Introduction
The assessment of the link between age and crime is vital since it can allow law enforcement to predict potential crime occurrences in society. Law enforcement agencies can carry out effective manpower planning by sending officers to areas where they are needed the most. Resultantly, criminal offenses can be prevented from occurring. In addition, the results of the examination of the link between crime and age can assist the government to allocate criminal justice resources effectively. For instance, the government can increase the number of juvenile rehabilitation centers if the results show a correlation between high crime rates and young individuals. The results can assist the policymakers to evaluate the effectiveness of their policies. If results show a link between high crime rates and young people, policymakers can formulate policies that target such individuals. After a period, the link between high crime rates and young people can be examined to identify whether there are notable differences. A decrease in the correlation between high crime rates and young individuals can show that the policy is working. On the other hand, if there is still a high correlation between high crime rates and young people, policymakers will need to introduce new policies. This research will focus on extracting variables, including age and total offenses charged, from a dataset to examine the correlation between crime and age. It is hypothesized that there is a strong positive correlation between young age and high crime rates and a strong negative correlation between old age and high crime rates.
Literature Review
The link between age and crime is one of the most tested relationships in criminology. Early research carried out by Adolphe Quetelet in the 1820s showed a relationship between age and crime. Quetelet reviewed France’s national crime statistics and discovered that the crime rate rose with age and peaked at age 25. The crime rate reduced significantly past the age of 25. These results are backed by recent findings. Horyniak et al. (2014) examined the age-linked variations in patterns of crime among a sample of polydrug injectors in Australia. The researchers focused on identifying the relationship between age and criminal behaviors such as drug dealing, property crime, and violent crime. They used descriptive statistics to examine the prevalence and frequency of each variable. Their results showed that each five-year increment in age was linked with substantial decreases in drug dealing, violent crime, and property crime. According to their results, older participants were less likely to report being arrested compared to young participants. They concluded that younger individuals were more significantly involved in crime than their older counterparts. Rocque et al. (2015) discussed the link between age and crime. According to them, criminal behavior increases in early adolescence, specifically from age 14 and peaks in ages between 20 and 25 years. The crime rate decreases thereafter. The researchers mentioned that the high correlation between the high crime rates and young individuals could be explained by physical and energy needs. According to them, criminal behavior requires considerable physical input and young individuals have greater physical energy than old individuals. For this reason, high crime rates are likely to be reported among young individuals. Physical strength recedes with age, and, as a result, old people are less likely to engage in criminal activities. The authors also indicated that the reduction in testosterone levels with age might explain the low crime rate levels among old individuals. According to the researchers, sociological factors also support the existence of a crime-age relationship. Young individuals typically have problems asserting their independence needs. In addition, they are constrained by various laws, including those which prevent them from voting and purchasing alcohol. Such situations are likely to lead to stress, and such individuals may act out by engaging in crime. For instance, they may acquire fake identification cards to enable them to purchase alcohol. In this regard, there is likely to be a strong correlation between young individuals and high crime rates, according to the researchers. Ulmer and Steffensmeier (2014) examined the age and crime link, and their results were congruent to earlier findings. According to them, age is one of the strongest demographic factors linked with criminal behavior. The authors' findings showed that high crime rates were realized at the onset of late adolescence, and the rates peaked in early adulthood. The crime rates reduced thereafter. According to the researchers, crime rate is likely to decrease in adulthood. The reduction in the number of crimes is attributed to the acquisition of meaningful bonds to traditional institutions such as marriage, family, and community institutions. Work is also another meaningful outlet for such individuals, given that it is economically rewarding. Work, marriage, family, and community institutions alter an individual's daily schedule in ways that make criminal behavior less likely. They also foster social bonds that promote desistence from crime and offer a strong basis for the creation of a non-criminal identity. For these reasons, crime is less likely to be experienced among old-aged individuals compared to their young counterparts. Old age comes with experience, and, in this regard, offenders may gradually learn that crime has more negative gains than positive ones. Old-aged offenders are more likely to view jail term as more a serious threat given that they have more to lose than young individuals (Ulmer & Steffensmeier, 2014). Aging criminals are more likely to fully realize that time is a highly valuable resource that is decreasing. In this scenario, such individuals are less likely to engage in criminal behavior, causing a low correlation between old age and low crime rates. According to the researchers, young individuals, due to their inexperience, are likely to be influenced to commit crimes by their peers. In this respect, there is likely to be a high correlation between young age and high crime rates.
