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Social Investment Down the Digital Path of Least Resistance

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In what could be a watershed moment in social policy in Australia, the Minister for Social Services, Christian Porter announced on 20 September, a commitment to a social investment approach to address welfare dependence.

And the catalyst that led to this significant announcement…data. I refer to some of the Minister’s statements in his speech to the National Press Club:

  • “…I do earnestly believe we have real future insight here as to how data can drive policy development”
  • “It allows us to then apply the types of algorithmic and actuarial analysis used in insurance industries.”
  • “Through this modelling we can predict the likely movements for target groups on, off and in between welfare payments, and calculate welfare expenditure for groups over the lifetime of the system.”

Wow, is this a data scientist impersonating a minister of state, or has the Minister truly seen the value of data and predictive analytics in driving social policy towards achieving outcomes, ahead of traditional rhetoric based on opinions, stereotypes and ideology?  Notwithstanding the “devil is in the detail,” the Australian Government is taking a leaf out of the book from their neighbours in New Zealand where the social investment model has been in vogue for several years.  So as not to be accused of overstating the antipodean claim to the social investment approach, the European Commission has also been advocating this proactive approach to welfare reform. The European Commission defines social investment as “

policies designed to strengthen people’s skills and capacities, supporting them to participate fully in employment and social life.”

This emerging trend towards social investment has come about as policy makers take advantage of the ever growing mass of digital data which is delivering deep insight into what works and what doesn’t. There has been widespread and valid questioning of the government’s policy intent which is reflective of the widening trust gap for people when it comes to supporting a reform agenda.   Some critics argue for a status quo policy approach with more money put into benefits. They seemingly ignore the validity of an evidence-based approach to policy development targeted at individuals to deliver a social outcome that people want. In a similar vein, perhaps we should stop research on individualised medicine and keep everyone on the same set of publicly subsidised drugs and hope they work.

Notwithstanding the questions on policy intent, there can be no dispute that data talks.  We may not however always like what it says, especially when it challenges some of the myths and perceptions about the efficacy of social programs. It comes as no surprise that a one-size-fits-all approach doesn’t work for most things, let alone dealing with complex social problems. Yet since the time of Bismarck*, social security systems have been inherently designed to be reactive with fairness and equity defined in the context of equal treatment for all under the law or policy of the day. The emergence of activation policies and mutual obligation in the 1980s and 1990s in many countries, signalled a trend towards a more proactive approach to social security policy. Critics see these policies as coercive and punitive against socially disadvantaged people while advocates argue incentives and sanctions are necessary to help people address social disadvantage.

The value of data analytics to transform the policy development process is a key driving force behind the research agenda of the SAP Institute for Digital Government (SIDG). At the SIDG, our research covers several areas relevant to this “devil in the detail” question, including the moral and ethical issues of using data and real-time program evaluation. We have co-authored a paper “The Digital Nudge in Social Security Administration” with Professor Shirley Gregor of the College of Business and Economics, Australian National University, which will be published in October 2016 in the International Social Security Review.

The concept of nudge from the field of behavioural economics has sparked a plethora of government initiatives yielding significant public value. A nudge is a method for predictably altering behaviour without restricting consumer choice options or significantly changing incentives. Nudges work by leveraging default human behaviour such as the tendency to take the path of least resistance when exercising choice. Government agencies around the world led by the UK, have run many successful trials with simple textual nudges designed to positively influence behaviours such as tax compliance, voter registration and student attrition.

This paper examines digital nudge in the context of social security administration, which leverages predictive analytics technology within a digital government framework to support a social investment policy approach. Based on a literature review of nudges within a digital government context, the paper identifies examples of innovation within social security administration where nudge is contributing to better social outcomes. At the same time, concerns regarding ethics and privacy are identified as nudge is applied at the individual (the social investment target) rather than the population level. The use of data and personal information to drive the nudge process needs to be managed in such a way that individual rights are protected. This requirement has to be reconciled against the broader interests of society in achieving outcomes that are affordable.

Insight gained from digital data will generate innovation in social program development, implementation and evaluation. Data itself will not remove the need for detailed program design and expertise in rolling out social programs. While most of us know the one-size-fits-all approach doesn’t work, it is been the standard administrative approach as it easier to design, implement and manage. Technology and digital data now gives us the tools to tailor positive interventions to an individual level, such as a digital nudge in real-time. These interventions will be based on the evidence supporting the highest probability of success. Success is defined in terms of a social outcome that benefits the person. The digital nudge is not a compliance tool or simply a way to reduce outlays. Reducing outlays is a function of outcome achievement.

Stay tuned for when our paper, “The digital nudge in social security administration,” is published  – it will make a small but important contribution to addressing the “devil in the detail” that sits beneath the Ministerial statements on a social investment approach to welfare reform.

To find out more about the SAP Institute for Digital Government visit, follow us on Twitter @sapsidg and email us at

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