Paula Bennett, Minister for Social Development, reveals the critical role data analytics is playing in reducing the number of people on welfare, and deciding which programs to target vulnerable citizens, especially children.
Analytics has helped in investing and using this data for positive action and make a massive influence in the lives of New Zealanders.
“Without using data analytics, we will be throwing a lot of money,” she says in her keynote the SAS User Conference in Wellington. “We will not be getting into the heart of the problem, we will not be putting resources and investment where these are needed,” she says.
“Since 2011, analytics has been at the heart of our welfare reforms,” says Bennett. While cognisant of the criticisms against her when the welfare reforms were announced, she lists the compelling figures on how the government applied analytics to determine funding for programs and beneficiaries.
A key feature used by the government is analytical technical segmentation, she says. This looked at the welfare system at macro level, an overall evaluation, and how much it is costing over a lifetime of the current population.
We spend an average of $22 million dollars a day on welfare, $8 billion a year, she says. The projected lifetime cost for people on benefit was $78 billion.
This baseline valuation of the welfare system is at a level that has not been attempted anywhere in the world, she says. “It is a pretty compelling story on why we want to look at this population to see what we can do,” she says.
She says in December 2010, there were 352,000 people on benefit, with 13 per cent of the working age population on some form of welfare. There were also 139,000 people who have been on benefit for more than a decade since 1993.
The welfare system was not providing them with the support they needed to build a future, she says.
The help they got was “passive” rather than active, and “not work focused as it could have been and should have been”.
She says the government, for instance, has developed a model which estimates the risk of welfare dependency amongst school leavers, based on information from agencies like the Ministry of Education.
By understanding the patterns of the school leavers in detail, we were able to develop a system of support directly targeted at catching young people before they even turn to benefit, she says.
Using a risk predictive model, the government invested more in programs for higher risk kids.
Analytics means being able to invest more on where we will make the biggest difference, she says.
But the difference is staff has the information that is literally changing lives, and at the extreme end, literally saving lives, she says.
Early intervention, knowing who they are targeting, are basis of effective investment and action, she says.
These families weren’t broken overnight, they have been broken over generations – we know a lot a lot about them, she says. “How do we take all that information we know and get to these children before they are abused and neglected?”
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