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(1998), who found supporting evidence about the effects of wage on crime, wage .... pick pocketing, fraud, abuse of trust, robbery, threat, bribery and auto theft.
International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40.

The Effects of Unemployment, Income and Education on Crime: Evidence from Individual Data Rifat YILDIZ Erciyes University, Deparment of Economics, Kayseri, [email protected]. Phone: +90 (352) 437 4913 / 30550.

Turkey.

E-mail:

Oguz OCAL* Nevsehir Haci Bektas Veli University, Avanos Vocational School, Nevsehir, Turkey, E-mail: [email protected], Phone: +90 (384) 511 5676.

Ertugrul YILDIRIM Erciyes University, Deparment of Economics, Kayseri, [email protected]. Phone: +90 (352) 437 4913 / 30560.

Turkey.

E-mail:

ABSTRACT There is a large empirical literature on the determinants of crime. But the studies investigating socio-economic determinants of crime reach mixed results. This paper investigates the effects of unemployment, income and education level on number of criminals, using data from 8896 suspect in Kayseri between 2002 and 2009. We create a panel data set which consists of 13 crime types and 8 time period. Panel GMM-system estimation reveals that income and education substantially affect the number of criminals. However, the impact of unemployment over number of criminals is marginal. The reason for this may be obtained income of unemployed criminals. That is, being unemployed does not necessarily mean having no income. The source of obtained income of unemployed criminals may be unregistered employment, unemployment payment and aid. It is suggested that in crime analysis, the number of unregistered employees and receiving unemployment payment should be dropped in macro data. JEL Classification: K42; R11. Key Words: Crime, Unemployment, Income, Education, Kayseri. *Corresponding author. 1.

INTRODUCTION

Since seminal paper of Becker (1968), what factors affect the decision of potential criminals to commit a crime is a well known issue. But empirical literature reaches mixed results and ambiguities about determinants of crime. The empirical literature about the determinants of crime can be divided into two categories. Studies in the first category use aggregated data in context of both time series and panel data analysis. According to Eide et al. (2006) use of aggregated data, however, has some disadvantages. First of all, behaviors of individuals are based on individual decision, in order to investigate the determinants of individual decision; research should rest on individual data. Secondly, the theoretical models of crime rest on the rational choice of individual. Thirdly, aggregated data leads to aggregation bias. Second category consists of use of individual data. Witte (1980), Myers (1983), Thornberry and Christenson (1984), Viscusi (1986), Trumbull (1989), Hagan (1993), Tauchen et al. (1994), Witte and Tauchen (1994), Grogger (1998), Mocan and Rees (1999) and Camanor and Phillips (2002) have based their research on disaggregated data. But most of these studies investigate the impact of some individual factors on crime using cross-sectional analysis. Very few studies use panel data analysis based on individual data. These studies suffer from both small sample size and lack of some individual variables. For example data used by Witte and Tauchen (1994) involve a 10 percent random sample of young males born in 1945 and residing in Philadelphia and does not involve individual income or wage data. The sample for Thornberry and Christenson (1984), Tauchen et al. (1994) and Hagan (1993) individuals consists of only 567 for the first two studies and 594 for latter. This is the first motivation of our study. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. The aim of this study is to present some evidence for economic model of illegal behavior by analyzing the effects of education, income, and unemployment on the number of criminals using individual data. For the first time in literature the study uses individual education, income, and unemployment variables altogether in panel data framework. Our data includes 8896 people engaged criminal activities between 2002 and 2009. The data from 13 crime types were obtained from the Police Department of Kayseri. Kayseri is a province of Turkey and has a population of 1.184.386. When the police investigate a suspect, they first ask for some general information about suspects, such as education status, employment status and income per month. If the end of the investigation police provides some persuasive evidence against the suspect, judicial procedure is initiated by the prosecutor. Our data are based on only these suspects, who were prosecuted. Fig. 1 Evolution of Crime in Kayseri 2000

