The article aimed to establish the relationship between economic and crime in Canada. According to the article, the relationship between the two variables has always been investigated by various social researchers. Several theories, including utilitarian, strain, and rational choice theories, have been used to describe the relationship. The article focuses on the counter and land model that discusses the relationship between crime and economy from a different perspective. The model uses unemployment as the representation of the economy, hence examining how unemployment influences criminal activities through motivation and opportunity.
Summary
Methodology
Researchers used the panel data of ten Canadian provinces between 1981 and 2009 from Statistics Canada’s Canadian Socio-economic Information Management database. The measurement of all crimes was the natural logarithm of crime rate per every 100,000 people (Jalles & Andresen, 2016, p.1272). Crimes included in the study were robbery, theft from and of vehicles, assault, sexual assaults, homicides, violent crimes, shoplifting, burglary, and property crime. Researchers were primarily interested in the GDP, unemployment, and low income. The measure of unemployment was the rate; the percentage of unemployed people relative to the workforce aged between 16 and 64 years in each province. GDP per capita was measured in millions of 2002 dollars, while the low income was measured using the natural logarithm of those considered low income in Canada. Low-income earners are those that spend 20% or more of their earning n the basic needs (Jalles & Andresen, 2016, p.1272). Low income was represented as a percentage to enhance a straightforward interpretation of data. Researchers used control variables including alcohol spending, young males, divorce, Gini index, police expenditure, percentage of immigrants, expenditure on corrections, and police officer per capita.
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The Cantor and Land's model illustrated serial correlation of the independent variable, hence using a dynamic panel approach to provide consistent estimates such as the General Method of Moments (GMM). Different GMM were used in the study. Particularly control variables were included in the regression to wipe out bivariate relationships. The Bayesian model of averaging (BMA) was used in the selection process (Jalles & Andresen, 2016, p.1272). Models and parameters were treated as random variables by BMA. The results of BMA analysis entailed posterior inclusion probabilities (PPE) for variables. PPE was less than 0.10, indicating a positive or negative correlation. Each crime becomes more robust, with a decline in the PPE.
Theory
The Cantor and Land’s model was used in the study to determine the relationship between the economy and the crimes in Canada. The theory uses unemployment to represent the measure of the economy. The model emphasizes that there are more contentious empirical outcomes where the opportunity effect tends to dominate the motivation effects both on frequency and magnitude of statistical significance. The theory argues that a rise in motivation results in an increase in criminal activities. On the other hand, economic hardships, together with guardianship, cause a decline in crimes. When more people stay at home providing guardianship to property and individuals, there are less criminal activities.
Cantor and Land model used the unemployment lagged one time period to illustrate that people are not expected to immediately turn into criminal activities. Instead, they wait until motivation impacts their decision to engage in crime (Jalles & Andresen, 2016, p.1269). However, individuals are expected to be at home shortly if they are not working. In the case of an economic downturn, the only people that will feel threatened by unemployment are still working. Therefore, they will opt to spend more time at home as well. The unemployment rate is considered appropriate for the model because it represents those that stay at home. After all, they do not have jobs and those that are forced by the economic changes.
Findings and Argument of the Study
The findings of the research were presented in the form of a table containing 12 types of crimes. The table illustrates the effects of unemployment on each offense. It shows the correspondence of the independent variable to the various crimes and the Posterior probabilities of exclusion (PPE) as the BMA analysis and the signs that indicate the certainty index of a relationship. All PPE less than 0.20 were given signs, while those that did not have a sign illustrated the uncertainty of the relationship. Findings were also presented using the fixed effects panel (Jalles & Andresen, 2016, p.1275). The panel showed that six out of 12 were significantly and related. All the connections were positive except for the cumulative of crimes of violent, which was constant with the motivation effects. GDP per capita estimates were positive.
The research claims that unemployment fosters crime when there is an insignificant coefficient of the SC and the economic conditions. Besides, it adds that the SEC is associated with more crimes.
Limitations of the study
One of the significant limitations of the study was that majority of the mechanisms used in various theoretical frameworks are identical if not the same. However, they are not considered in the same context (Jalles & Andresen, 2016, p.1268). Another limitation was the unemployment rate. The variable did not include the underemployed as well as those who gave up on the labor market.
Research Questions
The author scrutinized the relationship between the various forms of crimes and how unemployment might influence them to determine whether there is any relationship between the economy and crimes. Researchers focused on the multiple factors that influence the decision to engage in criminal activities, including low income and lack of employment. The research question of the study was”what is the relationship between economy and crime.”
Evidence
Researchers utilized several statistical methods and economic measures to enhance a better understanding of the nuance of the correlation between the two variables. The Cantor and Land model were used to test the relationship in all the variables and statistical methods. The model is used to establish both the opportunity and motivation impacts for each of the identified crimes. Low income was used as an indicator of motivation to engage in criminal activities. Researchers used the findings of other researchers to compare the outcome of the research and establish its credibility. Besides, there was the utilization of the control tests that enable researchers to show a change in crime with a change in unemployment relative to the control variables. Researchers ensure that they used data from different provinces to enhance generalization and reduce issues of bias.
Conclusion
I agree with the argument of researchers that there is a significant association between the economy and crime. Unemployed individuals are more likely to be enticed and motivated to engage in crimes as a way to obtain some living hood. As opposed to the underemployed and those earning low income, people without jobs do not have an optional way to cater to their needs. However, even as the economy continues to thrive, the unemployed may be motivated because of the increase in wealth among the employed. With a bad economy, even people with jobs are likely to concentrate on minimizing their spending. Therefore, they will emphasize protecting their homes and properties, thus minimizing the possibility of crimes like breaking in and theft of property.
References
Jalles, J. T., & Andresen, M. A. (2017). Understanding the relationship between the economy and crime: Canadian provinces, 1981-2009. International Journal of Social Economics .
