A spatial panel data analysis of crime rates in EU

Transcription

1 A spaial panel daa analysis of crime raes in EU by Jørgen T. Lauridsen, Fama Zeren and Ayşe Ari Discussion Papers on Business and Economics No. 2/2013 FURTHER INFORMATION Deparmen of Business and Economics Faculy of Social Sciences Universiy of Souhern Denmark Campusvej 55 DK-5230 Odense M Denmark ISBN Tel.: Fax: lho@sam.sdu.dk hp://

2 A spaial panel daa analysis of crime raes in EU Jørgen T. Lauridsen 1, Fama Zeren 2 and Ayşe Ari 3 1 Corresponding auhor: Universiy of Souhern Denmark, Cenre of Healh Economics Research (COHERE), Campusvej 55, DK-5230 Odense M, Denmark jl@sam.sdu.dk 2 Inonu Universiy, Faculy of Adminisraive and Economy, Deparmen of Economerics, Malaya, Turkey, fama.zeren@inonu.edu.r, İsanbul Universiy, Faculy of Economics, Deparmen of Economics, İsanbul, Turkey, ayseari187@yahoo.com Absrac The sudy invesigaes seleced facors affecing crime raes in he EU-15 counries during he years 2000 o 2007 wih an especial focus on inflaion rae, level of educaion, income and employmen. While hese opics have been invesigaed in former sudies, he presen sudy adds by inroducing spaial panel daa mehods o he case. Regarding he effecs of hese facors, he presen sudy obains resuls comparable o hose from former sudies, whereby he robusness of hese are confirmed. Keywords: deerminans of crime raes, cos-benefi analysis of crime, EU counries, spaial panel daa analysis. JEL classificaion: A120, C500, K4 1

3 1. Inroducion. I is generally acceped ha crime damages socieies psychologically as well as economically. Therefore, various aspecs of crime, including facors deermining crime, maers relaed o punishmen of crime ec., have always had an imporan place in research disciplines like psychology, biology, sociology, law and economics. Scholars from differen disciplines have aimed a exploring he deerminans of crime and hus conribued o he effors exered by auhoriies in order o decrease crime. A large number of sudies have revealed a variey of facors ha affec crime raes, including demographic characerisics, economic and sociological condiions, public regulaion ec. However, given ha hese sudies applies differen mehods and involves daa from differen counries and sources, a general consensus regarding he reasons of crime has no ye been reached. I is well known ha crime inflics imporan exernaliies for he economy due o he coss and he social deerioraion caused by i. As he governmen applies punishmen policies in order o preven crime, money are spend on prisons, cours, judges, prosecuors and police forces (Donohue 2007). These basic or direc coss caused by crime end o increase if he crime rae increases (Tella and Schargrodsky 2004). In addiion, a number of indirec coss are infliced, such as produciviy loss in labour marke, losses in human and social capial sock and coss caused by a decreasing rae of paricipaion in labour marke aciviies (Fajnzylber e al. 2000). Furhermore, a number of coss for proecion agains crime are infliced including securiy guards, lock sysems, alarm sysems, self proecion ec. (Clofeler 1977; Becker 1968). Cohen (2005) divides he coss of crimes for he vicims ino angible and inangible coss. Tangible cos include iems like work and produciviy loss for he vicim, work and produciviy loss for he vicim s family members, loss due o being unable o aend school, healh coss due o any 2

