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Analytical Paper: Estimate Crime Rate In Kenya
ABSTRACT This analytical paper uses data for the period 1992 '" 2009 to estimate crime rate in Kenya. The total index crime rate is disaggregated into three parts (a) crimes against persons (murder, homicide and physical injury) (b) crimes against property and theft (c) rape. The data reveals that several variables such as unemployment, inflation rate, gross domestic product and population density affect crime against person, property and rape. The data is analyzed using Statistical Package for Social Scientists (SPSS) and secondary data obtained from The Kenya Police and the Kenya National Bureau of Statistics (KNBS). CHAPTER ONE 1.0 INTRODUCTION There are many qualitative and empirical studies that focus on correlation between unemployment and crime rates. The most common and more acceptable is that unemployment causes crime. It is evidently unclear if crime causes unemployment. The table below shows the percentage of unemployment rate in percentage for Kenya for period ranging 2000 to 2008. The total crime as recorded by the Kenya police is as shown in the table below Source: Kenya National Bureau of Statistics Source: Kenya Police 1.1 BACKGROUND The unemployment rate is simply the number of unemployed persons divided by the total labor force (Williamson, 2002). Those that are unemployed are defined by the government as people who do not have a job, have actively looked for work in the prior four weeks, and are currently available for work. Unemployment is a prime concern for policy makers and it is often thought to be closely related with crime. Many researchers have attempted to answer the question. Agell and Nilson (2003) and Papps and Winkelmannn (1999) are examples of studies which found strong positive relationship between unemployment and crime, while Chilson and Choe (2005) reiterated that there is ambiguity in the empirical studies of crime economics regarding various income variables used to proxy the expected net gains from crime and as a result empirical findings are often mixed or contradictory to one another. Crime rates vary enormously across countries and regions, so does unemployment which varies enormously too. Cost of living and hardship due to lack of employment are normally and widely considered to be closely related to level of criminal activities. Many economists agree that they do contribute to making problems like poverty and crime more intractable and undermines the political base of democratic capitalism. There are a significant number of studies linking income inequality to crime. Madden and Chiu (1998) mentioned that it seems reasonable to expect that the level of property crime will be influenced in some way by the distribution of income (and wealth) while Teles (2004) reiterated that monetary and fiscal policies have impacts on crime. 1.2 Research Problem Crime is as old as mankind itself, moreover, it has become a common societal phenomenon and many people view it as if it is a functional component of the organization of human groupings (Scafer, 1976). The proposition that unemployment induces criminal behavior is intuitively appealing and grounded in the notion that individuals respond to incentives. Conceptualizing criminal activity as a form of employment that time and generates income Witte and Tauchen (1994), a "rational offender" should compare returns to time use in legal and illegal activities and make decisions accordingly. Many variables have been attributed to the ever increasing crime rate in Kenya but no study on the impact of unemployment of the crime rate has been done, this study seeks to unearth the relationship between unemployment and crime rate in Kenya. 1.3 Justification Various authors have tested for the presence of a relationship between unemployment and crime, most employing time-series data or cross-sectional data. However there has not been a research study for the same in Kenya. Hence in this paper I test for the presence of a relationship between unemployment and crime rate in Kenya. There is a need for a policy change towards employment creation and crime reduction in the country. It is against this background that I carry out this study. 1.4 Objectives 1.4.1 General objective The general objective of the study is to present an over-all picture of the crime trends in the Kenya (considering 8 provinces) within a period of twenty years (1990-2009), without necessarily looking for root to these crimes. 1.4.2 Specific Objectives Specifically, the study aims: 1. To identify social, economic and criminal justice factors associated with fluctuations in the recorded crime rates. 2. To measure the magnitude of change in the crime rate per unit change of each of the significant explanatory variables. 3. To use important outputs for policy making or assessment purposes. 