Helen Aki and Olivia Wills, Behavioural Science Aotearoa, Ministry of Justice New Zealand
Behavioral science research and experience shows us that people do not always respond rationally to our incentives, information and legislation. People are often influenced by seemingly unimportant factors or biases, and can behave in ways that are against their best interests and even against their own intentions. One of the challenging behaviors for the Ministry of Justice has been people failing to pay their fines. The Ministry of Justice collects court-imposed fines, reparations to victims and infringements on behalf of local authorities and the police. Over half a million New Zealanders owe fines and total collections debt is around US$395 million, or US$78 for every person in New Zealand. Not paying also impacts the people who owe fines, as they can be hit with enforcement actions or may even be summoned to court. We have used evidence from behavioral science to test new approaches to improve fines payment behavior. By combining empirical evidence from social sciences with human-centered design approaches, we made a range of small changes to existing processes to encourage an increase in fines repayment. Randomized controlled trials (RCTs) and other evaluation methods were used to evaluate changes to find out if they work, for whom they work and which versions work best. This presentation will cover some behavioral insights theory, and give an overview of the trials we have implemented, including design and evaluation approaches used. It will also summarize the trial results.
Watch Using Behavioral Insights to Increase Fine Payments by the author on the SAS Users YouTube channel.
The use of fines is an important sanction in the New Zealand justice process as an alternative to a prison sentence or community service.
Over half a million New Zealanders owe fines, and total collections debt is around US$395m, or US$78 for every person in New Zealand. The Ministry of Justice uses a range of interventions to support collection of court-imposed fines, reparations to victims, and infringement fees imposed by Local Authorities and Police. These interventions include letters, emails, texts, outbound phone calls, and sanctions such as attachment orders to take money from wages and seizure of property.
In 2018/19, US$125 million of this debt was collected and 77 percent of fines were paid or had an arrangement set up within four months. This is slightly short of the target of 84 percent of fines. We explored applying behavioral insights to help get closer to this target.
Behavioral insights introduce more realistic models of human behavior to policy making and interventions. Almost everything the Justice Sector does involves and influences behavior in some way . Whether it is asking people to pay their fines, requiring lawyers to submit documents for court, supporting people to follow their bail or parole conditions, or trying to deter people from offending in the first place. However, we do not always build our policies and interventions on evidence about how those policies are likely to impact human behavior. Instead, most of our initiatives expect that people will respond “rationally” to our incentives, information and legislation (making a decision based on perfectly calculated costs and benefits.) Yet, behavioral insights research and professional experience show us that is not always the case. People are often influenced by seemingly unimportant factors or biases and can behave in ways that are against their best interests and even against their own intentions.
To address this, behavioral insights introduce more realistic models of human behavior to policy making and interventions. These models combine empirical evidence from social sciences such as social psychology, behavioral economics and anthropology, with human centered design approaches. The basic premise is that if you want to encourage a particular behavior you need to make it easy, attractive, social and timely (summarized in the EAST framework, developed by the UK Behavioural Insights Team, 2014). In practice, behavioral insights often involve making small changes to existing processes, something also called “nudging”.
Behavioral insights also aim to robustly evaluate any changes or new designs to find out if they work, for whom they work and which versions work best. Because human beings are complex and behavior is context specific, it is important to evaluate whether something works in a particular situation. For this reason, behavioral insights interventions are ideally evaluated using randomized controlled trials (RCTs) or other robust evaluation methods where possible. RCTs randomly allocate people into two or more groups - one group receives business as usual (the Control group) and the other group(s) receive the new intervention (the Treatment group). Afterwards, researchers can compare the outcome of interest between the groups. Using this evaluation technique means that we can be confident that any difference in outcomes is due to the intervention and not due to external factors or inherent differences between participants.
The use of behavioral insights interventions in debt collection, including overdue taxes and fines, has been growing. Behaviorally informed trials have been conducted in several countries including the UK, US, Guatemala, Poland, Australia and Bangladesh.
However, not all behavioral insights interventions are equally successful and are dependent on the context and exact wording. Even the best evidence concepts do not always replicate, and sometimes even backfire. This highlights the importance of robust evaluation, especially in a context like New Zealand where behavioral insights are still a relatively new approach.
When a fine is imposed, the Ministry of Justice automatically sends a notice of fine to the debtor. If people fail to pay, the Ministry attempts further contact by way of an outbound phone call, an additional letter, or via a range of enforcement activities.
One method is to send an additional reminder letter to people who have failed to pay their fines for an extended period of time. These letters are manually printed and sent, so this provided the ideal opportunity to test whether they could be improved using a behavioral insights approach.
An evidence review informed the design of the behavioral letters for this trial. In total, we trialed three variations to the original letter: a simplified version, a social norm letter, and a fresh start letter.
We used the business-as-usual letter, already used in the pursuit of overdue fines, for the control group (Figure 1).
The letter was already of good quality. It was relatively short and passed the “flip test” – the reader was able to understand its purpose within a 2-second glance. The headline “outstanding fine” made it clear what the intention is without having to read further and it was free of legal jargon and complex language.
