SUÇU ETKİLEYEN SOSYOEKONOMİK FAKTÖRLERİN ROBUST YÖNTEMLER İLE BELİRLENMESİ: AVRUPA BİRLİĞİ ÖRNEĞİ

Yıl 2024, Cilt: 20 Sayı: 2, 392 - 417, 28.06.2024

Neslihan Akın Özdemir , Çiğdem Arıcıgil Çilan

https://doi.org/10.17130/ijmeb.1379567

Öz

Ülkelerde ve kurumlarda barış ve adaletin sağlanması Birleşmiş Milletler’in sürdürülebilir
kalkınma amaçlarındandır. Mevcut çalışmada Avrupa Birliği’ne üye ülkeler için şiddet ve mülkiyet
suçlarının sosyoekonomik belirleyicilerinin tespit edilmesi amaçlanmıştır. Bu amaçla uygulamada Robust
yöntemler kullanılmıştır. Çalışmada Robust Değer Atama Yöntemi; şiddet ve mülkiyet suç türlerine ilişkin
eksik gözlemler içeren bazı ülkelerin eksik gözlemlerinin atanmasında kullanılırken; Robust Seyrek Temel
Bileşenler Analizi veri setinin boyutunu indirgemede kullanılmıştır. Şiddet suçunun bağımlı değişken
olarak kullanıldığı model için Robust Regresyon Analizi sonucuna göre Fransa, Belçika ve İsveç aykırı
gözlemler olarak tespit edilmiştir. Kişi başına reel gsyih, işsizlik oranı ve gini katsayısı ile yüz bin
kişiye düşen şiddet suç sayısı arasındaki ilişki pozitif yönlü iken yükseköğrenime kayıt oranı ve 15-24
yaş genç nüfus oranı ile yüz bin kişiye düşen şiddet suç sayısı arasındaki ilişki ise negatif yönlü olarak
tespit edilmiştir. Mülkiyet suçunun bağımlı değişken olarak kullanıldığı model için ise sadece İrlanda
aykırı gözlem olarak tespit edilmiştir. Kişi başına reel gsyih ile yüz bin kişiye düşen mülkiyet suç sayısı
arasındaki ilişki ise pozitif yönlü bulunmuştur

Anahtar Kelimeler

Suç, Aykırı Gözlem, Robust Değer Atama Yöntemi, Robust Seyrek Temel Bileşenler Analizi, Robust Regresyon.

DETERMINATION OF SOCIOECONOMIC FACTORS AFFECTING CRIME WITH ROBUST METHODS: THE CASE OF EUROPEAN UNION

Öz

In the current study, it is aimed to determine the socio-economic determinants of violence and
property crimes for the member states of the European Union. For this purpose, Robust methods were
used in practice. In the study, Robust Imputation Method was used to impute missing observations of
some countries with missing observations on violence and property crime types, Robust Sparse Principal
Components Analysis was used to reduce the dimension of the data set. According to the results of the
Robust Regression Analysis for the model in which violent crime was used as the dependent variable,
France, Belgium and Sweden were identified as outliers. While the relationship between real gdp per
capita, unemployment rate and Gini coefficient and the number of violent crimes per hundred thousand people is positive, the relationship between the higher education enrollment rate and the young population
rate of 15-24 years old and the number of violent crimes per hundred thousand people is negative. It was
determined as. For the model in which property crime was used as the dependent variable, only Ireland
was identified as an outlier.