1.5 Scope of the Study The study focuses on the index crimes over the period 1990 - 2009 expressed in crime rate (crime per 100, 000 populations). Index crimes are those violations of the penal code. They are considered to have socioeconomic significance, and they occur with sufficient regularity. These include crimes against person (murder, homicide, physical injury and rape), and crimes against property (robbery and theft). All other crimes are classified as non-index. Only a limited number of potential influences on crime can be quantified, so that not all-influencing factors can be assessed since the study formulates a statistical model. CHAPTER TWO 2.0 LITERATURE REVIEW Holding all factors constant, the decrease in income and potential earnings associated with involuntary unemployment increases the relative returns to illegal activity. The concurrence of crime and labor market trends suggests that recent declines in crime rates may be due in part to the current abundance of legal employment opportunities. To the extent that increased legitimate employment opportunities deter potential offenders from committing crime, a decline in the unemployment rate may be said to cause decline in crime rates. Despite the intuitive appeal of this argument, empirical research to date has been unable to document a strong effect of unemployment on crime. Studies of aggregate crime rates generally find small and statistically weak unemployment effects, with strong effects for property crime than for violent crime. Criminal activity reduces the employability of offenders, either through a scarring effect of incarceration or a greater reluctance among the criminally- initiated to accept legitimate employment, criminal activity may in turn contribute to observed unemployment. Moreover, crime level may itself impede unemployment growth and contribute to regional unemployment levels. In "Crime and Economic Incentives, " Machin and Meghir use data collected from England to find the extent to which declining labor market opportunities specifically the wage rate, contributes to crime. Using the lower 25 th percentile wage rate instead of the unemployment rate leaves the possible notion that crime and work may coexist. Taken into account was both the size of the police force and conviction rates, as well as average sentence lengths. The lowest percentile wage and the less likely probability that one would be convicted were both found statistically significant in their model. From a policy standpoint, Machin and Meghir suggest that bettering the education system to provide increased economic incentives to work rather than to resort to criminal activity is the best prevention. Published in the American Economic Review, Burdett, Lagos and Wright focused on the relationship between crime, unemployment and inequality, suggesting that two identical neighborhoods could have different crime rates simply due to a variance in the wage rate. Basing their study on the efficiency wage theory, which states that firms pay their workers a higher wage to decrease "shirking, " or poor performance and lack of effort, they propose that firms may pay a higher wage to deter crime, which causes turnover if these individuals are caught. The mathematics of this article is highly complex but the conclusion is that crime leads to wage dispersion and multiple equilibria while efficiency wage theory will only lead to one equilibrium. Cook and Zarkin (1985) suggest four categories of factors that may empirically link the business cycle and crime: (1) legitimate employment opportunities, (2) criminal opportunities, (3) consumption of criminogenic commodities (alcohol, drugs, guns) and (4) the response of the criminal justice system. Then quality and the quantity of the criminal opportunities may be lower during recession as potential victims have less income, consume less and expend more effort on protecting what they have. Alcohol, drugs and guns are normal goods; consumption of these goods will be pro- cyclical. The extent of variation in policing and criminal justice activity over the business cycle is less clear since the quantity and efficacy of criminal justice activity depends on state tax revenues, community cooperation and political pressures (Levitt 1997). Omission of any of these factors from aggregate crime regressions may bias the estimate of the relationship we are seeking to measure. For example assuming that the consumption of drugs and alcohol is negatively correlated with unemployment and positively related with crime, omitting these factors from the regression model will bias estimates of the unemployment '" crime effect downward. While Becker (1968) emphasizes on the cost and benefits of crime, Ehrlich (1973) extends Becker's crime model of criminal gangs and suggest that there is substitution effect between property and violent crime. They further explained that unemployment increases the relative attractiveness of large and less violent gangs engaging more in property crime. Papps and Winkelman (1999) found some evidence of significant effects of unemployment on crime both for total crime and for subcategories of crime in their analysis that covered sixteen regions over the period 1984 to 1996 in New Zealand. As for the relationship between income and crime , Hipp (2007) using a unique non rural sub sample from a large national survey ( The American Housing Survey) found that higher income reduces disorder but increases crime, while Fedderke and Luiz (2008) in their study on South Africa found that rising income lower political instability, in turn it increases crime rate. Both authors concluded that