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Suç-Gelir Dağılımı İlişkisinde Mekansallığın Etkisi: Türkiye’de Düzey 2 Bölgeleri İçin Bir Analiz

Yıl 2020, Cilt: 11 Sayı: 3, 699 - 710, 25.10.2020

Öz

Suç, tüm sosyal bilimcilerin olduğu gibi iktisatçıların da oldukça ilgisini çeken bir konudur. Suç ile pek çok makroekonomik değişken arasında farklı düzeylerde ilişkiler bulunmaktadır. Bu değişkenlerden biri de toplumda yaratılan gelirin birimler arasında adil bir şekilde dağıtılması durumunu ifade eden gelir dağılımıdır. Toplumlarda suç olgusunun beslenmesinde gelir dağılımındaki bozulmaların etkili olduğu sonucuna ulaşan birtakım çalışmalar mevcuttur. Bu çalışmada, literatürden farklı olarak suç ve gelir dağılımı arasındaki ilişkinin mekansallık içerip içermediği yani bölgelerin sınır komşuluklarının suç-gelir dağılımı ilişkisinde belirleyici bir özellik taşıyıp taşımadığı araştırılmaktadır. Bu amaçla Türkiye İstatistik Kurumu (TUİK) bölgesel veri tabanından elde edilen 2016 yılına ait veriler kullanılarak Türkiye’de gelir dağılımı eşitsizliği ile mala karşı işlenen suçlar arasındaki ilişki yatay kesit verilerle İBBS 2 (İstatistiki Bölge Birimleri Sınıflandırması) düzey bölgeleri için mekansal ekonometrik yöntemler kullanılarak incelenmiştir. Mekansal belirleme testlerinin sonuçları, Mekansal Hata Modelinin en uygun model olduğunu göstermektedir. Çalışmanın bulguları, Türkiye’de İBBS 2 düzey bölgeleri için suç ile gelir dağılımı arasında pozitif ve anlamlı bir ilişki olduğunu gösterirken mekansallık etkisinin de bulunduğuna işaret etmektedir. Sonuç olarak, Türkiye’de 26 İBBS 2 düzey bölgesinde suç-gelir dağılımı ilişkisinde bölgelerin sınır komşuluklarının etkili olduğunu söylemek mümkündür. Bu durum gerek gelir dağılımı gerekse suçu önlemeye yönelik politikalarda bölgelerin komşuluk ilişkilerinin de dikkate alınması gerektiğine işaret etmektedir.

