REGIONAL DEVELOPMENT AND CRIMINALITY RATE IN ROMANIA: INSIGHTS FROM A SPATIAL ANALYSIS

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1 REGIONAL DEVELOPMENT AND CRIMINALITY RATE IN ROMANIA: INSIGHTS FROM A SPATIAL ANALYSIS Zz GOSCHIN 1 Abstract: Although many recent studes have approached the topc of crmnalty, the regonal dmenson of the phenomenon s stll under research. Ths paper employs a varety of statstcal methods, from descrptve statstcs to convergence and spatal econometrcs, n an attempt to explore crmnalty rate n Romana, at county level, over The analyss revealed that developed countes tend to have hgher crmnalty rates, wth Ilfov County and Bucharest Muncpalty frequently on top postons, and the county ranngs are relatvely stable n the short run. Aganst expectatons, the regresson models that have been estmated could not provde enough support for the GDP per capta (proxy for development level) as a statstcally sgnfcant factor of nfluence on crmnalty rate n all years, but the explanatory varable crmnalty rate n prevous year proved to be postve and hghly sgnfcant n all models, ndcatng the relatve nerta of ths phenomenon. Keywords: crmnalty rate; spatal model; county; Romana JEL Classfcaton: R10; R12; R58 1. Introducton Locaton-based analyses of crmnal offences are hghly popular snce the development of sophstcated spatal analyss tools whch are able to process geographcally coded data. Such analyses help shed lght on a wde range of socal, economc or 1 Bucharest Unversty of Economc Studes, Romana, Insttute of Natonal Economy, Bucharest, Romana, e-mal: zz.goschn@cse.ase.ro

2 36 Zz GOSCHIN demographc factors that encourage/dscourage crmes n a certan area (Sherman et al, 1989; Land et al., 1990; Levne, 1999; Messner et al., 1999, etc.). Despte a bg ncrease n the general crmnalty rate snce the collapse of the socalsm regme, Romana s stll consdered a safe destnaton, havng a low overall crme ratng (OSAC, 2015). The number of crmnal offences largely vares throughout Romana, dependng on demographc and soco-economc characterstcs, bg ctes and densely populated areas beng on the top of crme statstcs. Offcal statstcs pont to relevant regonal dfferences n the overall crmnalty fgures, the hot spots of crme changng n tme. Fgure 1 llustrates the terrtoral dstrbuton of crme rates, revealng sgnfcant nequaltes between Romanan countes, amd the overall rsng trend n total crmnal offenses per nhabtants durng the perod 1995 to Fgure 1: Spatal dstrbuton of the crmnalty rates n Romana, n 1995 and 2014 (1995) (2014) Source: author s processng usng GeoDa software In ths context, we am to explore the terrtoral varaton of total nfractons based on offcal data on crmnalty rate by county (NUTS 3 statstcs). Crmnalty rate s measured as the number of defntve convcted people per 100,000 nhabtants. Offcal statstcs on crme covers varous offenses recorded by polce, such as (attempted) ntentonal homcde, assault, dnappng, sexual volence, robbery, burglary, motor vehcle theft, and other unlawful acts. Ths data does not cover a recordng of all crmes, as certan crmes reman unreported. Fluctuatons n the crme levels may be nduced by methodologcal changes or mprovements n crme reportng, such as the one n 15 June 2004, when the 112 emergency phone call servce came nto operaton n Romana. Despte sgnfcant regonal varablty n crmnal offences, the ssue of crme convergence has never been approached n the Romanan economc research. Real convergence s a topc of hgh nterest n regonal scence, wth most emprcal studes

