By Josh Eidelson –

In the U.S. and the rest of the world, politicians looking to trim government spending have typically taken a simple approach to reforming social services: Cut them. In New Zealand, the ruling National Party pioneered a novel approach: Use data analytics to determine where spending can be most effective. In 2010, when Minister of Finance Bill English first convened a policy group to review welfare spending, government statistics showed half the 4,300 teenage single mothers receiving benefits in that country were likely to remain in the welfare system for 20 years, at a total cost of about $264,000 each. The government responded with $23 million to assign individual case workers to help teenage mothers finish school and find work. Now, after four years, the number of teenage single parents on benefits has dropped to 2,600.

Using data from welfare, education, employment, and housing agencies and the courts, the government identified the most expensive welfare beneficiaries—kids who have at least one close adult relative who’s previously been reported to child safety authorities, been to prison, and spent substantial time on welfare. “There are million-dollar kids in those families,” English says. “By the time they are 10, their likelihood of incarceration is 70 percent. You’ve got to do something about that.”

In 2012 economists at the University of Auckland published research establishing clear correlations between family circumstances and incidents of child abuse or neglect. “No one realized we were sitting on such rich data in terms of its predictive power,” says Rhema Vaithianathan, who led the research. “We can find children who are at considerably elevated risk, and we can find them at birth.”

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