4 injuries, spending made in order o ge over menal disorders due o he shock experienced and loss of propery. Inangible coss are defined as decreasing life qualiy of he vicim due o he inciden experienced. Given is negaive effecs on sociey as well as individuals, prevenion of crime is an imporan maer. To his end, he deermining facors of crime mus necessarily be explored, followed by he necessary precauions as deermined in ligh of hese facors. Given ha many sudies have quesioned he effecs of social, economic and demographic variables on crime raes, a general consensus regarding he ulimae reasons of crime has no been (and, presumably, will never be) obained. The presen sudy reviews exising knowledge regarding facors deermining crime raes. Nex, we conribue wih a replicaing invesigaion based on daa from 15 EU counries for he years 2000 o Our especial focus is on seleced facors (GDP per capia growh rae, inflaion, level of educaion, urbanizaion rae and labour force paricipaion rae). While he effecs of hese facors on crime have been invesigaed in previous sudies, our specific conribuion will be a closer examinaion of he robusness of hese resuls by applying spaial panel mehods. Following a brief presenaion of he economy of crime in Secion 2, Secion 3 will review facors deermining crime, while mehodological maers are briefly oulined in Secion 4 and he daa presened in Secion 5. Finally, he resuls are presened in Secion 6 and rounded off by a brief discussion in Secion The economy of crime. While crime and relaed socieal problems were early menioned by social researchers like Adam Smih and Karl Marx, Becker (1968) was he firs aemp o specify an economic model of 3

5 crime. This early aemp was laer elaboraed on by numerous auhors, including Ehrlich (1973), Schmid and Wie (1984), Wie (1980) and Myers (1983). Becker (1968) considers crime as an economic aciviy which bears high risks and high yields. The sudy argues ha hose who commi crimes base heir decision on a cos- benefi analysis and hen decide o commi crime if he profi is bigger han he cos. Becker defines benefi as he income obained by an illegal ac, and cos is composed of he loss of earnings from alernaive legal work, losses infliced by he risk of geing caugh, and poenial coss caused by imprisonmen and punishmen. Under hese condiions, individuals are assumed o commi a crime if he expeced benefi from he crime is larger han he expeced cos. On he oher hand, if he expeced cos is larger han he expeced benefi, individuals are expeced o urn o legal aciviies and hus change he opporuniy cos o a profi generaed by he legal acs ha hey oherwise had o abandon if hey commied he crime (Wie and Wi 2002; Grogger 1998; Doyle e al. 1999). Thus, he economic model apparenly has a unique approach o crime as compared o models and approaches from criminology, psychology, sociology and oher disciplines. The economic approach perceives crime jus like any oher economic aciviy ha coss ime and effor and yields moneary benefi. This implies ha individuals regard crime and work as subsiues (Wie and Tauchen 1994). In oher words, he decision of commiing a crime is no qualiaively differen from he decision of buying a good. Jus as, ceeris paribus, an increase in he price of a good decreases he demand for i, he cos of crimes migh be increased in order o reduce he crime rae. Moreover, in he economic approach criminal individuals are regarded o be raional and o maximize heir economic prosperiy (Schmid and Wie 1984). However, he economic 4

6 approach relies on an assumpion of full informaion. Typically, he consequences of criminal acs, alike he risk of being arresed and imprisoned, are no (or only vaguely) known in advance. The mechanisms underlying he decision o commi a crime can be explicaed as follows (Warren 1978). Le X denoe he income o be obained by an individual hrough legal acs, and Y he income o be gained hrough a crime. Nex, p denoes he probabiliy of being caugh and senenced, and Z he pecuniary consideraion of he punishmen if he caugh and senenced. The benefi o be obained by legal acs is denoed by U(X), and he benefi obained from crime by U(Y). Given ha he benefi o be obained from crime depends on wheher he individual is caugh or no, he expeced benefi from crime E(U) is obained by muliplying he benefi of crime if no caugh by he probabiliy of no being caugh, and add he benefi of crime if caugh muliplied by he probabiliy of geing caugh. Thus, E(U) = (1-p)U(Y) + pu(y-z). Accordingly, poor individuals wih low incomes (i.e. small X values and consequenly low U(X) values) are supposed o commi crimes wih a higher probabiliy. Moreover, hose who have been imprisoned for a while are srongly expeced o commi oher crimes. This follows readily, as U(X) should be larger han E(U) in order o refrain from crimes. Thus, a prevenion of crimes may be obained by increasing X, p or Z. However, for imprisoned individuals, i is no easy o increase X, given he lack of occupaional and educaional opporuniies in his siuaion. Furhermore, given of he nooriey of imprisoned individuals, i is prey hard for hese o ge a high salary or even a job. Nex, i may be difficul o increase he risk of geing caugh and senenced, as imprisonmen and oher experience gained from former crimes may learn individuals o become more professional criminals in he fuure. Thus, imprisonmen may readily 5