Anahtar Kelimeler

Crime, Outlier Observation, Robust Imputation Method, Robust Sparse Principal Component Analysis, Robust Regression

Kaynakça

• Adeleye, N., & Jamal, A. (2020). Dynamic analysis of violent crime and income inequality in Africa. International Journal of Economics, Commerce and Management, 8(2), 1-25.
• Aksu, H., & Akkuş, Y. (2010). Türkiye’de mala karşı suçların sosyoekonomik belirleyicileri üzerine bir deneme: Sınır testi yaklaşımı (1970–2007). Sosyoekonomi, 1, 191-214.
• Altindag, D. T. (2012). Crime and unemployment: Evidence from Europe. International review of Law and Economics, 32(1), 145-157.
• Amın, S., & Ahmad, N. (2017). Ethnic diversity, social exclusion and economic determinants of crimes: A case study of Pakistan. Social Indicators Research, 140, 267–286.
• Andresen, M. A. (2013). Unemployment, business cycles, crime, and the Canadian provinces. Journal of Criminal Justice, 41, 220-227.
• Anwar, A., Arshed, N., & Anwar, S. (2017). Socio-economic determinants of crime: An empirical study of Pakistan. International Journal of Economics and Financial Issues, 7(1), 312-322.
• Bagheri, A., Midi, H., Ganjali, M., & Eftekhari, S. (2010). A comparison of various influential points diagnostic methods and robust regression approaches: Reanalysis of interstitial lung disease data. Applied Mathematical Sciences, 4(28), 1367 – 1386.
• Baharom, H. A., & Habıbullah, S. M. (2009) Crime and inequality: The case of Malaysia. Journal of Politics and Law, 2(1), 55-70.
• Barnett, C., & Mencken, F. C. (2009). Social disorganization theory and the contextual nature of crime in nonmetropolitan counties. Rural Sociology, 67(3), 372–393.
• Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76(2), 169-217.
• Branden, K. V., & Verboven, S. (2009). Robust data imputation. Computational Biology and Chemistry, 33(1), 7–13.
• Brosnan, S. (2018). The Socioeconomic determinants of crime in Ireland from 2003-2012. The Economic and Social Review, 49(2), 127-143.
• Buonanno, P., & Montolıo, D. (2008). Identifying the socio-economic and demographic determinants of crime across Spanish provinces. International Review of Law and Economics, 28(2), 89–97.
• Chen, W., & Keen, M. (2014). Does inequality increase crime? The effect of income inequality on crime rates in California 130 Counties. Working Paper. San Francisco State University.
• Chen, X., & Zhong, H. (2020). Development and crime drop: A time-series analysis of crime rates in Hong Kong in the last three decades. International Journal of Offender Therapy and Comparative Criminology, 65(4), 409– 433.
• Choe, J. (2008). Income in the United States. Economics Letters, 101(1), 31–33.
• Corman, H., Joyce, T., & Lovıtch, N. (1987). Crime, deterrence and the business cycle in New York City: A var approach. The Review of Economics and Statistics, 69(4), 695.
• Corman, H., & Mocan, N. (2015). Alcohol consumption, deterrence and crime in New York City. Journal of Labor Research, 36, 103-128.
• Cömertler, N., & Kar, M. (2007). Türkiye’de suç oranının sosyo-ekonomik belirleyicileri: Yatay kesit analizi. Ankara Üniversitesi SBF Dergisi, 62(2), 37-57.
• Dehon, C., Gassner, M., & Verardı, V. (2009). A Hausman-Type test to detect the presence of influential outliers in regression analysis. Economics Letters, 105(1), 64–67.
• Durusoy, S., Köse, S., & Karadenız, O. (2008). Başlıca sosyo ekonomik sorunlar suçun belirleyicisi olabilir mi? Türkiye’de iller arası bir analiz. Elektronik Sosyal Bilimler Dergisi, 7(23), 172-203.
• Ehrlich, I. (1973). Participation in illegitimate activities: A theoretical and empirical investigation. Journal of Political Economy, 81(3), 521–565.
• Faisal, S., & Tutz, G. (2021). Multiple imputation using nearest neighbor methods. Information Sciences, 570, 500–516.
