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Issue Analysis Paper: Discussion of a Research Paper

In their article ‘The Effect of Job Loss and Unemployment Insurance on Crime in Brazil’ (2020), Britto et al. (2020) study the mechanism through which being laid off leads people to commit a crime and whether unemployment benefits can counter this tendency. The study is critical because Brazil has a high crime rate, and understanding the socioeconomic causes of crime is essential. The study took place between 2009 and 2017, which is mentioned, providing source specificity.

Britto et al. (2020) used rich individual-level data covering all workers in Brazil, containing information regarding workers’ employment history, criminal portraits, and welfare registries, which is why the data is a reliable source of information. The author clearly states in the first paragraph of the article that the article explains the mechanisms that make people commit crimes when laid off.

Economic losses and unemployment are complex topics to tackle, especially regarding criminology in Brazil – a country known for its high level of crime. It is widely believed that crime rates soar whenever there is an economic turnaround, and it is only natural to assume that job loss is one factor that fosters crime (Dong et al., 2020). First, individuals may engage in criminal acts solely because they have a lower cost of social indifference once they lose a job. Indeed, it is intuitive to picture that the liquidity constraint when a job is lost can reduce individuals’ willingness to work in the market because the psychological pressure of unemployment leads to extreme stress. In this paper, Britto et al. (2020) were interested in how much job loss contributes to crime and whether unemployment benefits could cancel out the effects of job loss on crime.

One reason such a study might be crucial is that – by providing researchers with a clear and identifiable causal signal – it can be of terrific use to policymakers interested in implementing interventions to reduce crime in Brazil and socioeconomic contexts around the globe. The study explores the complex relationships between job loss among workers in the hostelry sector in Brazil and criminal behavior through a big data approach, using an identification strategy based on detailed registry data. It analyses the criminal behavior of male workers who ceased employment due to mass layoffs; thus, it seeks causality through complementarity, adding the characteristics of their cohabiting sons to investigate how spillovers of job loss occur in the family context.

The quantitative approach employs causal forest algorithms – a form of machine learning or non-linear analytical techniques used to estimate conditional average treatment effects dependent on large and heterogeneous samples of individuals. Using causal forest algorithms is preferable to linear models because it allows for greater depth in estimating treatment effects and accommodates the heterogeneity among the sample of participants (Britto et al., 2020). Regression discontinuity designs are used to measure the impact of unemployment benefits on crime. The duration of unemployment benefits is a frequent subject of debate among policymakers. Social insurance policies are sometimes blamed for distorting individual behaviors by mitigating some of the adverse consequences of job loss. Using administrative datasets from Denmark, Britto et al. (2020) estimate the impact of unemployment benefits on criminal behavior. Specifically, Britto et al. (2020) show that being jobless decreases the probability that an individual commits crimes. Each six-month extension of unemployment benefits decreases legal activities in the following five years by 4.4 percentage points (a 24 percent reduction). The introduction of more generous unemployment benefits results in a 1.6 percent reduction in crimes with individual victims, such as theft or burglary, but no change in crimes against property, such as vandalism or corruption of a minor.

The results show that job loss substantially raises criminal activity among workers who lose their jobs, especially young and low-tenure workers. The results generally indicate that job loss creates liquidity constraints (Britto et al., 2020). Criminal activity rises significantly after job loss and is more significant for younger and low-tenure workers, whose liquidity constraints would be most material (Safat et al., 2021). On the other hand, increased severance pay can help offset the increase in crime when a worker receives the insurance payment. Unemployment benefits help insofar as they mitigate the impact of job loss shocks. The effects are also temporary, as criminal activity returns to baseline after unemployment benefits expire.

Drawing on these findings, Britto et al. (2020) suggest that policymakers should use passive and active policies to address how job loss contributes to crime. Unemployment benefits provide short-term relief, but it is also necessary to design policies that reinforce the pathways displaced workers might take to find new jobs and other forms of employment. Various forms of targeted assistance could be established to provide aid to the most vulnerable groups who are the most likely to experience poverty and other forms of criminal behavior after being laid off. The research stands as a reminder of the need to mitigate the economic insecurity plaguing too many ex-workers and enhance the social support provided to them when they lose their jobs.

References

Anser, M. K., Yousaf, Z., Nassani, A. A., Alotaibi, S. M., Kabbani, A., & Zaman, K. (2020). Dynamic linkages between poverty, inequality, crime, and social expenditures in a panel of 16 countries: two-step GMM estimates. Journal of Economic Structures9, 1-25.

Britto, D. G., Pinotti, P., & Sampaio, B. (2022). The effect of job loss and unemployment insurance on crime in Brazil. Econometrica90(4), 1393-1423.https://doi.org/10.3982/ECTA18984

Dong, B., Egger, P. H., & Guo, Y. (2020). Is poverty the mother of crime? Evidence from homicide rates in China. PloS one15(5), e0233034.https://doi.org/10.1371/journal.pone.0233034

Safat, W., Asghar, S., & Gillani, S. A. (2021). Using machine learning and deep learning techniques, empirical analysis for crime prediction and forecasting. IEEE Access9, 70080-70094.https://ieeexplore.ieee.org/abstract/document/9424589

 

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