there exist meaningful positive relationship between income and crime. Gould, et.al (2002) also concluded that both wages and unemployment are significantly related to crime but that wages plays a larger role in the crime trends over the last few decades. Narayan and Smyth (2004) in their study on Australia, employing Granger causality test, to examine the relationship between seven different categories of property crime and violent crime against the person, male youth unemployment and real male weekly average earnings from 1964 to 2001 within a co integrated and vector error correction framework, It is found that fraud, homicide and motor vehicle theft are co integrated with male youth unemployment and real male average weekly earnings. However , there is no evidence of a long run relationship between either break and enter robbery, serious assault or stealing with male youth unemployment and real male average weekly earnings. On the contrary Habibullah and Law (2007) also utilized vector error correction model (VECM) in their study about crime and financial economic variables in Malaysia and generally their result suggests that criminal activity in Malaysia cannot be explained properly by real income per capita, financial wealth and interest rate. In another related paper, Baharom and Habibullah (2008) found that crime exhibits neither long run nor short run relationships with income inequality and they are not co integrated. In contrary Habibullah and Baharom (2008) employing bounds test found that in the long run, strong economic performances (real income per capita) indeed have a positive impact on murder, rape , assault, daylight burglary and motorcycle theft, while on the other hand, economic conditions have negative impact on armed robbery. Magnus and Matz (2008) went a step further whereby they separated the effects of permanent and transitory income, diverting from the traditional aggregated measures. They reported that an increase in inequality in permanent income and traditional aggregated measures yields insignificant effect. There are two main views of the relationship between economic conditions and crime (Cantor and Land, 1985). The motivational perspective expects a positive relationship between crime and poor economic conditions, although there may be two different sources of motivation. One source of motivation may be the frustration that results from people being unable to obtain or to maintain employment at the same time they want to maintain or to improve their standard of living (Cloward and Ohlin, 1960; Greenberg, 1977, 1985; Merton, 1938) Thus, if economic conditions deteriorate overtime, the proportion of the population feeling frustrated should increase. The overall effect of increased level of frustration in the population would be an increased rate of crime. A second source of motivation may be the outcome of a rational choice process where the individuals weigh the costs and benefits of criminal behavior against legitimate behavior (e.g., Becker, 1968; Block and Heineke, 1975). In this view, crime would again be more likely for unemployed persons since the total cost of crime may be perceived as low relative to the total gains from the crime and imprisonment would not involve the loss of income from employment. In either case poor economic conditions would be responsible for higher rates of crime by increasing the proportion of the population prone to commit criminal acts. Greenberg (1985) applied the motivational perspective to the age distribution of crime in an attempt to explain youth have the highest rates of crime. He hypothesized that employment has become increasingly important to the youth because of a variety of media influences that have increased the perceived needs of the youth, at the same time that access to the adult labor market has been restricted. To obtain material possessions (e.g clothing) and to engage in leisure activities, youth are dependent on funds provided by their parents and / or own employment. In the absence of parental financial support, youth need to obtain some kind of employment. The problem of the youth occurs when they discover that access to jobs in the adult labor market is restricted, thereby preventing them from successfully gaining independent economic support to satisfy their perceived needs and decreased access to the adult labor market are then expected to increase the level of motivation to crime among the youth. There appears to be a relatively straight forward application to the relationship between youth unemployment and crime. The opportunity perspective suggests that when the unemployment rate among youth is high, youth would be more likely to remain at or near home, since they will probably be unable to support all their desired leisure activities. (Greenberg, 1985). In addition since trends in youth unemployment parallel closely trends in total unemployment there will likely be greater direct supervision of youth by other unemployed family members in the periods of high unemployment. Conversely as youth are more involved in the labor market, they have greater opportunity to commit crimes since the level of direct supervision of their behavior is reduced with the increased time spent away from home involved in both