Last night, I ventured out of my normal routine and headed off to a “Meetup” with folks who sought to “create space and opportunities for ongoing collaboration of non-profit data partners and data enthusiasts to explore interesting data sets for the greater good.”
Four Ways to Slice Obama’s 2013 Budget Proposal,
[Note, the full interactive graphic can be found at The New York Times ]
Big data and Business intelligence or applying analytic and visualization tools to explore and understand data have been steadily gaining their share of business headlines in the last few years. If I was more deft, I’d be able to show you a graphic illustration. But that’s the point of why I went to the meetup. To learn where and how to do exactly that!
I met several people, bolder than I, willing to put their ideas out in front of others before they were fully baked. Even better, these individuals were bold enough to push their initiatives in spite of the usual skepticism.
Compelling policy action with data
For example, the overlap of homeless people in neighborhoods with vacant buildings and apartments, sounds like a solution begging for grease to make happen. One attendee, a researcher, wanted to document the problem and then find folks willing to help change the situation. The grease might be converting the vacant properties to low-income housing, or an unemployment PLUS housing voucher system. Better yet, why not offer training in home and building maintenance, a program for home repair that puts the homeless to work to earn their rent? The researcher came looking for help to find the data and see what if anything could be done in Chicago. I happened to have investigated this questions briefly (see my post on the Rosenwald homes) and shared with her that indeed there were lots of organizations and public private partnerships working on this issue. For her, the meetup proved useful and helped her further her interests.
Policy wonk that I am, I recognize wider issues these simple ideas overlook. What I applaud however, is the willingness and gumption of technically skilled, many highly educated PhDs who have voluntarily bound together to tackle the status quo. In spite of my own experiences and deeper understanding of the problem, the Data luck meetup tapped my ever-present optimism and willingness to engage, and I guess that’s what prompted this post. I too, sense a good argument, made with honest data, can and should sway people to correct problems. Don’t you?
It’s certainly hard to roll back a policy after it’s been implemented. When new information or new insights emerge, typically the absurdity of the original solution only manages to compromise best intentions. The results represent the flaws or misconceptions of the original framers of the problem. The revelations of more problems, as in the example public housing created of co-dependence and how it helped sustain poverty for people who grew up in these projects. The emergent data just as easily undermines the willingness of lawmakers to find a better solution. Instead the aggregate data leads them to impose more rules and regulations to prevent cheating which does little to correct the underlying problem.
How is it that we have growing government? Adding rules to correct for the limitations of the original legislation, like any patch, is easier than starting over and addressing the problem from scratch.
Amending vs legislating
The Constitution, of 1789 was an overhaul within 10 years of the final ratification by all 13 colonies of the first constitution, or the Articles of Confederation which had effectively bound the states after the American revolution. Beginning with 10 amendments, in two centuries, Congress has amended the constitution only17 times, while it enacted numerous laws.
The scare of governmental encroachment, or interference in our lives may be the rallying cry of many; additional data however, suggests other factors.
Congress No Action Action Failed Enacted # and %
106th (1999-2000) 7460 922 28 558 (6%)
107th (2001-2002) 7750 841 5 350 (4%)
108th (2003-2004) 7045 932 13 476 (6%)
109th (2005-2006) 9141 930 22 465 (4%)
110th (2007-2008) 9218 1382 39 442 (4%)
111th (2009-2010) 9239 998 26 366 (3%)
112th (as of 8/4/2011) 3956 305 7 20 (0.5%)
Drafting a bill, as the blog points out certainly requires some thought, and the leadership in each party tends to only let those bills with a chance at passage actually reach the Congressional floor for votes. This explains the relative low # of fails, in spite of the discrepancy between the number enacted and those receiving some action.
Not clear how to interpret the relatively low proportion of enacted legislation. Is it a sign of efficiency, or the complete log jam that makes progress impossible? Anyone know whether repealing a piece of legislation , such as the repeal of Glass-Steagall would be tallied as enacted?
One thing is clear, having more volunteers who are not part of a political action committee, or beholden to a particular ideology other than honest analysis, can only help. The value of new tools and data visualization certainly helps, but as the presenters at last night’s Data luck meetup reminded, you still have to clean the data before beginning the analysis. I’m eager to learn how to use some of these tools, and access more data to help tell a different story.
I’ve got no problem using shame to illustrate the inadequacy of a policy, or the stranglehold of private interests that stand in the way of progress. What do you think? Any and all suggestions are welcome.