Our Work

Behavioural Insights and Crime: Part I

Violence against Women (VAW)

When you think of BI and crime, shows like Criminal Minds and Mindhunter (both of which the author of this blog post highly recommends) come to mind. However, tackling VAW requires more than psycho-analysing perpetrators. This is because there are a multitude of other factors leading to the creation of a climate of acceptability toward VAW. Here are some examples, drawing largely on a report by the European Commission:

  • Victims of violence may not report the crime: They may be afraid of the perpetrator, believe they are unlikely to receive help or simply because they are not aware of the support services available. It is also possible that a status quo bias is at play here; victims may be inclined to accept the situation and be unwilling to change it. Social norms could also prohibit them for speaking up.
  • Bystanders may not intervene: Either because they believe it is not their responsibility or that violence within couples is a private matter. They may also lack self-efficacy, i.e. confidence in their ability to make a difference.
  • Perpetrators may not believe they committed a crime, or think they can get away with it. They may also be present biased about the effects on the victim as well as implications for their own lives (punishment/ imprisonment)
  • Disagreement on definitions: For example, while most people agree that rape is wrong, there may not be a consensus on what exactly constitutes rape.
  • Professionals within the legal system may have prejudices too, creating an environment that is not supportive for victims to come forward.
  • Stereotyped media portrayal or inappropriate reporting by journalists of news regarding VAW.

rape

Source: emaze.com

The list is endless and usually a combination of factors operates to encourage victim blaming attitudes. Which is why in the mid-1990s, the causes of VAW were recognised to be probabilistic rather than deterministic. That is to say there is no single cause; the same outcome (VAW) can be caused by a different amalgamation of factors in different social contexts.

One of the simplest models for analysing behavioural causes for a certain action is the Theory of Planned Behaviour. It states that for someone to perform a given behaviour, the following 3 conditions have to hold (assuming they have the intention to carry it out):

  1. Holding a positive attitude toward the behaviour
  2. Considering the behaviour to be in line with social norms
  3. Having self-efficacy i.e. believing that they are able to perform the behaviour

How does BI fit in?

The aforementioned examples can be viewed in light of the above model to design programmes for changing attitudes and behaviours. Some behavioural levers that can be used for this purpose are:

  • Using social norms:

Example: In 1999-2000, James Madison University (JMU, United States of America) ran a campaign aimed at changing misconceptions among male college student about their peers’ sexist beliefs. The campaign used a series of posters and flyers containing contextualised normative messages like ‘A man always prevents manipulation: three out of four JMU men think it is NOT okay to pressure their date to drink alcohol in order to increase the chances of getting their date to have sex’ or ‘A man respects a woman: nine out of ten JMU men stop the first time their date says “no” to sexual activity’. Results showed that there was a significant increase in the percentage of males claiming that they ‘stop their sexual activity as soon as their date says no’, and who endorsed the statement ‘when I want to touch someone sexually, I try and see how they react’.

  • Scarcity:

Victims of violence are in a situation of scarcity of mental resources: they can be under severe time, emotional and/or financial constraints, impairing their decision-making abilities. Therefore making a specific plan with concrete steps to be carried out and creating awareness about it can enhance their self-efficacy.

  • Framing:

The narrative around VAW is slowly changing, with several countries changing the legal definition of rape from “crime against morality” to “crime against the individual”. Similarly, interventions can be designed to convey that VAW is not a private matter and is a serious crime.

Example: A 2002 advertising campaign by New York City focused on increasing reporting by women experiencing domestic violence via a 24-hour telephone hotline. Behavioural levers included framing messages to highlight that violence is a crime for which there is no excuse, and that abusers are diverse and include men with a positive image in society. Posters showed pictures of men — typically a college athlete or professional businessman — behind prison bars, with headings such as ‘Employee of the month. Soccer coach. Wife beater’ or ‘Big man on campus. Star athlete. Abusive boyfriend’, along with the subtext ‘There’s no excuse for violence against women. Men who hit or abuse their partner belong in jail. Report domestic violence and get the help you need’.  Results showed that calls to the hotline increased by 36% in the second week of the campaign.

  • Loss aversion:

Perhaps policemen and judges could be educated on the cost of VAW, rather than focusing solely on reducing prejudice and emphasising the importance of women’s human rights. Also, potential assailants could be discouraged by highlighting everything they stand to lose- their freedom (if imprisoned), their reputation (although this would mean shifting social norms to shaming the perpetrator rather than the victim) and so on. Victims could also be encouraged to report the crimes against them by showing them how they would be losing out the chance to live an abuse-free life.

