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

Year 2024, Volume: 20 Issue: 2, 392 - 417, 28.06.2024

Neslihan Akın Özdemir , Çiğdem Arıcıgil Çilan

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

Abstract

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

Keywords

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

Abstract

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.

Keywords

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

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