Number of crime events

1800

Number of suspects

1600 1400 1200 1000 800 600 400 200 0 2002

2003

2004

2005

2006

2007

2008

2009

Source: Police Department of Kayseri This study investigates crime events in the case of Kayseri for the first time in the literature. Figure 1 illustrates evolution of number of crime events and suspect numbers in 13 crime types. The rapid increase in number of crime events and suspects between 2003 and 2006 is outstanding. What factors explain the decision to commit a crime in Kayseri should be investigated. This is the second motivation of this study. Because criminal numbers correlated its past level, panel GMM techniques were used in this study. Our results suggest that income and education variables are the most important factors on number of criminals. Unemployment has positive but marginal effect on the criminal numbers. Furthermore, we believe that our data and results contribute to solving ambiguity about relationship between crime and unemployment. Our data depicts that unemployed criminals have income. Therefore unemployment may not be correlated with criminal number. This article was organized as follows. In section two the existing literature is reviewed. The third section introduces the data set. Empirical method is presented in the fourth section. The fifth section is devoted to empirical findings and last section includes some concluding remarks. 2.

PREVIOUS LITERATURE

Becker (1968) emphasizes that preference of committing a crime is not different from any economical choice. The decision to commit a crime is based on the comparison of the expected costs and benefits of legal and illegal activities. The difference between criminals and other people is only about benefits and costs. According to Becker, when criminals decide to commit a crime, they meet the decision function and solve this function rationally. In the left side of function there is the number of offenses and in the right side of the function there are the probability of conviction, to his punishment if convicted and other socio-economic and tests variables. Since the seminal paper of Becker, an enormous literature has been developed on the socio-economic basis of crime preference. The studies investigating socio-economic determinants of crime reach mixed results and the variables are whether relevant individual behavior or not is controversial. This study deals with especially income, education and unemployment as socio-economic variables. According to Fleisher (1966), delinquency is International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. positively and highly correlated with income. The low income raises the relative costs of engaging in legal activities and increases the propensity to commit crime. Also since legal income is low, the opportunity cost of serving in time in jail is low as well. Grogger (1998) highlighted that the relationship between wages and crime received little attention in the literature and there is only one study –Schmidt and Witte (1984)– investigating this relationship. But Schmidt and Witte do not reach evidently to support this relationship. According to Grogger (1998), who found supporting evidence about the effects of wage on crime, wage has three effects over crime. Firstly as real wages of young people decreased throughout the 1970s and 1980s, the crime rates among the young increased. Secondly whereas the involvement of African-American people crime is higher than white people, their wages are lower. Lastly where committing crime rises until adulthood and then starts to decrease, youth wages are lower than adults. Grogger focuses on property crime and reaches the conclusion that wage substantially affects the tendency to crime. Similarly investigating changes in crime rates for US from 1979 to 1997, Gould et al. (2002) and focusing on crime rates for UK and Wales from 1975 to1996, Machin and Meghir (2004) conclude that wages affect crime. The literature investigating income-crime relationship seems to reach a consensus, but there are too few studies exploring this relation. The main reason for this situation is the lack of individual wage statistics. Therefore the studies in literature mainly focus on the effect of income distribution on crime. The relationship between unemployment and crime is ambiguous in literature. Freeman (1994) states that although “[M]any people believe that joblessness is the key determinant of crime (…) economics does not support the traditional focus on unemployment as the key labor market variable affecting crime.” Chiricos (1987) reviews 63 studies in the literature by separating them according to use of time series and cross-sectional data. Chiricos reports that most of the studies on the relationship between unemployment and crime reach a positive relationship, but this effect is either little or insignificant. Cantor and Land (1985) in order to explain the mixed results in the literature, distinguish the effects of employment on crime as criminal opportunity effect and criminal motivation effect. The opportunity effect means that reducing unemployment increases the return of crime and leads to negative contemporaneous unemployment-crime relationship. In case of the motivation effect due to worsening economic situation of criminals, unemployment leads to increase in criminal’s tendency to commit a crime. The ambiguity of unemployment-crime relationship continues in recent studies such as Fougere et al. (2009), Lee and Holoviak (2006), Narayan and Smyth (2004). According to Lochner and Moretti (2004), effects of education on crime can be connected through six channels. First channel is by schooling which eventually leads to a rise in individual wage and an increase in opportunity costs of crime. Second channel is to increase costs of punishment for the higher educated individuals, since higher educated individuals gain higher income. Third, by changing the risk perception of individuals, education may lead to abstaining from crime. Fourth, education may lead to a proper approach to crime for individuals. That is, education may lead to hatred towards crime and respect to the rule of law. These channels imply that higher education leads to lower crime. However, fifth channel suggests that education may lead to rise in marginal returns of crime and thus higher education may lead to higher crime rates. Lastly, education may decrease the probability of getting caught and lead to increase in crime rates. In conclusion if the first four factors are dominant, a negative education-crime relationship can be implied. If last two factors are dominant, the relationship between education and crime is positive. The education-crime relationship is also ambiguous in the empirical literature. For instance, Tauchen et al. (1994) and Witte and Tauchen (1994), find no significant link between education and crime. Although Grogger (1998) reaches no significant relationship between education and crime, because of the negative relationship between wage and crime, she concludes that education reduces crime indirectly, by leading to rise in wage. Recent studies such as Lochner (2004), Lochner and Moretti (2004) and Buonanno and Leonida (2009) find a negative relationship between high school graduation and crime. 3.

