암수범죄 추정 : 미신고율을 활용한 수학적 모형

Estimating dark figure: Mathematical model utilizing crime-unreporting rate

Official crime data unfortunately contains innate problems, such as dark figure. The current study attempts to estimate dark figure using an approach different from traditional methods. This study, first, reviews literature on unreporting crime and finds out factors affecting unreporting crime. Second, this study develops a mathematical model of estimating dark figure using the factors identified based on logistic regression model. To prove validity of estimation from the model, this study collected data from 733 participants selected through multistage cluster sampling (sampling frame: 900) and convenience sampling (sampling frame: 300) in Seoul, Busan, Daejeon, and Kwangju cities. Dark figure index(DFI) from the model was 1.6643, which means that about 1.7 times of official data on larceny was the substancial total larceny cases occurred in 2008. DFI ranged 1.6 ~ 28.7 depending on crime reporting rates. Based on findings, policy implications have been suggested.