Kaynakça

  • Aaltonen M., Kivivuori J., Martikainen P. (2011). Social determinants of crime in a welfare state: do they still matter?, Acta Sociologia, 54(2), 161-181.
  • Allen R. (1996). Socioeconomic conditions and property crime: a comprehensive review and test of the proffesional literature. American Journal of Economics and Sociology, 55(3), 293-308.
  • Anselin L. (1988). Spatial Econometrics: methods and models. Folmer, H., Regional Economic Policy. 1986. ISBN 90-247-3308-1.
  • Anselin L. (1988b). Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity, Geographical Analysis, 20,1-17
  • Anselin L., ve Rey S. (1991). Properties of tests for spatial dependence in linear regression models, Geographical Analysis, 23(2), 112-131.
  • Anselin L., ve Hudak S. (1992). Spatial Econometrics in practice: A review of software options, Regional Science and Urban Economics, 22, 509-536
  • Anselin L., & Baltagi B.H. (Eds). (2001). Spatial Econometrics A companion to theoretical econometrics. Blackwell Publishing, 311-330.
  • Anselin L. (2006). Spatial Econometrics, (Der. Mills,Terence C., Kerry Patterson), Palgrave Handbook of Econometrics Vol. 1 Econometric Theory, New York: Palgrave Macmillan. 901-969.
  • Baharom A., ve Habibullah M.S. (2009). Income, unemployment and crime, panel data analysis on selected european countries, 9th Global Conference on Business&Economics
  • Becker G.S. (1968). Crime and punishment, an economic approach, The Journal of Political Economy, 76(2), 169-217.
  • Blau J., ve Blau P. (1982). The cost of inequality, metropolitan structure and violent crime, American Sociological Review, 47, 114-129.
  • Bourguignon F., Nunez J., Sanchez F. (2003). What part of the income distribution matters for explaining property crime? The case of colombia. Documento, CEDE 2003-07, ISSN 1657-7191.
  • Burridge, P. (1980). On the Cliff-Ord test for spatial correlation, Journal of the Royal Statistical Society Series B 42(1), 107-108.
  • Brush J. (2007). Does income inequality lead to more crime? a comparison of cross-sectional and time series analysis of united states countries, Economics Letters, 96, 264-268.
  • Cheong S. ve Wu Y. (2013). Inequality and crime rates in china, University of Western Australia, Economic Discussion/Working Papers, 13.11.
  • Choe J. (2008). Income inequality and crime in the united states, Economics Letters, 101, 31–33.
  • Cliff A., ve Ord J. (1972). Testing for spatial autocorrelation among regression residuals. Geographical Analysis, 4, 267-84.
  • Cliff A., ve Ord J. (1973). Spatial Autocorrelation. London, Pion.
  • Danziger S., ve Wheeler D. (1975). The economics of crime: punishment or income redistribution. Review of Social Economy, 33(2), 113-131.
  • Dursun, H. (1997). Suçun ekonomik modelleri. DPT, iktisadi sektörler ve koordinasyon genel müdürlüğü, hukuki tedbirler ve kurumsal düzenlemeler dairesi başkanlığı.
  • Ehrlich I. (1973). Participation in illegitimate activities: a theoretical and emprical investigation, Journal of Political Economy, 81(3), 521-565.
  • Fajnzylber P., Lederman D., Loayza N. (2002a). Inequality and violent crime, Journal of Law and Economics, 45(1), 1-40.
  • Fajnzylber P., Lederman D., Loayza N. (2002b). What causes violent crime, European Economic Review, 46(7), 1323-1357.
  • Fleisher B.M. (1966). The effect of income on delinquency, The American Economic Review, 56, (1/2), 118-137.
  • Freeman R.B. (1982). Crime and the labor market, Working Paper, 1031, National Bureu of Economic Research.
  • İmrohoroğlu A., Merlo A., Rupert P. (2004). What accounts for the decline in crime, International Economic Review, 45(3), 707-729.
  • İzaadi N., ve Piraee K. (2012). Income inequality and property crime: evidence from Iran, World Applied Sciences Journal, 19 (2), 281-286.
  • Kelly M. (2000). Inequality and crime, Review of Economics and Statistics, 82(4), 530–539.
  • Keshavarz G.H., ve Markazi H.M. (2011). The socioeconomic and demographic determinants of crime in Iran (a regional panel study), European Journal of Law and Economics, 32(1), 99–114.
  • LeSage J. (1998). Saptial Econometrics, Deparment of Economics, University of Toledo December [Available online at: https://www.spatial-econometrics.com/html/wbook.pdf], Retrieved on November 15, 2019.
  • LeSage J., ve Pace R.K. (2009). Introduction to Spatial Econometrics , Florida, Chapman and Hall.
  • Luiz J.M. (2001). Temporal association, the dynamics of crime and other economic determinants: a time series econometric model of South Africa, Social Indicators Research, 53, 33-61.
  • Merton R.K. (1938). Social structure and anomie, American Sociological Review, 54, 597 - 611.
  • Moran P. (1950a). Notes on continuous stochastic phenomena, Biometrika 37, 17-23.
  • Moran P. (1950b). A test for the serial independence of residuals, Biometrika 37, 178-181.
  • Neumayer E. (2005). Inequality and violent crime: evidence from data on robbery and violent theft, Journal of Peace Research, 42(1), 101-112.
  • Nilsson A. (2004). Income inequality and crime: the case of Sweden, ınstitute for labour market policy evaluation, Working Paper, 2004(6).
  • Oliver A. (2002). The economics of crime: an analysis of crime rates in America, The Park Place Economist, 10, 30-35.
  • Shaw C.R., ve Mckay H.D. (1942). Juvenile delinquency and urban areas: a study of rates of delinquencies in relation to differential characteristics of local communities in American cities, Chicago, University of Chicago Press.
  • Thorbecke E., ve Charumilind C. (2002). Economic inequality and its socioeconomic impact, World Development, 30(9), 1477-1495.
  • Warren E.H. (1978). The economic approach to crime, Canadian Journal of Criminology, 20(4).
  • Yıldız R., Öcal O., Yıldırım E. (2011). Suçun sosyoekonomik belirleyicileri: Kayseri üzerine bir uygulama, Erciyes Üniversitesi İİBF Dergisi, 36, 15-31.