MODELS AND DATA

We firstly used fixed effects panel OLS technique for the following model;

ln SN i ,t = β 0 + β1 ln U i ,t + β 2 ln LI i ,t + β 3 ln MI i ,t + β 4 ln HI i ,t + β 5 ln LEi ,t + β 6 ln HEi ,t +

β 7 ln CRi ,t + vi + ε i ,t International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. where the subscripts i and t denote crime type and time period respectively. vi is fixed effects for all of the crime types. Dependent variable (SN) is number of suspects for each crime types. Independent variables are the number of unemployed suspects (U), the number of suspects with low income (LI), the number of suspects with middle income (MI), the number of suspect with high income (HI), the number of suspects with low education (LE), the number of suspects with high education (HE) and the clearance rate (CR). All of the variables are in natural logarithmic. Friedman et al. (1989) urges that current criminal activities related to past criminal activities. Criminals gain expertise from their past experience and increase their current crime supply. So the crime model should be dynamic. Fixed effect panel OLS, however, may lead to several dynamic panel biases. Therefore testing dynamic panel data model with GMM techniques in this paper is:

ln SN i ,t = β 0 + β1 ln SN i ,t −1 + β 2 ln U i ,t + β 3 ln LI i ,t + β 4 ln MI i ,t + β 5 ln HI i ,t + β 6 ln LEi ,t +