3 Regonal development and crmnalty rate n Romana: nsghts from a spatal analyss 37 addressng ncome and productvty convergence, but rarely crme-related topcs. In ths paper we contrbute to ths strand of lterature by addressng the long-run trends n crme varablty and nequaltes from a terrtoral perspectve and by assessng the convergence process wth specfc spatal analyss methods. Our research targets the nterval 1990 to 2014, but, due to data lmtatons, the beta convergence model covers the perod , further dvded n three relevant sub-perods. The rest of our paper s structured as follows. Secton 2 descrbes the statstcal methods employed n the emprcal research, focusng on the beta convergence models n a spatal regresson framewor. Secton 3 presents and dscusses the results both from statstc and soco-economc perspectves and secton 4 concludes by summarsng the man fndngs. 2. Methods, varables and data In ths emprcal research on crmnalty rate, we combne the tradtonal convergence analyss (sgma and beta convergence) wth spatal regresson models that account for lely spatal autocorrelaton ssues. Startng wth the semnal paper of Barro and Sala--Martn (1995) the sgma and beta convergence methods have been extensvely used n regonal studes n order to asses the scale and trend of terrtoral nequaltes. The sgma convergence ndcator measures the overall terrtoral varaton: n 1 2 ( CR CR) n CR (1) where CR s the crmnalty rate by county. Dmnshng values of ths ndcator, n a certan perod of tme, ndcate convergence. When the values of the ndcator are growng n tme, t means dvergence. The second method s beta convergence, based on the estmatons of a regresson model that explans the growth rate of a varable n relaton to ts ntal regonal levels. For nstance, n the case of crmnalty rate, beta convergence occurs f the number of crmes growths faster n the regons havng lower crmnalty levels at the begnnng of the perod. The beta convergence model mght be appled n two forms: absolute and condtonal (Galor, 1996). In ths paper we prefer to estmate condtonal beta convergence models,

4 38 Zz GOSCHIN as they allow us to nclude addtonal regressors that reflect the dstnctve local characterstcs. We are gong to estmate both classc and spatal beta convergence models. Our analyss starts wth a classc OLS model of convergence: 1 CR ln( T CR 0T, 0, ) a b lncr _ ntal c lnx (2) Where: 1 CR0T, ln( ) s the annual average growth n crmnalty rate n county, T CR 0, CR _ ntal represents the crmnalty rate n county at the begnnng of the perod, X are the addtonal explanatory varables (see Table 1) and stands for the error term. We wll further compute the Moran s I statstc (Anseln and Rey, 1991) and apply the permutatons test to asses f there s spatal dependence n the countes crmnalty rates. Snce spatal dependence (f present) negatvely affects the regresson estmatons, we need to replace the classc model wth a spatal one (Anseln, 2005; LeSage and Pace, 2009). We wll frstly estmate the spatal lag specfcaton: 1 CR ln( T CR 0T, 0, ) a b lncr _ ntal, c lnx,0, w CR 0,, (3) Where: w CR0,, s the spatal lag of the dependent varable and w are the spatal weghts that descrbe the regonal structure of the country. The second spatal specfcaton to be tested n our paper s the spatal error model: 1 CR0 ln( T CR T, 0, ) a b lncr _ ntal c lnx ( w v ), (4) where w represent the spatally autoregressve errors and v the new uncorrelated errors of the spatal model.

5 Regonal development and crmnalty rate n Romana: nsghts from a spatal analyss 39 The fnal choce of the best model for our data s based on the value of Lagrange multpler test for both spatal error and spatal lag. In our search for relable regonal predctors of crmnalty, we selected for the condtonal beta convergence model the most relevant varables, as ndcated by the nternatonal lterature, but wthn the lmts of offcal statstcs currently avalable (Table 1). Table 1. The varables Varable name Descrpton Data source CR_growth Annual average growth rate of crmnalty rate over the perod of nterest. Natonal Insttute of Statstcs and own computatons CR_ntal Crmnalty rate (total number of crmnal offences per 100,000 nhabtants) at the begnnng of the Natonal Insttute of Statstcs and own computatons perod of nterest. GDP/cap Gross Domestc Product per nhabtant (Euro) Eurostat database FDI/cap The foregn drect nvestments stoc per capta (Euro) The Natonal Trade Regster Offce and own computatons Unempl Unemployment rate (%) Natonal Insttute of Statstcs Densty Populaton densty (nhabtants per square m) Natonal Insttute of Statstcs Dvorce The dvorce rate per 1000 persons Natonal Insttute of Statstcs Educaton The share of tertary educated per 1000 Natonal Insttute of Statstcs nhabtants and own computatons The nternatonal lterature on crmnalty ponts to economc envronment, demographcs and law enforcement effectveness as the most lely factors of nfluence (Blau, and Blau, 1982; Reman, 2001; Harres, 2006). Romanan crmnalty also seems to be the larger n the developed regons, usng GDP per nhabtant as proxy (Goschn, 2016). The dvorce rate s largely consdered n the lterature as a sgnfcant predctor of crmnalty rate n a regon, beng extremely relevant especally for the level of adolescent delnquency (e.g. Burt et al., 2008). Crmnalty naturally rses wth populaton densty, as frequently documented n many emprcal studes (e.g. Harres, 1995 and 2006; L and Ranwater, 2000). The data for our analyss came from several sources: the Natonal Insttute of Statstcs, Eurostat database, The Natonal Trade Regster Offce and own computatons and covers the perod 1990 to 2014.