The Effect of Spatiality on Crime-Income Distribution Relationship: An Analysis of NUTS 2 Level Regions in Turkey

Yıl 2020, Cilt: 11 Sayı: 3, 699 - 710, 25.10.2020

Öz

Crime is a subject that is of great interest to economists as well as all social scientists. There are different levels of relationship between crime and many macroeconomic variables. One of these variables is the income distribution, which implies that the income generated in the society is distributed fairly among the agents. There are a number of studies which conclude that income distribution is effective in feeding the crime phenomenon in societies. In this study, unlike the literature, it is investigated whether the relationship between crime and income distribution includes spatiality, that is, whether the border neighborhoods of the regions carry a decisive feature in the relationship between crime and income distribution. For this purpose, the relationship between income inequality and crimes against property was studied using spatial econometric methods for NUTS 2 level regions in Turkey. The data is obtained from Turkish Statistical Institute (TurkStat) local database for 2016. The results of the spatial determination tests show that the Spatial Error Model is the most appropriate model. The findings of the study illustrates that for NUTS 2 level regions in Turkey there are positive and significant relationship between income distribution and crime, and also suggests a spatiality effect. As a result, for the 26 NUTS 2 level regions in Turkey, it is possible to say that border neighborhood is effective in crime-income distribution relationship. This suggests that the neighborhood relations should be taken into consideration in both income distribution and crime prevention policies.