β 7 ln HEi ,t + β 8 ln CRi ,t + η i + ε i ,t where ηi is unobserved crime type-specific effect. SNi,t-1 is a lag of suspect number. In this model, all of the explanatory variables should be instrumented. Firstly, unemployment may lead to crime; crime may lead to unemployment as well. Secondly, the relationship between suspect number and low income may affect each other mutually. Thirdly income levels may be related to unemployment and education levels. Lastly, there may be measurement error in clearance rate. Since measurement error leads to bias and inconsistent results, clearance rate should be instrumented. The data used in this paper were obtained from the Police Department of Kayseri and it comprises only from the city center of Kayseri. Our panel data set consists of annual observations from 13 crime types between 2002 and 2009. Therefore we create a panel data set consists of 13 crime types and 8 time period. The Police Department of Kayseri uses 59 categories to classify crime events, but some brunches of police department do not record education, income and employment status information about suspects regularly. For this reason crime types including missing value were excluded from data set. The crime types that are included in data set are voluntary homicide, involuntary homicide, assault, shooting, kidnapping, terror, pick pocketing, fraud, abuse of trust, robbery, threat, bribery and auto theft. The police department had used different crime categorization criteria before 2002. Thus the data set begins from 2002. The sample includes nearly 90 percent of suspects in 13 crime types. The suspects with missing information about education, income and employment status were excluded. We have five different data. First one is number of suspects who were prosecuted. Second one is income the level of suspects. Income level is divided into three categories. First category consists of suspects with minimum wage and lowest income. Minimum wage is determined by minimum wage law. Second category comprises suspects with wages between minimum wage and twice the minimum wage. Last category consists of suspects earning more than twice minimum wage. These income categories are called as low income, middle income and high income respectively. Third data is education status of suspects. Education status of suspects is divided into two categories. First category is elementary school graduates. Elementary school education is compulsory in Turkey. Second category is number of suspects with high school and university degrees. The number of suspects with university degree is too few. In order to avoid missing value, number of suspects with university degrees were added to the number of suspects with high school degrees. We classified education categories as low education and high education. Fourth data is number of unemployed suspects. Last data is clearance rate and it is identified as the ratio of the number of crimes cleared up to the total number of crimes for each crime type and year. All clearance ratios were multiplied by 100. Descriptive statistics of all variables are represented in Table 1.

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40.

Table 1 Descriptive Statistics Variables Suspect Numbers Low Income Middle Income High Income Low Education High Education Unemployment Clearance Rate

Mean 3.961 3.488 2.644 0.988 3.687 2.001 3.246 4.357

Standard Deviation 1.060 1.068 1.197 1.117 1.137 1.293 1.257 0.441

Minimum 1.098 0 0 0 0 0 0 2.337

Maximum 6.033 5.602 5.590 4.356 5.476 5.459 5.455 4.605

Notes: All variables are expressed in natural logarithmic.

Our sample consists of information about 8896 suspects in 13 crime types. 60.65 percent of sample is in low income category, 32.11 percent of suspects are in middle income category and 7.22 percent of sample is in high income category. As to education status 76.07 percent of sample is in low education category and percent of high education category is 23.92. Unemployed suspects have 57.13 percent of the sample. 4.

EMPIRICAL METHODS

Econometric models of crime estimated via panel OLS technique suffer from various dynamic panel biases. First between current crime and past crime value, there is a significant relationship. Secondly, some regressors may be endogenous, such as unemployment and low income. Thirdly, there are statistically significant unobservable cross-section effects and the regressors may be correlated with these effects. Fourthly, crime data may have measurement error. Lastly, our sample has quite small time dimension. All of these problems indicate that there is a need to use of dynamic panel data model. Therefore we use GMM techniques. We select GMM-system estimator. For comparative objectives, we use fixed effects panel OLS and GMM-level which does not control crime type specific effects. Recently, to cope with dynamic panel bias, Arellano and Bond (1991), Arellano and Bover (1995) and Blundell and Bond (1998) dynamic panel estimators are used in crime analysis frequently. Using GMM, Arellano and Bond transform all regressors by first-differencing. Therefore, Arellano and Bond technique is known as difference GMM. Second way to get over dynamic panel bias is to take lags of dependent, endogenous, exogenous and time invariant variables as instruments. This is known as GMM-level. If there are weak correlations between current and past value of variables, GMM estimator reaches bias results. Arellano and Bover (1995) and Blundell and Bond (1998) assume that first differences of instruments are uncorrelated with the fixed effects. The assumption causes generation of more instruments and can substantially improve efficiency. Blundell and Bond demonstrate that if a variable is close to random walk, because past levels transmit little information about future changes; first-differenced GMM estimator has poor finite sample properties. Hence Blundell and Bond suggest that if a variable is close to random walk, GMM-system should be used. As opposed to Arrelano and Bond, Blundell and Bond instead of transforming the regressors to remove the fixed effects, transform the instruments to make them exogenous to the fixed effects. Arellano-Bover and Blundell-Bond approaches build a system of equations, which consists of two equations. First is original equation and second is transformed equation, so this is called GMM-system. GMM-system incorporates GMM difference and level approaches. GMM estimator is inconsistent if lagged values of the explanatory variables are invalid instruments. In order to test validity of instruments, Arellano and Bond suggest two specification tests. The first is Sargan test. Sargan test has the null hypothesis of overall validity of the instruments. Failure to reject the null hypothesis supports to the model. The second is first order (AR1) and second order (AR2) serial correlation tests. Even if original error term is uncorrelated, differenced error term should indicate first order serial correlation. Second order serial correlation of the differenced error term should not be observed. If there is second order serial correlation of the differenced error terms, original residual is serially correlated. Therefore second order serial correlation indicates that instruments are misspecified. Hence it should fail to reject the null hypothesis of no second order serial correlation. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. 5.

