services whereas direct demand equals to that of crime tolerance. Costs are involved in buying protection and the willingness of individuals to incur the costs acts as to reflect the perception n the cost and benefits. Expenditure of individuals on protection is e*i which varies according to the probability of getting victimized. The perceived risk of victimization equals to crime rate and so, The loss of becoming victimized is Li marginal efficacy of self protecting efforts are related to elasticity of victimization and losses with respect to spending made on protection. Direct demand for protection is not the same as demand for offenses and is an increasing function of crime rate. Higher expenditure raises cost and reduces the differential between criminal wage and opportunity cost and direct cost. In public enforcement laws crime is treated to be an externality. The authorities look to maximise the welfare by minimising losses conducted through crimes. The optimal strategy should account for the interaction between types of crime and. Therefore a complementarity as well as substitutability effects exist. Probability of apprehension is (pi) and penalty is (fi). Optimal strategy is given by Ti=pifi, and expenditure is positively related to crime rate. So, E=(pifi,qi). The curve of optimal expected punishment is an increasing function of crime rate. Market Equilibrium: Economic growth, accumulation of assets and inequality in income would increase ?i . The tolerated level of crime would increase if T remains constant. T increases or decreases according to changes in legislation and sentencing. The demand shifts according to the changes in the pay -off to crime. Shifting in the supply curve occurs when amount of crime supplied within a given net return increases or decreases. Individuals are assumed to be risk neutral and the positive as well as negative incentives would have the same deterrent effect. Bekar showed relaxation of the assumption has the potential to affect the effectiveness of the incentives (Becker, 1968). An increase in severity of punishment by 1% would have greater effect than 1% increase in probability of punishment for the risk avoiders. For the risk takers an increase in probability of punishment by 1% would have greater effect than 1% increase in severity of punishment. The positive and the negative incentives may have no effect if criminals are involved in spending their time in legal activity due to the effects of income and substitution. For the risk takers the loss of income as a result of higher penalties can induce the offenders to engage themselves in criminal activities. The changes in rate of crime are dependent on the elasticity of the two curves. The changes can be determined by reaction of the offenders, persons victimized and enforcement of laws to changes in incentives. Negative incentives are those that act as the barrier to the offenders entering into illegitimate activity where as positive incentives are those that induce the offenders to enter into the activity. The positive incentives include legitimate employment and opportunities of earnings, the programs of rehabilitation and lower disparities in income distribution. For risk takers the loss of earnings resulting from higher penalties can induce the offenders to spend more time on criminal activity. If the loss of legitimate part time wage increases then efforts might get shifted towards illegitimate earnings through higher risks and the risk taker might gamble the punishment in order to attain higher criminal return. Explain and illustrate spatial variation in the incidence of crime The market model of crime discussed above have the potential to account for spatial differences in crime rates. The differences in rates of crime can be due to changes in economic and social conditions. The new market model differs from that of above as c is a function of vector individual attributes (x) which has a spatial element. The new market model is as follows: The attributes are correlated. Benefits, probability of social disapproval vary according to location and can affect the individual thresholds. The focus of the model is on size of the city (N) and x. It has be stated that if an act of an agent to become criminal positively affects the decision of the neighbour then rates of crime is expected to differ from the predicted rates by city characteristics alone (Ehrlich, 1997). Criminals acting as peer group affect stigmatizing the rule abiding actions. Interaction on social grounds contributes in reducing the risk perceptions of likeminded people. With increase in the boundaries of cities it is difficult to locate the offenders as the pool of suspects’ increases and the probability of apprehension reduces. The informal community sanctions declines with the size of the city. A dense area is anticipated to have characterised by more victims within a given radius and urban density provides greater proximity between the wealthy victims and poor offenders. Glaeser lends his focus on observed crime and on the market model estimated crime. The error terms are attributed to social interactions. The paper decomposes crime into three components. The probability of apprehension may change according to changes in the size of city. The returns on crime as well as the attributes of community may change the characteristics of individuals that affect invariant crime costs. The empirical analysis of the paper is provided below: It can be stated that the deterrence lowers in case of large cities as there are fewer assets per crime. The returns in crime are larger in case of large cities while the concentration of crime is larger in large cities as well. This may be the resultant of weak social ties. The spending on police is positively related to the size of cities. The British public policy stated that crime can discourage inhibit local regeneration which can cause a downward spiral in community status. An argument can also be proposed that crime tends to reduce the community social capital investment with the view to encourage the high out migration rates as well as low in migration rates. The social