employment activities and leisure activities made possible by earnings from that employment. Research on the relationship between youth crime and economic conditions has failed to provide a clear set of findings. Studies using both cross-sectional and time series age specific arrest data from the Uniform Crime Reports have found both a positive relationship (Allan and Steffen Meier, 1989; Fleisher, 1963) and a negative relationship (Gibbs, 1966; Glaser and Rice, 1959; Smith et al, 1992) between crime and unemployment among youth. Singell (1967) used arrest data from Detroit (cross sectional and longitudinal) and Danser and Laub criminal activity among youth as a test for trends in unemployment and crime among the youth. However, the conclusions of this body of research are limited by the relatively short time series used in each study. Only Fleischer's (1963) and smith et als (1992) analyses of national arrest data cover periods greater than twenty years. CHAPTER THREE 3.1 THE ANALYTICAL FRAMEWORK/ MODEL 3.1.1 Data and Model Specification The study makes use of panel data consisting of time'"series observations over a period of twenty years (1990'"2009). Most of the secondary data were taken from the Kenya National Bureau of Statistics and from Kenya Police. Panel data were used because they permit a rich model specification and have more advantages since they allow the researcher to sort out economic effects that cannot be distinguished with the use of either cross-section or time-series data alone. Some of the advantages are: (1) Panel data provide an increased number of data points, which in turn generate additional degrees of freedom; (2) Panel data incorporate information relating to both cross-section and time-series variables, thereby substantially diminishing the omitted-variable problems; (3) Panel data eliminates some of the statistical inference problems which may arise from a probable correlation between some of the explanatory variable (problems such as multicollinearity, heteroscedasticity and autocorrelation) and the extent of under-reporting of crime rates; (4) Panel data has the ability to control heterogeneity and the likely joint endogeniety of some of the explanatory variables and the bias due to under-reporting. 3.1.2 The Statistical Model Panel-data analyses using states, counties, metropolitan statistical areas, or cities in the United States have generally obtained relatively consistent estimates of the impact of unemployment on crime. A 1% change in the unemployment rate is typically found to increase property crime by 1'"2% contemporaneously but often has no systematic impact on violent crime rates (Lee, 1993; Levitt, 1996, 1997; Raphael and Winter-Ebmer, 2000). Studies that substitute other measures of the labor market conditions at the bottom of the distribution reach similar conclusions (Gould et al., 1998; Machin and Meghir, 2000). The consistency of these results across data sets, includes covariates, and degree of aggregation is encouraging, as the national time series data yield results that are much more sensitive to the particulars of the estimation. I am unaware of any previous panel-data analysis that attempts to relate systematically lags in unemployment to crime rates. Using a state-level panel of annual data for the period 1990'"2009, I run regressions of the form Crimet =?1Unempt +Xt +est where t indexes years and Unemp is the unemployment rate. Crime reflects official crime rates per capita from the Kenya Police. I use the standard violent, rape and property crime definitions. The unemployment rate is included both contemporaneously and once lagged. X is a vector of covariates including inflation rate (CPI), the Gross Domestic Product and Population Density. 3.2 Data analysis (a) For Total crime rate The estimated model is: CR=368.693 '" 1.026UR '" 4.991GDP R2 = 0.867 Where: CR is the crime rate UR is the Unemployment rate GDP is the gross domestic product Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 368.693 25.255 14.599 .000 GDP -4.991 .474 -.959 -10.527 .000 UNEMP -1.026 .519 -.180 -1.974 .065 A Dependent Variable: TCRRATE Unemployment rate and gross domestic product are negatively related to the crime rate. That is, the higher they are the lower are the crime rates. The variables in the model explain about 86.7% variations in the crime rate. (b) For crimes against person The estimated model is: CR=19.441 + 0.173UR + 0.555PD R2 = 0.484 Where: CR is the crime rate UR is the Unemployment rate PD is the population Density The impact of unemployment on crime against persons is that crime rate will increase with an increase in the unemployment rate. The impact is not quite sensitive; unemployment and the other explanatory variable which is population density explain only 49% of the variation of crime rate against persons. Increase in the population density consequently leads to an increase in the crime rate against persons, the dense the area the more prone residents will be to criminals. Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 19.441 9.995 1.945 .068 UNEMP .173 .162 .186 1.066 .301 POPDEN .555 .146 .662 3.795 .001 R2 = .484 A Dependent Variable: CRATEHUM (c) For crimes against property The estimated model is: CR = 224.579 + 0.642UR '" 2.865PD R2 = 0.897 Where: CR is the crime rate UR is the Unemployment rate PD is the population Density In the model above unemployment rate is positively related to crime rate while population density is negatively correlated to the crime rate. This implies that an increase in the population density will lead to less crime against property. This is because there are many people in a densely populated area and hence low propensity to crime. On the other hand an increase in unemployment leads to increase in crime rate, this can be explained by that those unemployed will engage in criminal activities such as stealing properties and selling them in order to earn a living. The variables in the model explain about 89.7% variations in the crime rate. Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 224.579 16.263 13.809 .000 UNEMP .642 .264 .190 2.438 .026 POPDEN -2.865 .238 -.937 -12.030 .000 a Dependent Variable: CRATEPRO (d) For rape The estimated model is: CR = 0.34PD '" 1.3UR -5.391 R2 = 0.884 Where: CR is the crime rate UR is the Unemployment rate PD is the population Density The impact of unemployment on rape crime is negative, implying that an increase in unemployment rate leads to a decrease in the rape cases. This conforms with a study by Cohen (1981) who concluded that by spending a greater proportion of their time in or near their homes, unemployed persons have lower risks of violent victimization due to decreased exposure to the potential offenders. Population density has a positive impact on crime rate, an increase in the population density leads to increase in rape cases. Coefficients (a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -5.391 2.120 -2.543 .021 UNEMP -.130 .034 -.312 -3.776 .002 POPDEN .340 .031 .903 10.935 .000 a Dependent Variable: CRATERAP 4.0 RECOMMENDATIONS AND CONCLUSION 4.1 CONCLUSION From the results we see that economic factors and especially unemployment is a robust determinant of crime rate. The implication is that the more stable an economy is, the lower the crime rates. The following conclusions have been drawn; The results of the data analysis reveal that unemployment in Kenya causes crime. The reason is that unemployment in a country is a complimentary indicator of income opportunities in the legal labor market. Therefore when unemployment rate increases the opportunities for earning income decreases which instigates the individuals to commit crime. The end result is that unemployment causes crime. 4.2 RECOMMENDATIONS Economic growth with social and economic justice should be a key objective of the planning strategy. All major economic determinants of crime '" unemployment, inequalities, GDP growth are needed to be adequately addressed by the policy makers to check the crime rate in the country. With a superior data a more conclusive model can be found, furthermore this study indicates several possibilities for further research. In particular the introduction of additional regressors that may explain crime such as income inequality, police population and alcohol consumption. REFERENCES A.H. Baharom and Habibullah M.S. (2008). "Crime and Income Inequality: The Case of Malaysia". MPRA working paper, University of Munich: 11871 Agell, J. and Nilsson, A (2003) Crime, Unemployment and labor market programs in turbulent times. Forthcoming in the Journal of the European Economic Association Becker, Gary.1968. "Crime and Punishment: An Economic Approach". Journal of Political Economy 76(2):169'"217. Cook, Philip, and Gary Zarkin. 1985. "Crime and the Business Cycle". Journal of Legal Studies 14(1):115'"28. Ehrlich, Isaac. 1973. "Participation in Illegitimate Activities: A Theoretical and Empirical Investigation". Journal of Political Economy 81(3):521'"65. Gould, Eric, Bruce Weinberg, and David Mustard. 2002. "Crime Rate and Local Labor Market Opportunities in the U.S.: 1979'"1997". Review of Economics and Statistics 84(1):45'"61. Habibullah, M.S. and Law, S.H. (2007) Crime and financial economic variables in Malaysia: Evidence from a vector error-correction model. Journal of Social and Economic Policy 4(2), 263-276. Habibullah M.S and A.H. Baharom. (2008). "Crime and economic conditions in Malaysia: An ARDL Bounds Testing Approach". MPRA working paper, University of Munich: 11910 Levitt, Steven.2001. "Alternative Strategies for Identifying the Link between Unemployment and Crime". Journal of Quantitative Criminology 17(4):377'"90. Levitt, S.D. (2001) Alternative strategies for identifying the link between unemployment and crime. Journal of Quantitative Criminology 17(4) 377-390 Machin, Steve, and Costas Meghir. 2004. "Crime and Economic Incentives". Journal of Human Resources 39(4):958'"79. Madden, P. and Chiu, W.H. (1998) Burglary and income inequality. Journal of Public Economics 69, 123-141. Magnus, G. and Matz, D. (2008) Inequality and Crime: Separating the effects of permanent and transitory income. Oxford Bulletin of Economics & Statistics 70(2), 129-153. Republic of Kenya. 2002. The 1998/99 Integrated Labor Force Survey Report. Nairobi: Ministry of Planning and National Development. Teles, V.K. (2004) the effects of macroeconomic policies on crime. Economic Bulletin 11(1), 1-9. 2 By mike bosire -
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