EMPIRICAL FINDINGS

Table 2 reports estimation results. Although fixed effect OLS, GMM-level and GMM-system results are indicated in Table 2, we are interested in only GMM-system results in the last column. Table 2 Regression Results Fixed effects OLS Suspect Numbert-1 Low Income Middle Income High Income Unemployment Low Education High Education Clearance Rate Sargan Test [P Value] AR1 Test [P Value] AR2 Test [P Value] R2 Observation Number

GMM-level

GMM-system

0.3292 (0.0374)*** 0.2333 (0.0293)*** 0.0666 (0.0137)*** 0.0177 (0.0151) 0.2658 (0.0480)*** 0.0692 (0.0186)***

0.0340 (0.0100)*** 0.3499 (0.1320)*** 0.2657 (0.0604)*** 0.0539 (0.0142)*** 0.0186 (0.0095)** 0.2150 (0.1582) 0.0680 (0.0428)

0.0320 (0.0105)*** 0.3173 (0.1319)** 0.2222 (0.0474)*** 0.0528 (0.0132)*** 0.0205 (0.0072)** 0.2776 (0.1471)* 0.0839 (0.0340)**

-0.0113 (0.0312)

-0.1039 (0.0199)***

-0.0841 (0.0172)***

60.66 [0.087]* -2.25 [0.024]** -0.32 [0.752]

72.83 [0.236] -2.41 [0.016]** 0.43 [0.664]

91

91

0.98 104

Notes: Standard errors, reported in parentheses, are robust to heteroscedasticity and autocorrelation. The small-sample correction suggested by Windmeijer (2005) was applied on standard errors in GMM estimations. ***, ** and * indicate coefficient significant at the 1%, 5% and 10% levels, respectively. First order and second order test for serial correlation of the error term, distributed as standard normal N(0, 1) under the null of no serial correlation. Sargan test is a test of overidentifying restrictions, distributed as chi-square under the null of instrument validity. Number of cross-sections: 13 crime types. Time span: 2002–2009. All variables were instrumented using lag t−2. The codes (xtabond2) written by Roodman (2006) was used in GMM estimations.