6 40 Zz GOSCHIN 3. Results and dscusson The terrtoral dstrbuton of crme rates changes n tme, ndcatng sgnfcant dfferences between Romanan countes (Fgure 1). At the same tme, there are some concentratons of countes wth hgh or low crmnalty rates, or a combnaton of these (hgh crme locatons surrounded by low crme locatons or the opposte, a low-hgh mxture) as revealed by the maps dsplayed n Fgure 2. Fgure 2: Spatal clusters of low/hgh crmnalty rates, 1995 and 2014 (1995) Source: author s processng n GeoDa. (2014) Gven the sgnfcant terrtoral varablty n crmnalty rate n Romana and the general upwards trend n total number of crmes, we tested both sgma and beta convergence processes, to nvestgate a potental declne n crme nequaltes among countes. The computatons based on relaton (1) ndcated a sgma convergence long-run trend n the crmnal actvty over the perod (Fgure 3). Ths trend was stronger at the begnnng of the perod, then the sgma ndcator started to fluctuate (convergence alternatng wth dvergence) snce 1997 and now seems to level.

7 Regonal development and crmnalty rate n Romana: nsghts from a spatal analyss 41 Fgure 3. Sgma convergence n crme Source: author s processng Although the terrtoral varaton n crmnalty levels s now smaller, the total number of crmes s bgger. Ths means that the crme nequaltes among Romanan countes declned n the context of generalzed hgher crmnalty rates. We further tested the beta convergence hypothess, usng the regresson specfcatons (2), (3) and (4). The results dsplayed n Table 2 show a sgnfcant beta convergence process for the overall perod to , as well as for the three sub-perods that we analysed separately. All coeffcents on ntal crmnalty rates (CR_ntal) are negatve and hghly sgnfcant, ndcatng that the average growth of crmnalty rate has been stronger n the countes havng lower ntal crmes. The consequence s a steady declne n crmnalty nequaltes among Romanan countes, on the bacground of a step overall ncrease n total number of crmes. As regards the factors that stmulated the overall rse n crmnalty, the regonal development level (proxed by GDP per capta) and the populaton densty are hghly sgnfcant for the entre perod nvestgated: Ths s not a surprse, gven the wealth of research on crme that ponted to smlar factors (e.g. Blau and Blau,1982; Reman, 2001). Dependng on the perod nvestgated, other sgnfcant factors of nfluence are the unemployment and dvorce rates, whch are postvely lned to the regonal growth n crmnalty rates, whle FDIs and educaton have the opposte effect. Populaton densty seems to be a good predctor of regonal crmnalty rates, gven that t s sgnfcant n all nvestgated ntervals, except for the transton perod Hgher populaton densty seems to stmulate crmnal behavor by offerng more opportuntes, as documented n many emprcal studes (e.g. Harres, 1995 and 2006; L and Ranwater, 2000). The explanatory varables tested n the condtonal beta

8 42 Zz GOSCHIN convergence model seem to have ther effects lmted only to a certan nterval (Table 2). The GDP per capta varable represents a specal case, snce t was hghly sgnfcant over the entre perod , but surprsngly not on the sub-ntervals. Table 2. The results for the beta convergence models (dependent varable annual growth of crmnalty rate) Spatal error model** Spatal error model** Varables Coeffcent Prob Coeffcent Prob CONSTANT lncr_ntal lngdp/cap lnunempl lndensty lndvorce lneducaton LAMBDA Statstcs Value Prob Value Prob R-squared Log lelhood Breusch-Pagan test Lelhood Rato Test (spatal dependence) Spatal error model** Classc model* Varables Coeffcent Prob Coeffcent Prob CONSTANT lncr/cap ntal lnfdi/cap lndensty LAMBDA Statstcs Value Prob Value Prob R-squared Log lelhood F-statstc Breusch-Pagan test Koener-Bassett test Lelhood Rato Test (spatal dependence) *OLS estmaton ** Maxmum lelhood estmaton