Figure 1. Control Letter
We included a simplified letter (Figure 2) to be able to detangle the difference between general improvements to the letter and specific behavioral insights used in treatment letters 2 and 3. The simplified letter differed from the control letter in the following ways:
Figure 2. Simplified Letter
This letter used the simplified design and added a social norm. We chose the social norm “The vast majority of people pay their fines. You are in the small minority that still has to pay.”(Figure 3).
Experiments conducted by the UK Behavioural Insights Team found that adding one sentence to tax letters emphasizing a social norm - that most people are compliant (“Nine out of ten people in the UK pay their tax on time”) - can lead to a big increase in tax payments. The message “The great majority of people in your local area pay their tax on time. Most people with a debt like yours have paid it by now” had the biggest impact on payment rates (Hallsworth et al, 2014).
This finding has been replicated in several trials and has been shown to improve compliance with tax and fines and influence a wide range of other behavior including health and environmental messages (e.g. energy use and recycling). The more specific the social norm, and the smaller the relevant reference group, the bigger the impact tends to be. We did not use a specific social norm in this letter as we were worried the norm (78%) might not be high enough – norms only work if they are higher than what the person expected.
Figure 3: Social Norm message
This letter also used the simplified design but added a message building on ‘the fresh start effect’: “So far we have treated this as a simple mistake, but if you fail to pay now we will treat it as an active choice.” (Figure 4).
People are more likely to start behaving virtuously (e.g. going to the gym regularly) at the beginning of time-based landmarks such as the new year, a birthday, or moving to a new house. Emphasizing something as a new opportunity can nudge people to change their existing behavior, especially when their existing behavior was not in line with their beliefs.
This message was based on the best performing message in a tax collection trial in Guatemala, where the message “Previously we have considered your failure to declare an oversight. However, if you don’t declare now we will consider it an active choice” brought in an additional US$15 per letter when compared to a control letter and an additional US$6 when compared to a social norm letter.
Figure 4. Fresh Start Message
We evaluated the letters using an RCT design with randomization at the individual level. Everyone who was due to receive a fine was randomized into one of four groups and received one of the above letters.
In total, we sent letters to over 29,000 people with an overdue fine and a valid address. All participants had an overdue fine or a breached arrangement for more than one month and had no previous enforcement action taken against them.
In most cases, people had several fines assigned to them. All unpaid fines are combined into one debt amount that was included in the letter. The average amount owed was US$491.
We looked at three outcome measures:
Figure 5 shows the results. The social norm letter significantly increased the proportion of people who paid any amount (paid in full, partial payment or set up an arrangement) by 3.1 percentage points from 43.8 to 46.9 percent – a relative increase of 7.2 percent (p<0.001). The social norm letter also had a significant impact on the proportion who paid in full and the proportion who set up a payment arrangement. The simplified letter did not have a significant impact on the proportion of people who paid any amount, suggesting that including a social norm had a positive impact on top of the other small improvements, such as the call to action and payment box.
Figure 5. The proportion of people who paid any amount
Applying behavioral insights can have significant effects on behaviour. The social norm reminder letter led to a significant 7.2 percent increase in the proportion who made any payments. This result is in line with international evidence showing the effectiveness of social norms impact on paying behavior. Simply adding a sentence to a reminder letter not only increases fines payments, but is likely to also reduce additional enforcement costs for the Ministry of Justice and prevent the person being summoned to court.
These are significant effects from a relatively simple and minimal cost trial. However not all trials are successful in increasing payments, highlighting the importance of robust evaluation, especially in a context like New Zealand where behavioral insights is still a relatively new approach.
The Behavioural Insights Team. 2014. “EAST: Four simple ways to apply behavioural insights.” Available at http://www.behaviouralinsights.co.uk/publications/east-four-simple-ways-to-apply-behavioural-insight...
Behavioural Insights Unit DPC NSW. “Increasing on-payments of fines.” Available at https://www.dpc.nsw.gov.au/programs-and-services/behavioural-insights/projects/increasing-on-time-pa...
Hallsworth, M., List, J., Metcalfe, R., & Vlaev, I. 2014. “The behavioralist as tax collector: Using natural field experiments to enhance tax compliance.” NBER Working Paper 20007.
The Behavioural Insights Team. 2015. Guatemala tax collection trial referenced in “The Behavioural Insights Team Update 2013-2015.” Available at http://www.behaviouralinsights.co.uk/publications/the-behavioural-insights-team-update-report-2013-2...
We would like to acknowledge Vee Snijders who led the work on this trial, and the Collections business group at the Ministry of Justice for being open to try a different approach.
Your comments and questions are valued and encouraged. Contact the authors at:
Helen Aki
Behavioural Science Aotearoa, Ministry of Justice, NZ
Olivia Wills
Behavioural Science Aotearoa, Ministry of Justice, NZ
https://www.justice.govt.nz/justice-sector-policy/key-initiatives/behavioural-science-aotearoa/
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