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Using evidence to drive the policy process – lessons from Baltimore

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Last week Governor Martin O’Malley of Maryland was visiting London, talking about the successes they have had in the City of Baltimore (when he was Mayor there) and now in his state by using evidence to drive the policy process.

When O’Malley took office as Mayor of Baltimore in 1999 the city had one of the highest crime rates in the US, which was just one factor in a long downward trend in the City’s fortunes. O’Malley used this crisis as a galvanising principle to reform city services. He built upon the work pioneered by the NYPD, where the use of real-time data to understand the impact of crime on the ground revolutionised the effectiveness of policing.

O’Malley applied the principles of mapping real-time data across a number of government departments. Instead of taking service providers away from delivery and spending vast sums of money encouraging them to be ‘joined up’ across their silos, he presented his departments with CitiStat – a map-based data system allowing every department to see what was happening at any given location in the city. Fly-tipping, potholes, ambulance call-outs, and crime hot-spots are now easily cross-referenced and underlying causes can be addressed at their source in a coordinated fashion.

Putting each department’s own data in context gave them a greater sense of ownership of their own services as well as the goals of the government as a whole. And the ability to call service leaders to task for failings increased their focus on the front line.

Data can be very compelling as a policy tool, as the CitiStat experience shows. Many policy areas are not so amenable to direct measurement, however. Local economic growth programmes – such as employment training or business advice – are attempting to improve outcomes (jobs, growth). Outputs (training, advice) are only a stepping stone, with the real benefits coming further down the ‘loop’.

In our evidence reviews we are learning a lot about how NOT to evaluate the impact of such policy areas. Our challenge over the next year is to provide answers about what methods will produce the clearest, most rigorous data by which to assess the effectiveness of their work and to allow the kind of feedback loops seen in Baltimore. We know how to do this in principle (experimentation plays a big role) – the issue is getting experimentation and evaluation embedded in to the policy-making process. While some might contest the extent of the changes in Baltimore, it certainly provides an inspiring example of how we can make better use of data to improve policy effectiveness.