interactions can generate neighbourhood effects. US studies make the use of hedonic regression in order to estimate HH marginal willingness to ignore crime which was reflected in spatial variation in the prices of house. The reports generally found a negative relation between violent crime and HPs. But surprisingly there exists positive association between property crime and HPs. Part b ii) In the period of June 2011 to June 2012, the recorded crime rates fell by 6.1%. The crime rates fell by 27.2% compared to the decade ago. (Justice, 2012). The fall in the number of crimes can be taken as a surprising factor as the period aftermath recession and rising level of unemployment was supposed to increase the rate of crimes. On the last year ending in March, 2012, there were 1.96 million people who were given disposal of court or were preceded against the court. On the year ending on March, 2010, 2.1 million people were given disposal of the court or were preceded against the court. This shows there was a decline of 6.8%. On the year ending on March, 2008, the out of court disposals decreased by 35.9% compared with that of the previous year. On the year ending on March, 2012, the number of police cautions decreased by 7.7% as compared to that the previous year. The number of police cautions decreased by 37.9% on the year ending on March, 2007 as compared to the previous year. The penalty notices for disorder fell by 11.9% on the year ending on March, 2012 compared with that of 2011. At the end of March, 2012, 1.28 million offenders were sentenced. The number of persons sent to immediate custody fell by 1% compared to 2011. In the study of Siddhartha Bhattacharya, a strong negative relationship between the detection rates of crime and crime has been found. The results portray a 1% increase in the rates of detection is leading to 0.11% fall in the percentage of burglary along with 20% decrease in theft as well as 0.14% fall in cases of fraud and forgery . The following table will provide a clearer picture: (Bhattacharya, 2011, p. 4). Conclusion The aim of the project was to determine the effectiveness of economics n social issues. For the purpose, crime was selected as a social issue. It should be remembered the aim of the project was not to attain the ultimate solution in reducing crimes, but to induce the effects of crime rates on the economy and how economics is beneficial in analyzing the effects. The crime variations in England and Wales were taken as an example under which the discussion varies. It has been found out that crime rates are negatively related to economic growth. Researchers have opined that in an economy characterized by growth and is on the sustainable track, the rates of crime is lower n those cases. In economics of crime the offenders are economically rational and the aim is to maximize utility. Offenders while engaging themselves into crime tends to analyze the opportunity cost involved in the actions. If they find that the opportunity cost of committing the crime is not at par with the legal income levels they engage themselves into crime. The expected utility from crime is the determinant factor here. Data analysis on the statistics obtained on crime in England and Wales have been provided. The rate of crime specific detection is expected to be associated negatively with crime rates. A market model of crime has been presented under some assumptions. Three actors have been taken under consideration and the roles the actors play in the model have been discussed. It has been found that rise in expenditure reduces the difference between criminal wage and opportunity cost and direct cost. The optimal expected punishment is positively associated with rates of crime. Market equilibrium occurs where the demand and the supply curve interacts. Higher fines can affect the income levels of the risk takers which can induce the offenders from getting involved in crime related activities. The elasticity of the supply and demand curves are responsible for the change in crime rates which can be determined by the behaviour of criminals, the persons victimized during the actions and the enforcement laws prevailing. In spatial incidence of crime it has been discussed that agents’ actions to become offenders can affect the decision of neighbours and the crime rates will vary from the anticipated rates by city characteristics. The deterrence rates will be low for large cities while the returns in crime are larger as well. References Han, L. ECONOMIC ANALYSES OF CRIME IN ENGLAND AND WALES. 2009. Web. Retrieved From: http://etheses.bham.ac.uk/584/3/han10PhD.pdf. [Accessed: 15th November, 2012]. Freeman, R. THE ECONOMICS OF CRIME. 1999. Web. Retrieved From: http://webspace.qmul.ac.uk/fcornaglia/economics%20od%20crime.pdf. [Accessed: 15th November, 2012]. Eide, E., Rubin, P., Mehlop, J. Economics of crime. 2006. Now Publishers Inc. Benson, B. Handbook on the Economics of Crime. 2010. Edward Elgar Publishing. Winter, H. The Economics of Crime: An Introduction to Rational Crime Analysis. 2008. Routledge. Peltzman, S. and Fiorentini, G. The Economics of Organised Crime. 1997. Cambridge University Press. Ehrlich, I. Crime, Punishment, and the Market for Offenses. 1997. Journal of Economic Perspectives-Volume 10. Becker, G. Crime and Punishment: An Economic Approach. 1968. Journal of Political Economy, Vol. 76, No. 2. Becsi, Z. Economics and Crime in the States. 1999. Federal Reserve Bank of Atlanta ECONOMIC REVIEW. First Quarter. Home Office Statistical Bulletin, 2011. Crime in England and Wales: Quarterly Update to September 2011. Web. Retrieved from: http://www.homeoffice.gov.uk/publications/science-research-statistics/research-statistics/crime-research/hosb0112/hosb0112?view=Binary. [Accessed: 26th November, 2012]. Justice, 2012. Criminal justice statistics. Web. Retrieved From: http://www.justice.gov.uk/statistics/criminal-justice/criminal-justice-statistics. [Accessed: 28th November, 2012]. Bhattacharya, S. 2011. An Analysis of Crime and Crime Policy. Web. Retrieved From: http://www.civitas.org.uk/crime/crimepolicyMarch2011.pdf. [Accessed: 28th November, 2012]. Read More