By Matthew Davies and Simon Ruda
Many successful behavioural policies are noteworthy for the impact they achieve for what can seem quite simple interventions. But as BIT’s manifesto discusses, complex behaviours and policy ecosystems require behavioural science practitioners to adapt their approaches. Here, we outline lessons in applying behavioural insights to crime reduction building on our recently published paper in Behavioural Public Policy.
Why Saving Motorcyclists’ Lives Might Also Save Their Bikes
When West Germany introduced fines for motorcyclists not wearing helmets in 1980, few would have predicted a dramatic drop in motorbike theft. Yet the policy – ostensibly intended to improve road safety – resulted in a sustained 60 per cent decline in theft. Why? Because it meant that, if you wanted to steal a motorcycle, you needed to have a helmet with you in order to get away without detection. Since buying a helmet is relatively straightforward, we can conclude that a large proportion of motorcycle thefts happened because the opportunity simply presented itself (there was minimal displacement to other forms of crime). So this particular behaviour was, to a large extent, driven by contextual factors.
Contextual factors can often play an important role in high volume crimes, like motorcycle theft, presenting an ideal intervention point for behaviourally informed policies. And we have seen several successful applications of BI to crime prevention, but why has behavioural science not been able to claim ‘policy unicorns’ – consistently large and scalable impacts for relatively light touch interventions – in the way that we have seen in other areas of policy, like pensions, tax and energy use?
The Curious Case of Crime
As others have also illustrated, part of the challenge relates to the complexity of behaviours, actors, and disparate set of touchpoints implicated in crime. Each type (from cyber-crime to speeding to child sexual abuse) varies in severity, method, duration, frequency and degree of impulsivity/pre-meditation. Since the range of underlying drivers of these behaviours is equally wide, this results in a complex interplay of individual and environmental risk and protective factors for different perpetrators and victims. Underpinning these challenges has been a limited tradition of empiricism in crime-reduction. Underreporting of crimes makes it difficult to assess baseline rates while measuring other outcomes often requires bespoke data collection with hard-to-reach populations. Law-breaking also evokes a set of emotional responses that make it difficult for evidence-based approaches to cut through. The risks attached to experimentation in this field are different to those in other areas of social policy. These risks can be very real – relating to the physical safety of officers, victims or communities, as well as the high stakes political consequences of applying unfamiliar methods to a policy area that many citizens already feel they know the answers to.
Lastly, the field tends to be fragmented. Police forces are closest to crime (in terms of data and finding a point of intervention) but police forces are regionally demarcated, which makes it much harder to test and adapt interventions at real scale.
So the unicorn challenge may be one of coordination in order to scale the impact of promising interventions seen at local levels. But moving beyond the choice architecture type nudges that have seen easily scalable success in other policy domains, perhaps we need to dig deeper into the behavioural realm to find where BI can have transformative impact on crime.
Cross-Pollination of Ideas Is Fundamental to Innovation in Complex Systems
Inventors working in domains with high uncertainty are most likely to be successful where they have range – that is being able to draw on a breadth of perspectives to unlock new insights. Similarly, we can use insights from different disciplines to identify crime prevention opportunities.
Behavioural science has popularized the concept of choice architecture, revealing how subtle cues in our environment can shape behaviour. But criminologists too have long been preoccupied with the role that our physical environment (i.e. the literal architecture) plays in offending behaviour, such as street lighting.
But another area where BI can really add value is offering a lens to better understand how decisions to commit a crime might be made. For example, behavioural science research suggests that people are typically bad at estimating risk, particularly when an individual’s own behaviour creates a risk to herself (smokers are often guilty of ‘optimism bias’, fully understanding the health risks of smoking yet rationalising that they can smoke while mitigating their own personal risks). This tallies with research on deterrence theory, which suggests that certainty of being caught is the most instrumental to behaviour change relative to the severity of punishment or how quickly justice is administered.
We also know from behavioural science that the way a message is communicated is key to behaviour change. A growing body of work illustrates the way in which social networks can effectively disseminate messages through the power of messenger effects. For example, when police delivered a deterrence message to prolific offenders in the UK, subsequent offending significantly decreased among co-offenders who had not received the message (11% reduction compared to control). This illustrates the way in which information can cascade down through social networks to create wider change beyond the initial target group.
One-off interventions of this nature, whilst valuable, are likely to fade over time, particularly for complex behaviour like crime. Behavioural scientists working on crime reduction must therefore look beyond the confines of classic ‘nudges’ to better understand how to create long-term behaviour change. Such interventions must target well-evidenced risk factors, such as impulsivity, to affect longer-term patterns of behaviour. This approach has already shown promise in the shape of cognitive behavioural therapy (CBT) to slow down automatic System 1 responses in the case of youth offending.
These programmes can be bolstered to ensure that they create the wider conditions required for longer-term change. For example, an 8-week CBT programme in Liberia targeted at men working in low-skilled or illicit jobs tested the impact of CBT alone vs receiving a US$200 unconditional cash transfer alone. Those who received therapy demonstrated greater patience and forward-looking behaviour, with larger, more persistent effects among men who received both therapy and cash transfer. In the weeks following the end of treatment, crime rates among participants who had received therapy fell by up to nearly half relative to the comparison group. After 1 year, these effects persisted only among those who had also received the cash transfer. The authors hypothesise that cash reinforced therapy’s impacts by prolonging learning by doing, lifestyle changes, and self-investment.
Such studies highlight the potential to heed recent calls to change in the narrative about what the field of behavioural science does and could do.
Where to Next?
In the paper, we set out some areas ripe for further exploration:
1. Improving the Criminal Justice System
Much of the work to date has been focused on police, with comparatively little in the courts. There is considerable room to explore the role of behavioural biases – and potential solutions – in the context of, e.g., juror decision-making, sentencing and simplifying processes for end-users. The effectiveness of our institutions at managing and preventing crime will only ever be as good as the people who work in them. Behavioural science can better inform organisational design to motivate staff, improve governance and executive decision making, reduce resource wastage and improve critical processes (like recruitment and retention to fill staff shortages and improve representation). The recent Baroness Casey Review in the UK highlights the urgency of such work.
2. Improving Rehabilitation and Reducing Recidivism
Criminology has developed a comprehensive body of work on desistance, and yet there is much more that can be done by infusing a behavioural understanding. For example, experimental research could explore the impact of experiential peer support on desistance, or simply adherence to rehabilitative programmes. But BI could also be deployed towards more strategic questions at the policy level, such as how to reduce the prison population (through, e.g., reducing the likelihood of administrative breaches), to how to best manage an aging prison population.
3. Supporting Victims
Another area where BI could lend more weight is supporting victims, from encouraging help-seeking behaviour, to empowering by-standers, to reducing susceptibility to victimisation. For example, a BIT trial that involved a mock phishing attack on 17,000 Metropolitan Police Service officers found that three forms of email-based interventions significantly reduced the number of officers who clicked on the link and the number of officers who submitted their login credentials. Internet-mediated preventative measures like this, whose effectiveness to a large extent will depend on the presentation of information therein, seem particularly ripe for behavioural thinking.
All of this is not to say that behavioural science is the missing ‘silver bullet’ to many of these pervasive challenges faced by criminal justice systems. Challenges of deviancy, recidivism, and rehabilitation, for example, are perennial issues faced by all societies throughout history. There is enough evidence from other policy areas and some initial successes in application to the criminal justice system to suggest that the behavioural lens is worth testing further in the context of crime.
This article was edited by Lindsey Horne