7 decrease p, which in urn drives U(X) o be smaller han E(U), whereby convics will end o commi crimes again. In sum, i is quesionable wheher individuals will refrain from fuure crimes afer punishmen (Warren 1978). The economic approach o crime has several advanages compared o oher disciplines. One imporan advanage is ha he facors deermining crime are assumed o be exernal. This implies ha in case of disagreemen abou he assumpions concerning hese facors, he poin of disagreemen will be exernal and inference of models based on differen assumpions can be analyzed. In many oher social disciplines, he assumpions are inernal, so ha such an approach is no feasible for hem. Anoher imporan advanage is ha he variables of he economic approach are economic indicaors, which are measurable. On he oher hand, a major imporan criicism agains he economic approach is ha i only considers economic facors deermining crime and commonly excludes non-economic facors. Furhermore, he inference suggesed by he economic approach is commonly criicized for being oo specific. Moreover, many social disciplines may be less sensiive owards assumpions han economics. Specifically, he former ake an individual sample of crime and make inferences abou he illegal behaviour of he individual, based on heir behaviour, while he poin of deparure for he economic analysis is he laen decision by he individual wheher or no o commi a crime (Warren 1978). 3. Facors deermining crime. Crime is as old as mankind and i has become a major problem for governmens. Thus, an imporan objecive of auhoriies is o preven illegal aciviies. As a consequence, he facors causing crime have also been vasly invesigaed, as a proper knowledge of hese is necessary in order o preven crime. Wihin he economic model radiion, he major facors affecing crime 6

8 are predominanly considered o be economic, including level of educaion, level of income, income inequaliy, unemploymen, urbanizaion rae, young populaion, female/male populaion rae, labour force paricipaion rae, number of polices, securiy expendiures, crime hisory of individuals ec. Many empirical sudies have considered he effecs of socio-economic and demographic facors on crime raes. A variey of mehodological approaches have been applied, and differen ime periods and counries considered. This may parly explain why a lack of consensus, and even conflicing resuls, are found in he lieraure regarding he effecs of hese facors. One example is gross domesic produc (GDP), which is commonly used as a measure of income and which is commonly expeced o have a negaive effec on crime. In many sudies invesigaing facors affecing crime (e.g. Ehrlich 1973; Kelly 2000; Imrohoroğlu e al. 2006) income appears as a basic explanaory variable. I is obviously expeced ha individuals wih low income presen higher raes of crime involvemen. According o he opporuniy cos consideraion of he economic approach, an increase in income would raise he opporuniy cos of crime involvemen and hus decrease he endency o involve in crime. However, Fajnzylber e al. (2002) evaluaes he maer differenly and asser ha crime raes migh increase despie he increase in per capia income. Their explanaion is ha as he wealh of oher members of sociey grows wih economic developmen, he poenial income from crime would also increase. Furhermore, he welfare level of hose people whose income decreases or remains unchanged despie he general increase in per capia income would decline in relaive erms. In oher words, despie he increase in per capia income he welfare of some ciizens would decline due o he increased inequaliy in he income disribuion. Moreover, widespread echnological innovaions and easier communicaion opporuniies during economic growh periods migh faciliae crime involvemen and lead o an 7

