Unemployment and Crime

An analysis of the Cointegration and the Socio-economic Impacts of Unemployment on Crime In today’s society, we are faced with an alarming situation with tends to plaque us and have made it on many of our chief economist and researchers list. Evidence of this is based on the works of many econometricians who tried to find the relationship between crime and how it affects the level of unemployment within a country. Siegel (2009) asserted that “crime is a violation of societal rules of behavior as interpreted and expressed by a criminal legal code created by people holding social and political power. Individuals who violate these rules are subject to sanctions by state authority, social stigma, and loss of status” (p. 18). On a more macro-economic level, the Bureau of Labor Statistics (BLS) defines unemployment as people who do not have a job, have actively looked for work in the past four weeks, and are currently available for work. Also, people who were temporarily laid off and are waiting to be called back to that job are counted as unemployed.

Numerous studies have shown that there is a positive correlation between unemployment and crime with the former bear strong influence on the latter. Economics of crime or illegal activities has grown into a new field, which requires an investigative review of its principal components; particularly this is as a result of the rapid increase in criminal activities “in various western and eastern countries of the world.” Ehrlich (1973) considers that unemployment has its effects on the crime rate. He outlines that the unemployment rate can be viewed as a complementary measure of income opportunities available in the legal labor market. Therefore, when unemployment rate increases, the opportunities in the legal sector decline leading individuals to associate in criminal activities. To analyze the co-integration and the socio-economic impact of unemployment and crime, we first need a review of literature concerning the study. Coomer (2003) undertook a study to assess the impact of macroeconomic factors on crime.

He applied Ordinary Least Square (OLS) regression analysis to find out the results. In his analysis, he first included unemployment, poverty, prison population, high school, college education degree, and income disparities as independent variables and run the regression to understand the relationship. He then dropped the insignificant variables and rerun the regression and found that unemployment, inflation and poverty impact crime positively. With all the empirical results already found, the question, has the number of unemployed workers exacerbated the level of rising crime? This has been an issue, which have been escalating for decades with remarkable hopes of curving this trend seems a public cry. Raphael and Winter-Ebmer (2001) has shared his proposition and provided assistance with a complete analysis of the correlation of unemployment on crime. Throughout their studies, United States data were used to establish relationships between unemployment and felony offenses.

After the study was completed, the results were conclusive that unemployment was correlated to property crime rates regardless of demographic and economic factors, less evidence related violent crime with unemployment. In the co-integration analysis, several variables were used in the model and accounts are usually made on which variables significantly impacts the results. Much research to this date has confirmed that unemployment affects violent crime rate where both appears to move in a positive direction. One of the variables that significantly affect the relationship between crime and unemployment is the individual’s age. It is often noted in many of the statistical results by top government official statistics that young adults vastly exceed older persons in the category of arrest for burglary, robbery, and other crimes such as theft or misappropriation of property belonging to others. Farrington et al. (1986) British study concluded that the relations between the two variables are most intense for youths who were out of school as well as work.

This result has lent meaningful contribution to the statement that one tends to be ineffective and non-productive and finds other means or ways to entertain oneself. Many can expect many economic issues to arise during this stage as often will normally start with minor criminal offences and has the potential to escalate thereafter. Baharom and Habibullah (2008) have examined in their study the causality between income, unemployment, and crime in eleven European countries employing the panel data analysis for the period 1993-2001 for both aggregated (total crime) and disaggregated (subcategories) crime statistics. They offered the following analysis: The impact of crime on an economy can be segregated into, primarily the prevention cost, and secondarily the correctional cost and the lost opportunity of labor being held in correctional facility. Costs acquainted with crime preventions, such as private investment for crime prevention gadgets such as anti theft or anti burglary equipment, or government expenditures such as campaigns and education on safe society and police personnel expenditure.

The correctional cost refers to cost such as correction facilities cost and prison personnel, while the lost opportunity refers to the lost of potential labor contribution due to being in correction facilities. (p. 56) Any statistical generalizations on the linkage of crime to unemployment, as well as to age or other personal attributes of offenders and non-offenders, can only be tested with incomplete data. The completeness of our knowledge on lawbreakers necessarily varies with the extent to which they are caught and the use of detention rather than alternative penalties for those convicted. Data on employment, age, and various other attributes of person’s committing crimes is usually reported for those offenders who are arrested, but their total number, and information on them is somewhat diminished (although undoubtedly made more precise) if one studies only those arrestees who are subsequently convicted of the crimes for which they were arrested. Furthermore, data on the personal attributes of those convicted are often not compiled in as much detail for those fined or released on probation as for those who are imprisoned.

Kapuscinski, Braithwaite, and Chapman (1998) have illustrated in their paper that studies show that there is a strong positive association between crime and unemployment at the individual level, a clear positive association at the cross-sectional level that gets weaker as the level of geographical aggregation increases, but quite an inconsistent relationship over time. They draw on the assumption of (Becker, 1968; Ehrlich, 1973) that many mainstream economists generally believe that unemployment is associated with crime because reduced expected utility from legitimate work decreases the opportunity costs of illegitimate work. They have come to the conclusion that a sensible scientific disposition is that to be confident about a relationship one would want to see it supported at both the cross-sectional and the time-series levels of analysis. This they noted is because the potential sources of error under the two methodologies are very different.

When there is a convergence, more confidence is warranted that the association is a result of true relationships captured under the two methodologies rather than the different sources of error that exist in the two approaches. Yet, sadly, the two methodologies all too frequently give different results. In summary, evidence and research indicate that unemployment is predictive of crime, but disproportionately for youth, the least educated, those in damaged or disorganized families, and those segregated in poor minority residential areas. Also, these relationships of unemployment to crime are expected to continue unless unsegregated housing, special education, family unity, work experience, and desirable career jobs become more readily available to those who are unemployed.

References

Baharom, A. H. & Habibullah, M. S. (2009). Crime and Income Inequality: The Case of Malaysia. Journal of Politics and Law, 2(1). Retrieved March 17, 2010, from http://mpra.ub.uni-muenchen.de/11927/1/MPRA_paper_11927.pdf Coomer, N. (2003). America’s underclass and crime: The influence of macroeconomic factors. Issues in Political Economy, Vol.12. Ehrlich, I. (1973), Participation in illegitimate activities: A theoretical and empirical investigation. The Journal of Political Economy, 81(3), 307-322. Farrington, D. P., Gallagher, B., Morley, L., St. Ledger, R. J & West, D. J. (1986). Unemployment, School-leaving, and Crime. British Journal of Criminology 26(4) 335–356. Glaser, D., & Rice, K. (1959). Crime, Age and Employment. American Sociological Review 24(5), 679–686. Kapuscinski, C., Braithwaite, J., & Chapman, B. (1998). Unemployment and Crime: Toward Resolving the
Paradox. Journal of Quantitative Criminology, 14(3), 215. Retrieved from Academic Search Premier database. Raphael, S. & Winter-Ebmer, R. (April, 2001). Identifying the Effects of unemployment and Crime. Journal of Law and Economics. Vol. XLIV. Siegel, L. J. (2009). Criminology. Belmont, CA: Thomson Learning Inc.