The GMM specification tests are as expected. The Sargan test confirms that the instruments used are valid. While there is evidence of first-order serial correlation, there is no evidence of second-order serial correlation. GMM-system results indicate that except for clearance rate, all of the variables have positive signs. A lag of suspect number positively affects current suspect number at 1% significance level. But the effect is small and a lag of suspect number is not the main determinant of suspect number. Whereas all of the income variables have positive effect on suspect number, this does not mean that the increase of income does not lead to decrease in suspect number. The coefficient of low income variable is nearly 0.32. The positive effect of middle income decreases nearly 1/3 times. Although the effect of high income is positive, the coefficient diminishes more than 4 times as to the middle income coefficient and 6 times as to the low income coefficient. These results should lead to the inference that the increase of income leads to reduce of number of criminals. In other words, income variables and suspect number are positively related at level, but increase of income leads to decrease of criminal number. Unemployment has a positive but the smallest effect on the suspect number. This is the same puzzle in literature regarding the marginal effect of unemployment over the crime. We believe that our data and some macro variables can solve this puzzle. Firstly all of 8896 suspects’ income is higher than zero. That is being unemployed does not necessarily mean having no income. Since the unemployed suspects give the information about her income to the police, these income must not be illegal. There may be three main sources of this income of the unemployed suspects. First one is unregistered and seasonal employment. According to OECD surveys, unregistered employment in Turkey between 2002 and 2009 is 40-50 percent. Second one is unemployment payment and the third is aid coming from various government agencies, aid agencies and relatives. The income obtained by unemployed suspects may break the unemployment-suspect number relationship. In other words there may be “crowding-out effect” of the income obtained by unemployed suspects on unemployment. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. Both education variables have positive signs. This is the second puzzle in crime literature. Some studies find a positive high school graduation-crime relationship, while some find a negative relationship. Our result confirms the positive relationship. However, positive coefficient of high education is 3.5 times lower than the coefficient of low education variable. The increase of education level leads to decrease of growth level of suspect number. In other words, positive high education-crime relation does not mean that increase of education level cannot lead to decrease of crime. Only clearance rate has negative sign. That is, the increase of probability of being captured leads to decrease of the suspect number. The effect of clearance rate is relatively small. We think that the reason of this small effect of clearance rate is the fact that crime statistics are not disclosed to the public. In this case, potential criminal decides under lack of information. It is likely that the decision based only on her observation is wrong. 6.