Kaynakça

  • Aaltonen M., Kivivuori J., Martikainen P. (2011). Social determinants of crime in a welfare state: do they still matter?, Acta Sociologia, 54(2), 161-181.
  • Allen R. (1996). Socioeconomic conditions and property crime: a comprehensive review and test of the proffesional literature. American Journal of Economics and Sociology, 55(3), 293-308.
  • Anselin L. (1988). Spatial Econometrics: methods and models. Folmer, H., Regional Economic Policy. 1986. ISBN 90-247-3308-1.
  • Anselin L. (1988b). Lagrange multiplier test diagnostics for spatial dependence and spatial heterogeneity, Geographical Analysis, 20,1-17
  • Anselin L., ve Rey S. (1991). Properties of tests for spatial dependence in linear regression models, Geographical Analysis, 23(2), 112-131.
  • Anselin L., ve Hudak S. (1992). Spatial Econometrics in practice: A review of software options, Regional Science and Urban Economics, 22, 509-536
  • Anselin L., & Baltagi B.H. (Eds). (2001). Spatial Econometrics A companion to theoretical econometrics. Blackwell Publishing, 311-330.
  • Anselin L. (2006). Spatial Econometrics, (Der. Mills,Terence C., Kerry Patterson), Palgrave Handbook of Econometrics Vol. 1 Econometric Theory, New York: Palgrave Macmillan. 901-969.
  • Baharom A., ve Habibullah M.S. (2009). Income, unemployment and crime, panel data analysis on selected european countries, 9th Global Conference on Business&Economics
  • Becker G.S. (1968). Crime and punishment, an economic approach, The Journal of Political Economy, 76(2), 169-217.
  • Blau J., ve Blau P. (1982). The cost of inequality, metropolitan structure and violent crime, American Sociological Review, 47, 114-129.
  • Bourguignon F., Nunez J., Sanchez F. (2003). What part of the income distribution matters for explaining property crime? The case of colombia. Documento, CEDE 2003-07, ISSN 1657-7191.
  • Burridge, P. (1980). On the Cliff-Ord test for spatial correlation, Journal of the Royal Statistical Society Series B 42(1), 107-108.
  • Brush J. (2007). Does income inequality lead to more crime? a comparison of cross-sectional and time series analysis of united states countries, Economics Letters, 96, 264-268.
  • Cheong S. ve Wu Y. (2013). Inequality and crime rates in china, University of Western Australia, Economic Discussion/Working Papers, 13.11.
  • Choe J. (2008). Income inequality and crime in the united states, Economics Letters, 101, 31–33.
  • Cliff A., ve Ord J. (1972). Testing for spatial autocorrelation among regression residuals. Geographical Analysis, 4, 267-84.
  • Cliff A., ve Ord J. (1973). Spatial Autocorrelation. London, Pion.
  • Danziger S., ve Wheeler D. (1975). The economics of crime: punishment or income redistribution. Review of Social Economy, 33(2), 113-131.
  • Dursun, H. (1997). Suçun ekonomik modelleri. DPT, iktisadi sektörler ve koordinasyon genel müdürlüğü, hukuki tedbirler ve kurumsal düzenlemeler dairesi başkanlığı.
  • Ehrlich I. (1973). Participation in illegitimate activities: a theoretical and emprical investigation, Journal of Political Economy, 81(3), 521-565.
  • Fajnzylber P., Lederman D., Loayza N. (2002a). Inequality and violent crime, Journal of Law and Economics, 45(1), 1-40.
  • Fajnzylber P., Lederman D., Loayza N. (2002b). What causes violent crime, European Economic Review, 46(7), 1323-1357.
  • Fleisher B.M. (1966). The effect of income on delinquency, The American Economic Review, 56, (1/2), 118-137.
  • Freeman R.B. (1982). Crime and the labor market, Working Paper, 1031, National Bureu of Economic Research.
  • İmrohoroğlu A., Merlo A., Rupert P. (2004). What accounts for the decline in crime, International Economic Review, 45(3), 707-729.
  • İzaadi N., ve Piraee K. (2012). Income inequality and property crime: evidence from Iran, World Applied Sciences Journal, 19 (2), 281-286.
  • Kelly M. (2000). Inequality and crime, Review of Economics and Statistics, 82(4), 530–539.
  • Keshavarz G.H., ve Markazi H.M. (2011). The socioeconomic and demographic determinants of crime in Iran (a regional panel study), European Journal of Law and Economics, 32(1), 99–114.
  • LeSage J. (1998). Saptial Econometrics, Deparment of Economics, University of Toledo December [Available online at: https://www.spatial-econometrics.com/html/wbook.pdf], Retrieved on November 15, 2019.
  • LeSage J., ve Pace R.K. (2009). Introduction to Spatial Econometrics , Florida, Chapman and Hall.
  • Luiz J.M. (2001). Temporal association, the dynamics of crime and other economic determinants: a time series econometric model of South Africa, Social Indicators Research, 53, 33-61.
  • Merton R.K. (1938). Social structure and anomie, American Sociological Review, 54, 597 - 611.
  • Moran P. (1950a). Notes on continuous stochastic phenomena, Biometrika 37, 17-23.
  • Moran P. (1950b). A test for the serial independence of residuals, Biometrika 37, 178-181.
  • Neumayer E. (2005). Inequality and violent crime: evidence from data on robbery and violent theft, Journal of Peace Research, 42(1), 101-112.
  • Nilsson A. (2004). Income inequality and crime: the case of Sweden, ınstitute for labour market policy evaluation, Working Paper, 2004(6).
  • Oliver A. (2002). The economics of crime: an analysis of crime rates in America, The Park Place Economist, 10, 30-35.
  • Shaw C.R., ve Mckay H.D. (1942). Juvenile delinquency and urban areas: a study of rates of delinquencies in relation to differential characteristics of local communities in American cities, Chicago, University of Chicago Press.
  • Thorbecke E., ve Charumilind C. (2002). Economic inequality and its socioeconomic impact, World Development, 30(9), 1477-1495.
  • Warren E.H. (1978). The economic approach to crime, Canadian Journal of Criminology, 20(4).
  • Yıldız R., Öcal O., Yıldırım E. (2011). Suçun sosyoekonomik belirleyicileri: Kayseri üzerine bir uygulama, Erciyes Üniversitesi İİBF Dergisi, 36, 15-31.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Uğur Çapar 0000-0002-0292-8473

Nihal Yayla 0000-0002-0647-5088

Yayımlanma Tarihi 25 Ekim 2020
Gönderilme Tarihi 22 Kasım 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 11 Sayı: 3

Kaynak Göster

APA Çapar, U., & Yayla, N. (2020). Suç-Gelir Dağılımı İlişkisinde Mekansallığın Etkisi: Türkiye’de Düzey 2 Bölgeleri İçin Bir Analiz. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 11(3), 699-710. https://doi.org/10.36362/gumus.649958