Crime Statistics and Regression Model - Research Paper Example

Crime Statistics and Regression Model Literature Review In order to gain a better understanding of a particular area of study, it is important to review the literature. This will provide the necessary background information and a better understanding of what has been done before and what further work needs to be done. A literature review provides information on conflicting information in different studies and may also provide information on the reliability of the information obtained on the basis of the data used and the methodology employed in carrying out the research studies. It may also provide valuable information on how best to conduct a study of this type based on the criticisms noted in other studies. It may also direct the researcher to other relevant and useful research studies. Definition According to the FBI Uniform Crime Report (UCR) (2014) offenses such as motor vehicle theft, burglary, arson and larceny are all described as property crimes. All except arson involves the theft of money or property. In the case of arson, it represents damage to property. Motor vehicle theft and burglary are the two main property crimes of special interest because they are serious, prevalent and reliably measured by the FBI (Alfred Blumstein and Richard Rosenfeld 2008; 18). Additionally, only 32% of larcenies are reported compared to more than 50% of burglaries (Alfred Blumstein and Richard Rosenfeld 2008; 18). Several reasons are given for the difference in reporting rates and included among them is the requirement to report for insurance purposes. An important source of heterogeneity associated with the theft of motor vehicles is that a large proportion is stolen for joyriding as opposed to economic gain. Trends in property crime statistics The trends in crime statistics in the United States show a 16.3% decline in property crimes for the period 2004 to 2013 (FBI). In fact, burglary has been on a steady rate of decline since 1980 (Alfred Blumstein and Richard Rosenfeld 2008; 18). An explanation for this might be increase in sanctions in relation to burglary which makes it less attractive than robbery. In terms of motor vehicle theft the trend which changed n the 1990’from one of an increase in rate per thousand changed to a decline. This trend is still continuing (Alfred Blumstein and Richard Rosenfeld 2008; 18). The theft of motor vehicles has been associated with the era of crack when they were stolen to trade for drugs and later to prevent their own motor vehicles from confiscation. In order to reliably forecast the property crime rate several factors have been assessed through various studies. Factors impacting the crime rate Several demographic and economic variables have been identified as having the ability to forecast crime trends and therefore provide explanation for the trends in property crime rates. It has been suggested that some of the best predictors of crime are changes in age composition and race. However, an assessment of the changes in age composition indicates that it is very unlikely to significantly influence the rate of crime up to 2025 (Alfred Blumstein and Richard Rosenfeld 2008; 19). The link between crime and economic conditions have a long history and there are several studies that have been done in this area (Alfred Blumstein and Richard Rosenfeld 2008; 24). Some of the economic variables that have found favor with researchers include changing prices of staples, unemployment rate, wages (income), and GDP. Income In the article entitled “The Effect of Income on Delinquency” Fleisher indicates that one of the main reasons for the belief that low income earners have a higher tendency to commit crimes is that the cost of getting involved in activities that are and the possible cost of being caught is slim. This is because offenders do not think they will lose much as the chances of earning high income low and so getting a criminal record will not have great impact. The opportunity cast of spending time in prison is low (1966, 120). Additionally, if the income of potential victims are high then the incentive to commit property crimes, in particular, against them is high. In fact, the results of studies done in 101 cities in the United States in 1960 found that there were lower arrests of young males from families with higher incomes than those from families with lower incomes for crimes such as larceny, motor vehicle theft, burglary and robbery (128 – 129). Becker, in the article “Crime and Punishment: An Economic Approach” indicates that although crime is an activity that is of economic importance, it has not been given the attention it deserves by economists (Becker 170). Becker’s model was based on a cost benefit approach where the individual would consider the costs versus the benefit before making a decision on whether to commit the crime. The decision to commit a crime then relates to the income that can be generated from the act. In extending the work of Becker (1968), Ehrlich in the article “Participation in Illegitimate Activities: A Theoretical and Empirical Investigation” looked at how the propensity to commit a crime is affected by the distribution and level of income. Ehrlich’s argument was based on the thinking that payoffs, and in particular that associated with property crime was dependent on the opportunity that a potential victim provided (Ehrlich 539). In his study Ehrlich found that higher median family income and higher crime rates, including property crimes. Additionally, higher crime rates were also associated with offenders the percentage of families whose income lies below a half of the median income. This finding contradicts the findings from previous studies including Fleisher. (See Fleisher). Unemployment rate Studies have suggested uncertainties in the relationship between crime generally and unemployment. In fact, Freeman’s study “Crime and the Job Market” indicates that the results from several studies have been inconsistent and therefore inconclusive. In fact, Freeman concluded that even when the estimation effect of unemployment on crime indicates that there was minimal contribution to increasing crime trends (Freeman 1). Imrohoroglu, Merlo and Rupert has provided additional information on the effects of unemployment