CONCLUDING REMARKS

In this article we studied the relationships between number of criminals and its socio-economic determinants using completely individual data set consisting of 8896 suspects in Kayseri between the years 2002 and 2009. Using panel GMM-system we achieved some results. First, the increase of income leads to decrease of number of criminals. Second, the effect of low education and high education on criminal number may be positive. But the positive effect of high education is lower than the positive effect of low education. That is, the increase of education level leads to the decrease of number of criminals. Third, unemployment has marginal positive effect on number of criminals. The article argues that unregistered employment, unemployment payment and aid to unemployed people break the unemployment-crime relationship. It is suggested that in crime analysis, the number of unregistered employees and receiving unemployment payment should be dropped in macro data. Fourth, clearance rate has a negative but small effect on crime. Lastly, our results indicate that a lag of crime is not the main factor leading to crime. For policy purposes, our results imply that income policy, education policy and security policy are efficient to decrease crime. According to our results especially income policy is the most efficient factor. If the minimum wage level increases twice, number of criminals may decrease 1/3 times. If the minimum wage level increases more than twice, number of criminals may decrease 6 times. Education policy is the second efficient factor. If the low education level increases to the level of high education, the number of criminals may decrease 3.5 times. REFERENCES Arellano, M. and Bover, O. (1995), Another Look at the Instrumental Variable Estimation of Error-components Models, Journal of Econometrics, 68: 29-51. Arellano, M. and Bond, S. (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, The Review of Economic Studies, 58(2): 277-297. Becker, G. S. (1968), Crime and Punishment: An Economic Approach, The journal of Political Economy, 76 (2): 169-217. Blundell, R. and Bond, S. (1998), Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87: 115-143. Buonanno, P. and Leonida, L. (2009), Non-market effects of education on crime: Evidence from Italian regions, Economics of Education Review, 28: 11–17. Cantor, D. and Land, K.C. (1985), Unemployment and Crime Rates in the Post-World War II United States: A Theoretical and Empirical Analysis, American Sociological Review, 50(3): 317-332. Chiricos, T.G. (1987), Rates of Crime and Unemployment: An Analysis of Aggregate Research Evidence, Social Problems, 34(2): 187-212. Comanor, W.S. and Phillips, L. (2002), The Impact of Income and Family Structure on Delinquency, Journal of Applied Economics, V(2): 209-232. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. Eide, E., Rubin, P.H. and Stepherd, J.M. (2006). Economics of Crime, Foundation and Trends in Microeconomics, 2(3): 205-279. Fleisher, B.M. (1966), The Effect of Income on Delinquency, The American Economic Review, 56(1/2): 118137. Fougère, D., Pouget, J. and Kramarz, F. (2009), Youth Unemployment and Crime in France, Journal of the European Economic Association, 7(5): 909–938. Freeman, R.B. (1994), Crime and the Job Market, NBER Working Papers, No. 4910. Friedman, J., Hakim, S. and Spiegel, U. (1989), The Difference between Short and Long Run Effects of Police Outlays on Crime: Policing Deters Crime Initially, but Later They may “Learn by Doing”, American Journal of Economics and Sociology, 48(2): 177-191. Gould, E.D., Weinberg, B.A. and Mustard, D.B. (2002), Crime Rates and Local Labor Market Opportunities in the United States: 1979-1997, The Review of Economics and Statistics, 84(1): 45-61. Grogger, J. (1998), Market Wages and Youth Crime, Journal of Labor Economics, 16(4): 756-791. Hagan, J. (1993), The Social Embeddedness of Crime and Unemployment, Criminology, 31(4): 465-491. Lee, D.Y. and Holoviak, S.J. (2006), Unemployment and crime: an empirical investigation, Applied Economics Letters, 13: 805–810. Lochner, L. (2004), Educatıon, Work, And Crıme: A Human Capıtal Approach, NBER Working Paper Series, No.10478. Lochner, L. and Moretti, E. (2004), The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports, The American Economic Review, 94(1): 155-189. Machin, S. and Meghir, C. (2004), Crime and Economic Incentives, The Journal of Human Research, 39(4): 958-979. Mocan, H.N. and Rees, D.I. (1999), Economic Conditions, Deterrence and Juvenile Crime: Evidence From Micro Data, NBER Working Paper Series, No.7405. Myers, S.L. (1983), Estimating the Economic Model of Crime: Employment versus Punishment Effects, The Quarterly Journal of Economics, 98(1): 157-166. Narayan, P.K. and Smyth, R. (2004), Crime Rates, Male Youth Unemployment and Real Income in Australia: Evidence from Granger Causality Tests, Applied Economics, 36: 2079–2095. Roodman, D. (2006), How to Do xtabond2: an Introduction to “Difference” and “System” GMM in Stata, The Center for Global Development Working Paper Series, No.103. Schmidt, P. and Witte, A.D. (1984), An Economic Analysis of Crime and Justice: Theory, Methods, and Applications, Academic Press., Orlando. Tauchen, H., Witte, A.D. and Griesinger, H. (1994), Criminal Deterrence: Revisiting the Issue with a Birth Cohort, The Review of Economics and Statistics, 76(3): 399-412. Thornberry, T.P. and Christenson, R.L. (1984), Unemployment and Criminal Involvement: An Investigation of Reciprocal Causal Structures, American Sociological Review, 49(3): 398-411. Trumbull, W.N. (1989), Estimations of the Economic Model of Crime Using Aggregate and Individual Level Data, Southern Economic Journal, 56(2): 423-439. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2013, Volume 7, Issue 2, 32-40. Viscusi, W.K. (1986), The Risks and Rewards of Criminal Activity: A Comprehensive Test of Criminal Deterrence, Journal of Labor Economics, 4(3): 317-340. Windmeijer, F. (2005), A Finite Sample Correction for the Variance of Linear Efficient Two-Step GMM Estimators, Journal of Econometrics, 126: 25-51. Witte, A.D. (1980), Estimating the Economic Model of Crime with Individual Data, The Quarterly Journal of Economics, 94(1): 57-84. Witte, A.D. and Tauchen, H. (1994), Work and Crime: An Exploration Using Panel Data, NBER Working Paper Series, No. 4794.

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