There are several other behavioural levers that can be harnessed, like choosing the source of the message wisely, addressing the status quo bias, etc. Another idea for changing attitudes, inspired by Dan Ariely’s Ask Ariely column could be presenting people who hold misogynistic views with even more extreme arguments supporting their belief. The absurdity of the ideas may force them to re-evaluate their own attitudes.

Conclusion:

We are finally realising the importance of behavioural science in designing interventions for reducing crime, including VAW.  It is important to pretest initiatives before implementing them and also conduct an assessment of impact once it is in place. VAW is a complex phenomenon, but if we break it down and target specific behaviours, perhaps attaining the goals we want to is not as unattainable as it may seem!

 

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Article Review

Enhancing the Efficacy of Teacher Incentives through Loss Aversion: A Field Experiment

Review of a paper by Fryer, Levitt, List and Sadoff (2012)

Improving the productivity of teachers has long been a priority in Public Policy. In recent years, there has been growing enthusiasm among policy makers for initiatives that tie teacher incentives to the achievement of their students. However, evidence as to the efficacy of such policies is mixed. Studies in developing countries have shown that linking pay to teacher performance helps reduce teacher absenteeism and dramatically improves students’ test scores. In contrast, the only two field experiments conducted in the US have shown small, if not negative, treatment effects.

The authors of this paper conducted a field experiment to test whether the psychological bias we call “loss aversion” can be exploited to design better incentives.

To be sure we’re all on the same page, let’s take a minute to define what loss aversion means. A perfectly rational person should feel the same amount of pain on losing say $5 as the joy felt on gaining $5. However, for a loss averse individual, the pain of losing is greater than the pleasure of gaining, even for the exact same amount. Studies have shown that losses may be more than twice as powerful as gains!

There is overwhelming laboratory evidence for loss aversion, but the paper in question is one of the first to demonstrate it in a field experiment. The study was conducted in Chicago Heights, Illinois during the school year 2010-2011. Chicago Heights is made up of primarily low-income minority students who struggle with low achievement rates.

Two methods of framing teacher incentives are compared:

  1. The teacher gets a sum of money at the end of the academic year if his/her students perform well (gain frame; the one we’re most familiar with in this context)
  2. The teacher is given the sum of money at the beginning of the year and it is withdrawn if the students do badly (loss frame)

At the beginning of the school year teachers were randomised into Treatment and Control groups. Within the treatment group, some were assigned the gain frame and others the loss frame. Further, within the “Loss” and “Gain” groups, the authors tested for heterogeneous effects for individual rewards compared to team rewards. Thus, participating teachers were randomly assigned to one of four treatment groups or a control group. Rewards were based on students’ end of the year performance on the ThinkLink Predictive Assessment, an otherwise low stakes standardised diagnostic assessment that is designed to be aligned with the high-stakes Illinois Standards Achievement Test (ISAT) taken by 3rd-8th graders in March and the Iowa Test of Basic Skills (ITBS).

Image result for children taking tests

They estimate intent-to-treat (ITT) effect by taking the difference between treatment and control group means. They find large impacts on both the ThinkLink as well as ISAT and ITBS scores, but only in the LOSS frame. There is no significant difference for student scores of teachers in the “Gain” treatment, in line with findings from previous studies done in the US. Also, no significant difference was found between individual and team incentives under either the Gain or the Loss treatment.

But can we really be sure that it is loss aversion that is driving these results? There could be 3 other possible explanations. These are listed below, along with the reasons why they are implausible in this case:

  1. Attrition- Perhaps teachers found a way to discourage weaker students from taking the exams. The authors run a probit regression on all of their covariates, where the dependent variable is an indicator for missing any of the exams considered. They find no significant difference between the gain and loss groups. Furthermore, the ISAT scores were not directly incentivised and they found similar rates of attrition even there. So attrition is unlikely to explain the results.
  2. Liquidity constraints- Teachers under the loss treatment have more money at the beginning of the year, which may enable them to spend more on resources for the classroom. However, surveys on the amount of spending reveal that this was not the case.
  3. Cheating- Teachers may cheat and give their students high scores. However, they cannot manipulate the ISAT and ITBS scores, which mirror the results found on the ThinkLink tests.

Thus, by spending the same amount of money, but just changing the way the incentive was framed, they obtained dramatically better results. This suggests that there may be significant potential for exploiting loss aversion for designing Public Policy (as well as for maximising profits, of course)!