9 increase in crime raes (Fajnzylber e al. 2002). Mera and Jayakumar (1995) asser ha moral values migh decline due o he impac of increasing economic welfare and a maerialis way of life ake over. Cero and Merloni (2000) sudied 22 Argeninean provinces beween 1990 and 1999 aiming a invesigaing facors ha migh deermine crime. In conras o heory, hey revealed ha increasing income was relaed o increasing crime raes. Gumus (2004) focused on facors deermining crime in USA and found ha per capia income, inequaliy, urban populaion, black populaion and unemploymen were significan such facors. Anoher imporan facor deermining crime is inflaion rae. Despie is relevance, however, many sudies of crime do no include i. Among excepions are sudies by Curis (1981), Ralson (1999), Teles (2004) and Tang and Lean (2007) where inflaion was invesigaed as a poenial deerminer of crime. As inflaion weakens purchasing power, individuals may purchase lesser goods wih a given income. This may poenially worsen heir life sandard and hus simulae he individuals o pick crime as a source of exra resources. Thus, as poined ou by Teles (2004), as income affecs preferences concerning crime, and as inflaion decreases he real income, i is clear ha income encourages o crime involvemen. Furhermore, i is known ha low income groups are mos severely influenced by increasing inflaion. Thus, inflaion causes people wih low incomes o be more suscepible o crime, unless heir wages are increased. However, wages canno adjus o inflaion as rapid and easy as oher prices (Seals and Nunley 2007). Anoher sudy ha poins ou he delay in reacion of wages o inflaion is Devine e al., (1998). For siuaions where he real income of unskilled workers decrease due o inflaion, Long and Wie (1981) showed ha illegal aciviies wih higher profi migh poenially be preferred. In exension of his, Devine e al. (1998) noed ha problems of mainaining social conrol may occur, as inflaion poenially erodes he rus in exising 8

10 agreemens among insiuions and individuals. Thus, Curis (1981) observed ha minoriies and he poor are affeced more severely by inflaion han oher members of he sociey, which migh moivae hem for commiing crimes. Ralson (1999) sudied he period 1958 o 1995 in USA and suppored heory by showing a posiive relaionship beween inflaion and crime raes. Tang and Lean (2007), who also sudied USA, found a similar posiive relaionship beween inflaion and crime raes as well as a long erm co-inegraion beween hem. Similarly, Deadman and MacDonald (2002) found in a sudy USA ha in a process where economic growh coninues, a decrease in inflaion is relaed o a decrease in crime raes. Anoher sudy by Seals and Nunley (2007) assered ha inflaion is a significan driver of crime. Specifically, hey found ha inflaion rae and crime rae were correlaed during he 1960s and he 1970s as well as in he 1990s. Tang (2009) sudied Malaysia and found ha inflaion during he period period was a significan driver of crime. In he same sudy, a co-inegraion es revealed a long erm relaionship beween inflaion and crime. Similarly, Gillani e al. (2009), who did a similar analysis for Pakisan, assered ha inflaion and povery eners a long erm relaionship wih crime. Furhermore, crime raes are relaed o urbanizaion. Theoreically, an increase in he urbanizaion rae is supposed o bring along an increase in he crime rae. Given ha in big ciies he risk of geing caugh is low, and hus he benefi o be received hrough he crime is high, such an expecaion was suggesed by Masih and Masih (1996) and Helsley and Srange (1999). Glaeser and Sacerdoe (1999) showed ha he high benefis o be poenially received in big ciies increased he crime raes here by some 25 percen. Moreover, he ouskirs of big ciies may poenially be areas where migrans from he counry side sele. Such migrans are ypically used o he life in an agriculural sociey and herefore experience difficulies in adoping he 9