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Data and Methods
Data for this quantitative research were obtained from the Uniform Crime Reporting Program that generates credible statistics for use in law enforcement. The publicly accessible data is provided by the FBI, which owns the UCR program. The data for the quantitative analysis was obtained from table 38, which listed information from 2019. It showed the total number of offenses charged in 2019 by age. The variables of interest were the total offenses charged for individuals aged above ten years. Based on the data, the total number of observations was 21. The age groups that were used in the quantitative analysis include 10-12, 13-14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, and 65-and-above. The UCR data did not exhibit uniformity, given it presented individual age values and age groups. In this sense, the data could not be used for the analysis in its original form. Resultantly, numeric values from 1 to 21 were assigned to each age variable to ensure it was eligible for quantitative analysis. For instance, age group 10-12 was represented by 1 and the last age group, that is 65-and-above, was represented by number 21. The uniform numeric age data was then subjected to quantitative analysis.
The primary quantitative techniques used in analyzing the data include descriptive statistics and Pearson correlation analysis. The descriptive analysis showed the ages in which the highest and lowest number of total charged offenses were reported. The correlation analysis was used to determine the Pearson correlation coefficient between age and the total number of offenses charged.
Results
The crime-age relationship is illustrated below.
The descriptive statistics are depicted below.
| highest number of offenses charged | age |
| 1,164,519 | 25-29 |
| lowest number of offenses charged | Age |
| 36,691 | 10-12 |
Based on the maximum value, the highest number of total offenses charged were reported among individuals aged between 25 and 29 years. On the contrary, the lowest number of aggregate offenses charged were reported among individuals aged between 10 and 12 years. The correlation results are illustrated below.
| correlation between total offenses and all ages | ||
| age representation | Total offenses charged | |
| age representation | 1 | |
| Total offenses charged | 0.390228983 | 1 |
| correlation between total offenses and younger ages (below 30 years) | ||
| age representation | Total offenses charged | |
| age representation | 1 | |
| Total offenses charged | 0.610966843 | 1 |
| correlation between total offenses and older ages (above 30 years) | ||
| age representation | total offenses charged | |
| age representation | 1 | |
| total offenses charged | -0.977631942 | 1 |
The Pearson correlation coefficient between all ages and the total number of offenses charged in 2019 is 0.39. It is below 0.5, meaning that the relationship between age and the total number of offenses charged is weak. The positive correlation shows that the two variables move in the same direction. In this regard, if age increases, the total number of offenses also increases. Overall, the crime rate rises with the increase in age.
The Pearson correlation coefficient between younger ages (below 30) and the total number of offenses charged in 2019 is 0.61. The coefficient is above 0.5, meaning that the link between younger age and the total number of offenses is strong. It is positive, meaning that the two variables move in the same direction. In this case, if the age of young individuals aged below 30 years increases, the total number of offenses also increases.
The Pearson correlation coefficient between older ages (above 30) and the total number of offenses charged in 2019 is -0.98. The coefficient is negative, meaning that the two variables move in the opposite direction. It is above -0.5, meaning that there is a strong relationship between older ages and the total number of offenses charged. In this case, if the age of older individuals aged above 30 years increases, the total number of offenses also decreases.
Conclusion
The results of the quantitative analysis verified the stated hypothesis. They proved that there is a strong positive relationship between young age and high crime rates and a strong negative correlation between old age and high crime rates. In this case, young individuals, specifically those aged below 30 years, form the bulk of the perpetrators. Based on the results, high crime rates are prevalent among young people between 10 years and 30 years. The number of crimes committed by individuals aged below 30 years increases with age. On the other hand, there are low crime rates among individuals aged above 30 years. In addition, the number of crimes committed by individuals aged above 30 years decreases with the increase in age. The crime rate peaks between the ages of 25 and 29 years. Overall, there is a weak positive relationship between age and crime. Based on the results, policymakers should create policies targeted towards young individuals to ensure a reduction in crime rates. In this case, the policymakers will play a large part in making society safe. Future research should focus on individual US states to identify whether a similar relationship is present between crime and age.
References
Horyniak, D., Dietze, P., Degenhardt, L., Agius, P., Higgs, P., Bruno, R., ... & Burns, L. (2016). Age-related differences in patterns of criminal activity among a large sample of polydrug injectors in Australia. Journal of Substance Use , 21 (1), 48-56. https://doi.org/10.3109/14659891.2014.950700
Rocque, M., Posick, C., & Hoyle, J. (2015). Age and crime. The Encyclopedia of Crime and Punishment , 1-8. https://doi.org/10.1002/9781118519639.wbecpx275
Ulmer, J. T., & Steffensmeier, D. J. (2014). The age and crime relationship: Social variation, social explanations. In The Nurture Versus Biosocial Debate in Criminology: On the Origins of Criminal Behavior and Criminality (pp. 377-396). SAGE Publications Inc. https://doi.org/10.4135/9781483349114.n23
