Digital welfare (III): transformation through technology

18 November 2016

Governments around the world increasingly use behavioural science to achieve policy goals such as increased pension savings, timely tax payments, and healthier lifestyles. Behavioural science is also increasingly applied to improve the welfare system and the experience of those who come into contact with it.

At its best, a behavioural insights approach helps us to better understand the factors that sometimes stop people from fully engaging with government services. Drawing on evidence from different fields, the approach can shine light on new, innovative, and sometimes small changes to policy and the delivery of services that can significantly improve outcomes. It also provides a rigorous methodology for measuring the human and financial impact of any design or policy change.

As a part of its mission to improve policy making and to find out ‘what works’, the Behavioural Insights Team (BIT) has run a series of trials in Jobcentres, testing ways to improve people’s life chances by supporting them back to work more effectively. We have shown that encouraging people to make specific plans improves employment outcomes, and that personalising messages increases attendance rates. These relatively simple tools have a lot of potential if applied consistently across the welfare system.

More recently, our focus has been on understanding the complex situations faced by those with health conditions and disabilities who are out of work. We are interested in finding simple ways to make people’s lives easier – whether by increasing the timely access to mental health services through new routes such as eCBT or by reducing the cognitive load taken up by complex benefit processes (Universal Credit is taking us in the right direction, but more can be done). Many people with disabilities and health conditions want to work, and we want to test ways to make this possible for more of them.

Going forward we are excited by the possibilities offered by technological advances in government, in particular big data and predictive analytics. Linking large datasets across government and building models that predict long-term unemployment will help us design more targeted interventions and time them well. This approach can help produce savings and ensure people get the best support.

We are also excited by the opportunities to robustly trial interventions within Universal Credit. We should make the most of this opportunity to produce new evidence on how to increase take up of voluntary services, encourage people to self-refer to the most useful support, and to widen their horizons in terms of the kinds of work and training that they look for. We are also keen to do more work on understanding how work coaches set requirements and tailor support under Universal Credit. I look forward to discussing how the government can achieve these aims.

Dr Tiina Likki, Senior Advisor, The Behavioural Insights Team

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