- how many jobs have gone offshore
- what kinds of jobs are created by public investments
- the actual number of foreclosures and evictions
- which early-education programs work
- who is funding outside spending on political campaigns
- how much students actually learn in college.
(In this case, I’d argue that it’s more important WHAT students learn than “how much” they learn).
Anyway, as the article argues,
Information is the life-blood of public policy. Identifying a problem is the first step to solving it, and once a solution is in place, we need metrics to understand if the policy is working and how to turn its weaknesses into strengths. Though the data we have today are, unsurprisingly, better than ever, there are still too many dark spots and missing data on a host of important issues. Take, for example, the financial sector. Bank regulators, lacking a clear picture of how predatory mortgage loans are connected to global capital flows, allowed an exuberant bubble to spin into a crash in 2008. It’s a reminder that if we can identify gaps in our knowledge, we would be remiss not to close them.
This debate isn’t just about how to collect new data but also how to interpret the data we already have. A perennial question is how to define poverty; the current measure is pegged to the economy of the 1960s and doesn’t reflect today’s family finances. A new measure developed by the Commerce Department will paint a more accurate picture of poverty, but it is opposed by conservatives who fear that the results will portray more penury in the United States — “propaganda,” scoffs the Heritage Foundation. Sometimes, people just don’t want to know.
Ignorance, however, is only bliss if you think the government shouldn’t play an active role in society; bureaucratic inertia favors conservatives. Though new technology has made sharing and manipulating data easier, the government is playing catch-up in putting information online so the public and academics can put it to good use. In many policy areas, the government isn’t just failing at transparency but also usability — data sets don’t always lend themselves to apples-to-apples comparisons, making much-needed analysis more difficult to obtain.
Can you name any other instances of what Donald Rumsfeld famously called “known unknowns’?