Abstract
The age–crime curve has been regarded as a stylized fact of the age–crime relationship. However, recent studies have found that some Asian countries have an age–crime distribution that deviates from the archetypal age–crime curve. This variation has been attributed to cultural factors associated with age effects. Using age-specific arrest data from Korea for the years 1976 through 2019, this study offers a complementary explanation of divergent age–crime patterns across countries. We make an empirical case that the observed difference between the Korean age–crime distribution and the archetypical age–crime curve in Western countries cannot be due to contextual influence on age effects alone. The age–crime relationship in Korea shows both static and dynamic characteristics. The age–period–cohort analysis of variance shows that age effects largely explain the age–crime relationship, but period and cohort effects also explain the change in the age–crime relationship over time. Moreover, the extent to which each effect manifests depends on the type of crime. Based on our findings, we propose hypotheses regarding the role of social context in shaping each effect. We suggest that cultural factors largely shaped age effects by creating age-graded changes in the social control mechanism and in routine activities. In addition, we suggest that other contextual factors, such as population dynamics, simultaneously shaped period and cohort effects by changing the level of social control, and routine activities across birth cohorts. Both cultural and historical dimensions of social context are required to understand divergent age–crime patterns across countries.
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Notes
Some scholars have interpreted the age–crime invariance thesis as a mathematical-form invariance (Britt, 1992)––that is, if two age–crime distributions fit in the same mathematical functional form, they are considered as invariant even though two distributions have significantly different statistical parameters of distributions (e.g., peak age, mean age, or skewness of distribution). Yet, we understand Hirschi and Gottfredson’s use of the word “invariance” as a qualitative term, and not a rigorously statistical one. Their major concern was whether any historical/cultural influences can be inferred from the difference in parameters between two or more age–crime distributions. Hirschi and Gottfredson’s (1983: 554) two major theses were “(1) the age distribution of crime is invariant across social and cultural conditions, … [and], (3) the age distribution of crime cannot be accounted for by any variable or combination of variables currently available to criminology”. And they said “our third thesis is a corollary of the thesis that the age distribution of crime is invariant across social conditions. If the age effect cannot be even partially explained by historical trends or cross-cultural comparisons, …, then there is reason to believe that efforts to explain the age effect with the theoretical and empirical variables currently available to criminology are doomed to failure” (p.566). Put differently, the age–crime invariance thesis will no longer be the case if the difference in the parameters between two age–crime distributions––even though they can fit into a single mathematical functional form––can be explained by historical or cultural differences. Consistent with how they suggest the age effect in a qualitative manner, our analytical goal is to examine whether the difference in the age–crime distributions between Korea and Western countries can be explained by historical or cultural differences.
In the Analytical Statistics on Crime (ASC), total crime comprises 213 types of offense including traffic offenses such as drunken driving and unlicensed driving. Although there was no significant difference, we present both the age distribution of total crime and that of total crime excluding traffic offenses in Figure S2. The violent crime index comprises assault, battery, willful infliction of bodily injury, extortion, and other crimes involving violence. Note that homicide, robbery, rape, and arson are excluded in this category. The property crime index consists of burglary, larceny-theft, fraud, possession of stolen goods, embezzlement, and breach of trust.
Age groupings in the ASC are not consistent both within and between years. For the years 1976–1994, age at arrest is recorded in single year increments from the age of 14 to 25, in 5-year increments from the age of 26 to 40, in 10-year increments from the age of 40 to 60, and 61 and over; for the years 1995–2013, age at arrest is recorded in 10-year increments from the age of 40 to 70, and 71 and over; for the years 2014–2019, it is recorded in 5-year increments from the ages of 26 to 60, in 10-year increments for the age of 61 to 70, and 71 and over. The data availability for age–specific arrest rates in Korea is shown in our supplemental file (Table S1).
APC ANOVA approach do not identify “the amount of variance in the outcome variable that is due to the linear trend component for the factor that is entered into the model last.” (O’Brien, 2014, p.118) However, it allows us to estimate how much of the variance in the age-specific crime rates is uniquely associated with the non-linear effects of each factor controlling for the other two factors (O’Brien, 2014). Thus, the statistical significance test is a sufficient, rather than necessary, condition for showing that each factor contributes to age-specific arrest rates (O’Brien, 2014).
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Kang, B., Hureau, D.M. Social Context and the Static and Dynamic Age–Crime Relationship in the Republic of Korea. Asian J Criminol 18, 21–41 (2023). https://doi.org/10.1007/s11417-022-09391-6
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DOI: https://doi.org/10.1007/s11417-022-09391-6