• Fılzmoser, P., & Todorov, V. (2011). Review of robust multivariate statistical methods in high dimension. Analytica Chimica Acta, 705(1–2), 2–14.
• Gad, A. M., & Qura, M. E. (2016). Regression Estimation in the presence of outliers: A comparative study. International Journal of Probability and Statistics, 5(3), 65-72.
• Gao, G., Liu, B., & Kouassı, I. (2017). The contemporaneous effect of unemployment on crime rates: The case of Indiana. Southwestern Economic Review, 44, 99-107.
• Grant, D. S., & Martınez, R. (1997). Crime and the restructuring of the U.S. economy: A reconsideration of the class linkages. Social Forces, 75(3), 769–798.
• Halıcıoğlu, F. (2012). Temporal causality and the dynamics of crime in Turkey. International Journal of Social Economics, 39(9), 704–720.
• Hazra, D. (2020). What does (and does not) affect crime in India?. International Journal of Social Economics, 47(4), 503–521.
• Hooghe, M., Vanhoutte, B., Hardyns, W., & Bırcan, T. (2010). Unemployment, inequality, poverty and crime: Spatial distribution patterns of criminal acts in Belgium, 2001-06. British Journal of Criminology, 51(1), 1–20.
• Hubert, M., Reynkens, T., Schmıtt, E., & Verdonck, T. (2016). Sparse PCA for high-dimensional data with outliers. Technometrics, 58(4), 424–434.
• Hubert, M., Rousseeuw, P. J., & Van Aelst, S. (2008). High-Breakdown robust multivariate methods. Statistical Science, 23(1), 92–119.
• Hubert, M., Rousseeuw, P.J., & Branden, K. V. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47(1), 64-79.
• Igbinedion, S. O. & Ebomoyi, I. (2017). Socio-Economic determinants of crime: Further evidence from Nigeria. Annals of the University of Petroşani, Economics, 17(1), 101-114.
• Ishak, S., & Banı, Y. (2017). Determinants of crime in Malaysia: Evidence from developed states. International Journal of Economics & Management, 11(3), 607– 622.
• Khan, N., Ahmed, J., Nawaz, M., & Zaman, K. (2015). The socio-economic determinants of crime in Pakistan: New evidence on an old debate. Arab Economic and Business Journal, 10(2), 73-81.
• Kızmaz, Z. (2003). Ekonomik yapı ve suç: Bazı araştırma bulguları üzerine genel bir değerlendirme. Fırat Üniversitesi Sosyal Bilimler Dergisi, 13(2), 279-304.
• Kposowa, A. J., Breault, K. D., & Harrıson, B. M. (1995). Reassessing the structural covariates of violent and property crimes in the Usa: A county level analysis. The British Journal of Sociology, 46(1), 79- 105.
• Levin, H. M., Belfield, C., Muennig, P., & Rouse, C. (2007). The costs and benefits of an excellent education for all of America’s children. New York: Teachers College.
• Lıu, F., Huang, M., Yang, Q., & Wang, Y. (2021). Research on sustainable development performance evaluation of China’s high end equipment manufacturing enterprises. In IOP Conference Series: Earth and Environmental Science, 632(5)(pp. 052027). IOP Publishing.
• Lochner, L. (2020). Education and crime. In S. Bradley and C. Green (Eds.), The economics of education a comprehensive overview (pp. 109-117). United Kingdom: Academic Press.
• Meera, A. K., & Jayakumar, M. D. (1995). Determinants of crime in a developing country: A regression model. Applied Economics, 27(5), 455–460.
• Moller, S. F., Von Frese, J., & Bro, R. (2005). Robust methods for multivariate data analysis. Journal of Chemometrics, 19(10), 549–563.
• Moore, M. D., & Recker, N. L. (2013). Social capital, type of crime, and social control. Crime & Delinquency, 62(6), 728–747.
• Nikolaos, D., & Alexandros, G. (2009). The effect of socio-economic determinants on crime rates: An empirical research in the case of greece with cointegration analysis. International Journal of Economic Sciences and Applied Research, 2, 51-64.
• Odabaşı, S. (2021). The economics of crime and immigration: A panel data analysis. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 45, 387-399.