9 Regonal development and crmnalty rate n Romana: nsghts from a spatal analyss 43 The Lagrange Multpler tests ndcated that the spatal models are more approprate for our data than classc regresson, except for the perod (Table 2). Ths outcome confrms the fndngs of many prevous emprcal studes on crmnalty that hghlghted the relevance of locaton and the need to use approprate tools of spatal analyss (e.g. Land et al., 1990; Levne, 1999; Messner et al., 1999). 4. Conclusons The regonal convergence n crmnalty rates n Romana has been emprcally confrmed n ths paper, based on sgma and beta tradtonal methods. Moreover, the condtonal beta convergence model was estmated both n classc and n spatal specfcatons, accountng for the spatal autocorrelaton that exsts n the terrtoral levels of crmnalty rate by explctly ncludng t n the regresson models. The hypothess of beta convergence holds for the perod , as well as for three sub-perods ncluded n our analyss, whle sgma convergence has been revealed for the nterval 1990 to Allowng for addtonal factors of nfluence on the regonal convergence process, n the framewor of the condtonal beta convergence model, we found that economc development, unemployment rate, populaton densty and dvorce rates are postvely lned to regonal growth n crmnalty rates, whle FDIs and educaton have the opposte effect. These sgnfcant factors of nfluence hghlghted by our research on regonal crmnalty n Romana are n lne wth the nternatonal manstream lterature. Snce the statstc tests ndcated that the spatal models are more approprate for crme data than classc OLS regresson, we emphasze the relevance of locaton n ths area of research and the need to use specfc tools of spatal analyss n studes on regonal crmnalty. Further research should confrm the robustness of these results and deepen the analyss by examnng the dstrbuton of dfferent types of crmes. References Anseln, L. (2005), Explorng Spatal Data wth GeoDaTM : A Worboo, Spatal Analyss Laboratory Department of Geography Unversty of Illnos, Urbana, Anseln, L. and Rey, S. (1991) Propertes of Tests for Spatal Dependence n Lnear Regresson Models, Geographcal Analyss, 23, pp Barro, R.J. and Sala--Martn, X. (1995) Economc Growth, New Yor: McGraw-Hll. Barro, R.J. and Sala--Martn, X. (2004) Economc growth, 2nd edton., MIT, Cambrdge Blau, J., & Blau, P. (1982) The cost of nequalty: Metropoltan structure and volent crme, Amercan Socologcal Revew, 47 (1),

10 44 Zz GOSCHIN Burt, S. A., Ashlee R. Barnes, Matt McGue, Wllam G. Iacono (2008) Parental Dvorce and Adolescent Delnquency: Rulng out the Impact of Common Genes, Developmental Psychology, 44(6): Galor, O. (1996) Convergence? Inferences from Theoretcal Models, Economc Journal, Royal Economc Socety, vol. 106(437), pp GeoDa (2014), The GeoDa Center for Geospatal Analyss and Computaton, edu/about Goschn, Z. (2016), Mappng global and economc crme n Romana. Regonal trends and patterns, Romanan Journal of Economcs, vol. 42 (2) (forthcomng). Harres K., (2006) Property Crmes and Volence n Unted States: An Analyss of the nfluence of Populaton densty, Internatonal Journal of Crmnal Justce Scences, Vol 1 Issue 2 Harres, K., (1995). The ecology of homcde and assault: Baltmore Cty and County, , Studes n Crme and Crme Preventon 4, Land, K., P. McCall, and L. Cohen Structural covarates of homcde rates: Are there nvarances across tme and socal space?, Amercan Journal of Socology, 95: LeSage, J.P. (1999) The Theory and Practce of Spatal Econometrcs. Department of Economcs, Unversty of Toledo. LeSage, J.P., Pace R.K. (2009) Introducton to Spatal Econometrcs, Boca Raton, CRC Press. Levne, N. (1999) CrmeStat: A spatal statstcs program for the analyss of crme ncdent locatons.washngton, D.C.: U.S. Department of Justce, Natonal Insttute of Justce. L, J. and Ranwater, J. (2000) The real pcture of land-use, densty, and crme: A GIS applcaton. Avalable at: Messner, S., L. Anseln, R. Baller, D. Hawns, G. Deane, Tolnay, S. (1999) The spatal patternng of county homcde rates: An applcaton of exploratory spatal data analyss, Journal of Quanttatve Crmnology, 15 (4): Natonal Insttute of Statstcs, Database TEMPO - tme seres OSAC (The Overseas Securty Advsory Councl), U.S. Department of State. Romana 2015 Crme and Safety Report, avalable at: Reman, J. (2001) The rch get rcher and the poor get prson: Ideology, class, and crmnal ustce. Boston: Allyn and Bacon. Sherman, L.W., Gartn, P.R., Buerger, M.E. (1989) Hot spots of predatory crme: Routne actvtes and the crmnology of place, Crmnology, 27:27 55.