11 indusrialized life syle of he big ciies. Thus, such ouskirs are he ypically pars of he own where he crime raes are high. Furhermore, in hese pars of he ciy, i is commonly seen ha he living sandard is low and ha he unemploymen rae is high. In sum, individuals living in he ouskirs, being marginalized and experiencing hard living condiions, migh be moivaed for criminal aciviies. Educaional level is ye anoher imporan facor affecing crime. A negaive relaionship beween educaion and crime has been suggesed and may parly be relaed o income, as individuals wih high level of educaion generally have well paid and respecable jobs. This implies ha he opporuniy cos of commiing crime is higher for educaed people. Lochner and Morei (2004) indicaed ha educaed people care may care even more abou heir respecabiliy han abou heir income. Besides, as shown by Fajnzylber e al. (2002), educaion is one of he mos imporan sources of civilizaion. In his conex, Lochner and Morei (2004) underlined ha educaed people ac calmer and more reasonable, hey are more paien, and hey are risk averse. Thus, as educaion increases, he percepion of problems, of facors leading o crime and of crime iself may change. In oher words, moral and social values may improve, whereby he preferences regarding crime may change. Similarly, Usher (1997) assered ha educaional insiuions promoe social values, grow up individuals as hardworking and hones people, and raise heir culural and moral values in favour of he sociey. While radiional sudies of educaion focused on he personal benefis of receiving i, laer economic sudies also focused on socieal oucomes of educaion. Thus, given ha crime causes large social coss, educaion is mos helpful as i resuls in an imporan decrease in hese social coss (Lochner and Morei 2004). Along hese lines, Morei (1999) showed ha workers wih 10

12 high levels of educaion increase he income of he oher workers around hem, which may provide a furher reducion of crime. However, he sudies so far have o some exen reached conflicing resuls regarding he relaionship beween educaion and crime. For example, Lochner (1999), reached significan resuls consisen wih heory while Grogger (1998) and Marselli and Vannini (1997) found insignifican resuls. Wie and Tauchen (1994), on he oher hand, assered ha in order o preven crime he engagemen in legal aciviies like educaion or employmen was far more effecive han he income o be received from hese aciviies. Finally, crime raes are naurally affeced by unemploymen, which in urn is deermined by he (mis)mach beween labour force size and employmen capaciy of he sociey. Specifically, he proporion of inhabians in he age group 16 o 65 years who are able o work, denoed he labour force conribuion rae, should mach he employmen capaciy of sociey. If he laer is large enough, hen he acive populaion is able o obain a saisfying legal earning. However, if he employmen capaciy is oo low, hen some of he unemployed people migh commi crimes for money (Blackmore 2003). Thus, unemploymen is included as a deermining facor for crime. 4. Mehods Given ha panel daa conain he wo complemenary dimensions of cross secion and ime, hey ypically provide more informaion and variabiliy as compared o one-dimensional daa. Thus, analyses carried ou using panel daa ypically offer an increase in he degrees of freedom and a decrease in he poenial collineariy problems among he explanaory variables (Hsiao: 2005). Hence, panel daa provide more effecive esimaes. Panel daa models have a heerogeneous srucure due o unobservable or excluded effecs. A one-way specificaion considering 11

13 heerogeneous individual effecs may be formulaed. Wheher his effec would be sable or random is much debaed in boh mehodological sudies and several applied sudies (Wooldridge 2010). According o he assumpions concerning hese effecs, panel daa models are classified as fixed effec and random effec models. Furhermore, for cases where panel daa conains a locaional componen a spaial dependence migh be presen in he observaions (Elhors 2003). Spaial dependences are commonly expressed hrough a weigh marix which specifies he neighbourhood relaions. Such a marix is commonly assumed o remain consan hrough ime (Anselin e.al 2008). Thus, for a panel daa model wih N regional unis T ime periods, a weigh marix wih NT by NT dimensions ha conains neighbourhood relaions is given by W I T W N (1) where I T is a T dimensional ideniy marix and W N he N by N spaial weigh marix for one year. For he fixed effec models, he individual effec is capured using dummy variables for each region. Spaial fixed effec models are furher classified ino fixed effecs spaial lag model and fixed effecs spaial error model by he ype of spaial dependency hey conain. The fixed effecs spaial lag model (Elhors 2003) reads as y W y X u (2) N where y is an N dimensional vecor of observaions for he dependen variable, X an N by K marix of explanaory variables, he spaial spillover parameer, a K dimensional vecor of regression coefficiens, an N vecor of individual fixed effecs, and u an N dimensional vecor of whie noise residuals. Model (2) is esimaed by maximum likelihood mehods afer 12