• Odabaşı, S. (2022). Türkiye’de suç ve iktisadi belirleyicileri: Panel veri analizi. İzmir İktisat Dergisi, 37(3), 586-606.
• Purnomo, S. D., Supriyo, D. A., Rusito, R., Anindito, T., Hariadi, W., & Jati, D. (2023). How economic indicator drive crime? Empirical study in developing country, Indonesia. International Journal of Economics and Financial Issues, 13(3), 94.
• Rakshıt, B., & Neog, Y. (2020). Does higher educational attainment imply less crime? Evidence from the Indian states. Journal of Economic Studies, 48(1), 133–165.
• Rousseeuw, P. J., & Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association, 85(411), 633-639.
• Rousseeuw, P. J., Aelst, S. Van, Drıessen, K. Van., & Gullo, J. A. (2004). Robust multivariate regression. Technometrics, 46(3), 293–305.
• Saridakıs, G. (2004). Violent crime in the United States of America: A time-series analysis between 1960–2000. European Journal of Law and Economics, 18(2), 203–221.
• Schleimer, J. P., Pear, V. A., McCort, C. D., Shev, A. B., De Biasi, A., Tomsich, E., ... & Wintemute, G. J. (2022). Unemployment and crime in US cities during the coronavirus pandemic. Journal of urban health, 99(1), 82-91.
• Staudte, R. G., & Sheather, S. J. (1990). Robust estimation and testing. Canada: John Wiley & Sons. Sugiharti, L., Purwono, R., Esquivias, M. A., & Rohmawati, H. (2023). The nexus between crime rates, poverty, and income inequality: A case study of Indonesia. Economies, 11(2), 62.
• Tarlıng, R., & Dennıs, R. (2016). Socio-Economic determinants of crime rates: Modelling local area police-recorded crime. The Howard Journal of Crime and Justice, 55(1-2), 207– 225.
• Tadjoeddın, M. Z., Yumna, A., Gultom, S. E., Rakhmadı, M. F., & Suryahadı, A. (2021). Inequality and violent conflict: New evidence from selected provinces in Post-Soeharto Indonesia. Journal of the Asia Pacific Economy, 26(3), 552-573.
• Todorov, V., & Fılzmoser, P. (2009). An object-oriented framework for robust multivariate analysis. Journal of Statistical Software, 32, 1-47.
• Toka, O., & Çetın, M. (2016). Imputation and deletion methods under the presence of missing values and outliers: A comparative study. Gazi University Journal of Science, 29(4), 799-809.
• Toka, O., Çetin, M. & Arslan, O. (2021). Robust regression estimation and variable selection when cellwise and casewise outliers are present. Hacettepe Journal of Mathematics & Statistics, 50(1), 289 – 303.
• Tunca, H. (2019). Suçun sosyo-ekonomik belirleyicileri: Panel veri analizi. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 8(4) , 2767-2784.
• Tümer, T. (2019). Suçun fırsat maliyeti yaklaşımı ile analizi: Türkiye örneği. Anadolu İktisat ve İşletme Dergisi, 3(2), 93-113.
• United Nations. (no date). Peace, Justice and Strong Institution. Retrieved from https://www.un.org/ sustainabledevelopment/peace-justice. Accessed 16.08. 2022.
• Verboven, S., Branden, K. V., & Goos, P. (2007). Sequential imputation for missing values. Computational Biology and Chemistry, 31, 320–327.
• Yohaı, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, 15(20), 642-656.
• Yorulmaz, Ö., & Gıray Yakut, S. (2017). Türkiye’de suç oranını etkileyen sosyoekonomik faktörlerin incelenmesi: Path analizine dayanıklı yaklaşım. Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 39(1), 307-322.
• Yorulmaz, Ö. (2016). Dayanıklı istatistiksel yöntemler ve R uygulamaları. İstanbul: Beta.
• Yu, C., & Yao, W. (2017). Robust linear regression: A review and comparison. Communications in Statistics - Simulation and Computation, 46(8), 6261–6282.
• Yuan, K.-H., & Zhong, X. (2008). Outliers, leverage observations, and influential cases in factor analysis: Using robust procedures to minimize their effect. Sociological Methodology, 38(1), 329–368.