[This was originally posted on the web on June 15, 2009 for elected executives of governments.]
Every day, the employees of your government follow the same routine.
They have a stack of problems, applications, forms and the like in their inbox. It may be a real, old-fashioned inbox with lots of paper or the computer-based equivalent. Doing the best they can, they then work through the pile and, we hope, with wisdom and efficiency, they process the incoming tasks and then move them to the outbox. As far as many employees are concerned, their work is done when the thing is put in the outbox.
However, for the people who run the government, this represents more than a ledger of what came in and what went out. It is a gold mine of information. Especially because of all the automation that has been put in place in government agencies, it is also an easily accessible gold mine.
Unfortunately, this gold mine is often ignored. But if that data is analyzed, you will discover the patterns that can help you improve government programs and policies. Consider two examples, from very different areas, of what statistical analysis of that data can tell you:
What kinds of programs have worked best for which kinds of prisoners? (This knowledge can be used to come up with better treatment and assignment of prisoners at intake.)
Who has used the public golf courses at what times of the week and day? (This can identify where you might want to offer new programs targeted at particular groups of residents to even out usage during the day and get more golf fees.)
In 2007, Professor Ian Ayres wrote a book, “SuperCrunchers: Why Thinking-By-Numbers Is The New Way To Be Smart”, in which he described how various organizations are using statistical analysis to dramatically improve their performance.
One of its chapters, “Government By Chance”, provides public sector examples and offers an interesting idea.
Imagine a world where people looked to the IRS as a source for useful information. The IRS could tell a small business that it might be spending too much on advertising or tell an individual that the aver age taxpayer in her income bracket gave mote to charity or made a larger IRA contribution. Heck, the IRS could probably produce fairly accurate estimates about the probability that small businesses (or even marriages) would fail. In fact, I’m told that Visa already does predict the probability of divorce based on credit card purchases (so that it can make better predictions of default risk). Of course, this is all a bit Orwellian. I might not particularly want to get a note from the IRS saying my marriage is at risk. But I might at least want the option of having the government make predictions about various aspects of my life. Instead of thinking of the IRS as solely a taker, we might also think of it as an information provider. We could even change its name to the “Information & Revenue Service".
This is yet another example, though, of moving the public sector from a transactional view of citizens to something more helpful. While even the author admits the IRS example is a scary, there are other possibilities that are not scary and that your residents would like.
The use of the data the government collects for better policy and better service to citizens is what I call “learning how to drive the government” because it is different from the usual fad and fashion approach to policy.
Too often policy debates are like a driver in a car who cannot see outside the windows. So the driver keeps going until the car hits a wall, at which point the usual reaction is to go in the opposite direction until the same thing happens again. This accounts for the feeling of a pendulum swinging in public policy debates, rather than real learning occurring.
When everyday data is analyzed, it is like being able to look out the windows and figure out what direction to drive.
© 2011 Norman Jacknis