on crime in their article entitled ““What Accounts for the Decline in Crime?” Their model predicted that approximately 79% of individuals who engaged in criminal activities were employed compared to 21% being in the unemployed category. Therefore, changes in the unemployment rate did not have any effect on the rate of crime. However, the explanation is that the unemployment rate was low but a particular cohort of the population – the youths had a higher unemployment rate. Illicit drugs The studies show that illicit drug use and property crime are associated to the extent that drug addicts want to support their drug use. Greene in his article entitled “An Examination of the Relationship between Crime and Substance Use in a Drug/Alcohol Treatment Population,” indicates that the majority of drug users started to commit these crimes before getting involved with drugs (Greene 627). Critical Analysis of the literature The research results have not been conclusive and so it is difficult to indicate that the unemployment rate has a significant impact on property crime. Freeman’s research suggests that it is the need for certain basic or other need fulfilling item that forces individuals to commit property crimes. From this information a relationship between income and employment is obvious and these effects have to be separated in any study. When individuals are not satisfied with their income they will find other means of “topping it up” and this is so if they have a tendency to be overcome by greed. It all comes down to values and values are not affected by income and unemployment or being from a rich or a poor family. Despite that fact the majority of offenders were employed individuals and so it is clear that unemployment is not a relevant factor for predicting crime. It is the amount of money that they earn and their lifestyle that will determine what they do. Some of these factors are seen as coincidence and not major factors that could cause changes in crime statistics (Alfred Blumstein and Richard Rosenfeld 2008; p 24). Additionally, they are considered to be difficult to anticipate and so macroeconomic indicators that can serve as effective predictors of crime are being sought (Alfred Blumstein and Richard Rosenfeld 2008; 24). Use of Illicit drug is a relevant indicator as so many drug addicts are involved in property crime just so that they can earn some money to buy their next dose of drugs. The background information suggests that there is plenty of room for further studies in the area of crime and the factors which affect it. As one researcher indicates, some of the variables are just coincidental and so further work should use more robust models in order to account for autocorrelation and other factors that impact the reliability of these studies. Methodology This study aims to use data on several States in the United States (US) for the years 2004 to 2013 in order to obtain information on the nature of property crime based on the fact that crime is related to a particular area and its associated characteristics. In order to do this it employs panel data techniques that take into account time series information as well as cross sectional information relating to the several variables of income, unemployment rate, and illicit drugs. It aims to use a model that accounts for the dynamics of criminal activities in the relevant States. It employs the Generalized Methods of Moments (GMM) methodology which facilitates control over unobserved effects that may be specific to a particular State. These effects may result from the joint endogeneity of some of the independent variables in the model. It is important that this is controlled in order to obtain reliable results. According to Greene, data sets combining time series and cross-sectional data are use in econometric studies (Greene 557). Time series models are sometimes very expensive and since panel data sets are geared towards cross-section analysis. Additionally, heterogeneity is the central focus of any analysis of this type (557-558). The econometric model that will be used in this experiment takes into account variables such as the unemployment rate, income and illicit drug use. The formula reads as follows: PropCrimei, t = αi + αt + PropCrimei, t-1 + βXi,t + Ɛi, t In the model property crime is denoted by the word “PropCrime” and the subscripts “i” and “t” represent the relevant State and time period respectively. The symbols “αi and αt” are the constant terms which represent the fixed effect for state and time and the lagged dependent variable PropCrimei, t-1. The Xi, t represents the explanatory variables that will explain the model and Ɛi, t is the error term. Works Cited Becker, G.S. “Crime and Punishment: An Economic Approach”, Journal of Political Economy, 76.2 (1968): 169-217. Print Blumstein, Alfred, and Richard Rosenfeld. 2008. Factors Contributing to US Crime Trends. In  Understanding Crime Trends: Workshop Report , ed. Arthur Goldberger and Robert Rosenfeld, 13–44. Washington, DC: National Academies Press. (2008). Web 16 Mar 2015 Deitch, David ., Koutsenok, Igor and Amanda Ruiz. “The Relationship Between Crime and Drugs: What We Have Learned in Recent Decades. Journal of Psychoactive Drugs, (2000). Web 16 Mar 2015 Ehrlich, I. “Participation in Illegitimate Activities: A Theoretical and Empirical Investigation”, Journal of Political Economy, 81.3 (1973): 521-565. Print FBI. “Uniform Crime Report: Crime in the United States, 2013.” Web. 16 Mar 2015 Fleisher, B., (1966), “The Effects of Income on Delinquency”, American Economic Review, 56.1/2 (1966): 118-137. Print Freeman, R.B. “Crime and the Job Market”, in Wilson, J. Q. and J. Petersilia (Eds.), Crime. San Francisco: ICS Press. 1994. Print Greene, Bradford T. “An Examination of the Relationship between Crime and Substance Use in a Drug/Alcohol Treatment Population,” Substance Use and Misuse, 16.4 (1981): 627-645. Web 16 Mar.2015 Greene, Williams, H. Econometric Analysis. Upper Saddle River, NJ. 2000. Print. Imrohoroglu, A., Merlo, A. and P. Rupert (2001). “What Accounts for the Decline in Crime?”, Federal Reserve Bank of Cleveland, (2001) wp 0008. Print. http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2013/crime-in-the-u.s.-2013/property-crime/property-crime-topic-page/propertycrimemain_final.pdf Read More