14 demeaning y and X. Alernaively, a spaial error varian of he fixed effecs model reads as (Elhors 2003) y x u, wih N u W u (3) where denoes he spaial spillover parameer. Similar o (2), he model (3) is esimaed by maximum likelihood mehods. An alernaive o he fixed effec specificaion is a random effecs model based on he assumpion ha individual effecs are random. Thus, a random effecs spaial lag model (Elhors 2003) is given by y W y x v (4) N where he N dimensional residual vecor v reads as v u wih he definiions oherwise as above. For any wo ime periods and s, he covariance marix of v and v s reads as s ' 2 2 v vs I N I N E if s E v v I oherwise. Again, he spaial ' 2 and s s N effec may be capured alernaively by specifying a random effecs spaial error model (Elhors 2003) reading as y x v (5) wih he residual specificaion v 1 B u, where B I N W, wih definiions of erms oherwise as above. Alike he fixed effecs specificaions, he wo random effecs specificaions. 13

15 5. Daa The presen sudy aims a invesigaing he facors deermining crime according o he models of Becker (1968) and Ehrlich (1973) for he case of 15 European Union counries (denoed EU-15 1 ). Thus, economic and social facors hypohesised o deermine crime for he EU-15 counries beween he years were colleced. The daa came from World Bank's World Developmen Indicaor daabase and Eurosa. Figure 1. Spaial disribuion of crimes recorded by police for Crimes agains propery and violence-based crimes are commonly assumed o be affeced by differen facors. As noified by Scorcu and Cellini (1998), while economic variables are efficien 1 EU-15 covers Denmark, Belgium, Ireland, France, Germany, Ialy, Greece, Unied Kingdom, Sweden, Spain, Porugal, Neherlands, Luxembourg, Slovakia and Ausria. 14

16 and significan in case of crimes agains propery hey are no expeced o be significan in violence crimes. However, due o shorness of daa he analysis variable is aken as oal crime rae or crime rae per capia. The presen sudy applies a log ransform of he crime rae. As a proxy variable for crime, he number of crimes recorded by police is applied. Figure 1 shows he disribuion of his variable across counries for he 2007 daa. I is eviden ha spaial spillover is presen. Turning o he explanaory variables, he growh rae of per capia gross domesic produc (GDPG) is applied as an indicaor of economic growh and welfare, while demographic daa is made up by he rae of populaion aged o he oal populaion (POP). As indicaor of educaion, he sudy applies logarihm of primary educaion (PEDU), while urbanisaion is measured by urban populaion growh (URP), and he consumer price index (2005 prices) denoed by INF serves as indicaor of inflaion. Table 1 provides a descripion of he daa. Table 1. Descripion of variables for he sudy Variable Definiion Average Sd. dev. Expeced sign CRIME Logarihm of number of crimes recorded by polices (Dep. variable) GDPG The growh rae of per capia gross domesic produc POP The rae of he populaion aged o he oal populaion PEDU The logarihm of primary educaion URP Urban populaion growh INF Consumer price index wih 2005 fixed prices Definiion of neighbourhood will be obained by applying a Criical Value Neighbourhood (CVN) crierion defined by disances. Specifically, if he disance d ij beween wo regions i and 15

17 j is beween a cerain criical hreshold value d *, hen he wo regions are assumed o be neighbours, and he corresponding elemen w ij of he weigh marix W N is se o one, while i is se o zero oherwise. For he presen sudy, he hreshold value d * is se o he minimum disance required o ensure ha each locaion has a leas one neighbour. 6. Resuls The esimaion resuls peraining o spaial panel daa model are given in Table 2. According o he Hausman es, which is used for deciding wheher he fixed or he random effec spaial lag model (1) versus (2) should be used, he fixed effecs model is convenien for he curren siuaion. According o he similar Hausman saisics for he fixed or random effec spaial error models (3) versus (4), he fixed effec spaial lag model urns ou o be superior. For he fixed effec specificaions (1) versus (3), he LM saisics may be applied as indicaion of which ype of spaial dependence should apply, I urns ou ha he spaial lag model is he mos convenien for he presen daa since * LM is more significan han * LM for he raw as well as he robus versions of he ess. Thus, according o he specificaion ess, he mos convenien specificaion appears o be he spaial lag fixed effec model (1). Generally, he signs of all he coefficiens of he explanaory variables seem o comply wih he heoreical expecaions. Thus, he impac of educaion on crime seems o be negaive hroughou, so ha i migh be inferred ha uneducaed individuals have a significan poenial for commiing crime. Hence, i migh be assered ha crime raes migh be reduced placing more emphasis on educaing individuals. This suppors he claim of Lochner and Morei (2004), where 16

18 i was furher shown ha increased educaion expendiures are less cosly compared o rehabiliaion of individuals involved in crime. Table 2: The Esimaion Resuls of Spaial Panel Models Spaial Lag Models Esimaion (1) (2) Spaial fixed Spaial random Variable effecs model effec model Inercep * (2.189) GDPG * * (-2.898) (-2.440) POP * * (3.267) (3.296) PEDU * (-2.035) (-0.361) URP * ** (1.975) (1.734) INF * * (2.041) (2.589) * * (3.511) (2.711) Spaial Error Models Esimaion (3) (4) Spaial fixed Spaial random effecs model effec model * (4.114) * * (-3.094) (-2.609) * * (2.343) (2.696) ** (-1.855) ( ** (1.895) * (3.802) * LM Tess LM [0.000] Robus LM * Tess LM [0.056] LR Tes Hausman Tess [0.000] [0.000] [0.000] (3.572] LM [0.001] * LM [0.453] [0.000] (1.577) * (3.829) * (3.255) [0.000] [0.000] Values in parenheses are -saisics and values in brackes parenheses are p-values. Also *, indicaes parameers are significan a 5% and ** indicaes parameers are significan a 10%. Furhermore, crime raes increase wih increasing inflaion raes. However, while he coefficien is significan and in accordance wih heory, a relaively low effec was deermined. Turning o 17

19 per capia GDP, he sign of he effec is significanly negaive hus supporing he heoreical claim ha low economic welfare would increase crime raes. The coefficiens peraining o urbanizaion rae and populaion aged are significanly posiive as heoreically expeced. Thus, i can be assered ha growing urban populaion is sysemaically relaed o problems leading o increased criminal aciviy, and ha an increase in he employmen capaciy may also be lised among facors ha poenially increase crime raes. 7. Conclusion Crime leads o fear and anxiey in sociey and disurbs social order and harmony. Therefore, auhoriies aim a eliminaing crime which adversely effecs sociey, boh physically and economically. In order o eliminae crime he reasons of crime mus be well deermined. The presen sudy conribues by examining significance of facors commonly acceped o affec crime raes in EU-15 counries by using spaial panel daa mehods. A spaial correlaion of crime raes among he EU-15 counries is shown o be in play. I is shown ha economic capaciy and educaion affec crime negaively while facors such as inflaion rae, employmen capaciy and urban populaion have posiive effecs. Regarding he effecs of hese facors, he presen sudy obains resuls comparable o hose from former sudies, whereby he robusness of hese are confirmed. References Anselin L, Gallo JL, Jaye H (2008) Spaial Panel Economerics. In: Máyás L, Sevesre P (eds) The Economerics of Panel Daa. Springer-Verlag, Berlin Heidelberg, pp Becker GS (1968) Crime and punishmen: an economic approach. J Poli Econ 76:

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